Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.5: Which Model Wins?

Three flagship models, three different bets: Claude Opus 4.8 for agentic coding and long-horizon autonomy, GPT-5.5 as the broad generalist, and Gemini 3.5 as the fast, low-cost multimodal workhorse. The practical question is not “which model is best?” It is “which model should I route this workload to?”

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Use this comparison as an implementation guide. Most headline benchmarks are vendor-reported, and vendors tend to publish tests where they perform well. Treat benchmark numbers as a shortlist, then validate against your own prompts, tools, latency targets, and failure cases. For more Opus-specific background, see what is Claude Opus 4.8.

Quick verdict

• Pick Claude Opus 4.8 for agentic coding, long autonomous runs, and tasks where silent bugs are expensive.
• Pick GPT-5.5 for general-purpose reasoning, writing, and the widest ecosystem of integrations.
• Pick Gemini 3.5 Flash when speed and cost matter most, or when you need heavy multimodal throughput.

If you split workloads across providers, use a shared API testing workflow in Apidog to send the same prompt to all three models and compare outputs side by side.

The three contenders

Claude Opus 4.8

Claude Opus 4.8, released May 28, 2026, is Anthropic’s most capable model. It supports a 1M-token context window, up to 128K output tokens, adaptive thinking, and an effort parameter that lets you trade reasoning depth for token efficiency.

Use it when your application needs:

• Long-running agent workflows
• Autonomous coding sessions
• Multi-step tool use
• Careful review of generated code
• Higher tolerance for longer or more expensive reasoning

GPT-5.5

GPT-5.5 is OpenAI’s flagship generalist, with strong tool-use support and the largest third-party ecosystem of the three. It is often the safest default when your workload mixes coding, writing, reasoning, extraction, summarization, and agent tasks.

Use it when your application needs:

• Broad task coverage
• Mature SDK and framework support
• Existing OpenAI integrations
• General-purpose reasoning and generation

We compared its predecessor lineup in Cursor Composer 2.5 vs Opus 4.7 vs GPT-5.5.

Gemini 3.5 Flash

Gemini 3.5 Flash leads on speed and price. It supports a 1M-token context window at lower pricing than flagship-tier models and is built for fast streaming and high-throughput use cases.

Use it when your application needs:

• Low latency
• Lower cost per request
• High-volume API calls
• Multimodal processing
• Long-document processing at scale

The Gemini 3.5 Flash pricing breakdown has the numbers, and the Gemini 3.5 vs GPT-5.5 vs Opus 4.7 comparison covers the previous Opus generation.

What Anthropic reported for Opus 4.8

Anthropic’s launch announcement focuses on agentic results:

• Beats GPT-5.5 on the Super-Agent benchmark, which measures end-to-end task completion
• Tops the Legal Agent Benchmark and is the first model to break 10% overall on it
• Scores 84% on Online-Mind2Web, a web-navigation agent test
• Is about 4x less likely than Opus 4.7 to let a code flaw pass unremarked

The implementation takeaway: these are agent and coding signals, not a universal “best model” score. For general reasoning and writing, the gap can be small enough that prompt design, context quality, and tool integration matter more than the model choice.

Pricing and specs

Confirmed figures for Opus 4.8 are listed below. Verify competitor rates on the vendor sites before budgeting because pricing changes often.

Dimension	Claude Opus 4.8	GPT-5.5	Gemini 3.5 Flash
Positioning	Agentic coding, autonomy	Generalist	Speed and cost
Input price per 1M tokens	$5	Check vendor	About $1.50
Output price per 1M tokens	$25	Check vendor	About $9
Context window	1M tokens	Large	1M tokens
Max output	128K tokens	Large	64K tokens
Thinking control	Adaptive + effort dial	Reasoning effort	Built in

Two practical takeaways:

1. Gemini 3.5 Flash is the cost leader because Flash is a fast tier rather than a flagship model.
2. Opus 4.8 is better compared against other frontier reasoning models when the task requires long-horizon autonomy or deep code review.

