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Posted on • Originally published at apidog.com

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

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.

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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
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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)
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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)}`);
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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
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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
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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
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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>
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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
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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."
      }
    ]
  }'
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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:

  1. Export your top workloads by monthly token volume.
  2. Recalculate costs with:
    • $1.00/M input
    • $3.00/M output
    • $0.20/M cached input
  3. Select 100 representative production prompts.
  4. Run MiMo V2.5 Pro and your current model side by side.
  5. Validate:
    • Output quality
    • Tool-call JSON shape
    • Streaming behavior
    • Latency
    • Cache-hit rate
  6. Move only the traffic classes where quality and latency are acceptable.
  7. 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.

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