For exact GPT-5.5 rates, check OpenAI’s platform. For Gemini, see Google’s AI docs. Opus 4.8’s full cost math is in the pricing breakdown.

Coding and agentic work

This is Opus 4.8’s strongest use case. The combination of adaptive thinking, the xhigh effort level, and efficient tool calling is tuned for long agent runs where the model must:

1. Plan the task
2. Inspect files or external data
3. Call tools
4. Make changes
5. Review its own output
6. Recover from mistakes

The reported 4x drop in code defects slipping through review is especially relevant for unattended coding workflows.

A simple routing rule:

function chooseModel(task: {
  type: "coding" | "writing" | "multimodal" | "search" | "summarization";
  autonomy: "low" | "medium" | "high";
  latencySensitive: boolean;
  costSensitive: boolean;
}) {
  if (task.type === "coding" && task.autonomy === "high") {
    return "claude-opus-4-8";
  }

  if (task.latencySensitive || task.costSensitive) {
    return "gemini-3.5-flash";
  }

  return "gpt-5.5";
}

GPT-5.5 is also a strong coding model, and its ecosystem advantage means more ready-made agent frameworks may support it first. Gemini 3.5 Flash can handle coding work well for its price, but it is optimized for throughput rather than deepest reasoning.

For multi-agent architectures, the managed agents vs Agent SDK guide covers build choices that apply regardless of model.

Speed and cost

If your workload is high-volume, latency-sensitive, or cost-capped, Gemini 3.5 Flash is the default starting point. It is built to stream fast and bill light.

Opus 4.8 gives you tuning controls that help narrow the gap:

• Use lower effort for simple tasks.
• Use higher effort only for tasks that need deeper reasoning.
• Use fast mode when user-facing latency matters.
• Route only high-risk coding or agent tasks to Opus.

Example routing pattern:

const modelPolicy = {
  quickChat: {
    model: "gemini-3.5-flash",
    reason: "low latency and low cost",
  },
  generalAssistant: {
    model: "gpt-5.5",
    reason: "broad task coverage",
  },
  autonomousCodeReview: {
    model: "claude-opus-4-8",
    effort: "xhigh",
    reason: "deep reasoning and defect detection",
  },
};

The main point: do not use one model for every request by default. Route by task risk, latency requirement, and budget.

When to pick each model

Pick Opus 4.8 when

• You are running agentic coding sessions.
• A silent bug could cause real business damage.
• The agent needs to make judgment calls unattended.
• The task requires frontier reasoning across many steps.
• You can justify higher cost for higher reliability on hard tasks.

Pick GPT-5.5 when

• You want one model for a broad mix of tasks.
• Your stack depends on the widest integration ecosystem.
• You are already invested in OpenAI tooling.
• You need strong general reasoning, writing, and tool use.
• You want the safest default for mixed workloads.

Pick Gemini 3.5 Flash when

• Throughput and cost are the binding constraints.
• You are doing heavy multimodal work.
• You are processing long documents at scale.
• You need fast streaming for a chat UI.
• You want to reserve expensive models for escalation paths.

Test all three from one workspace

Benchmarks help you shortlist models. Production tests decide the winner.

Run the same prompt set against all three APIs and compare:

• Output quality
• Latency
• Token usage
• JSON validity
• Tool-call behavior
• Failure modes
• Cost per successful task

Apidog lets you test every provider’s API from one workspace.

A practical workflow:

1. Create one request for claude-opus-4-8.
2. Duplicate it for GPT-5.5.
3. Duplicate it again for Gemini 3.5.
4. Keep the same prompt, system message, and test data.
5. Run each request.
6. Compare response quality, latency, and usage token counts.
7. Add assertions for structured outputs.
8. Mock endpoints to test fallback logic without spending credits.

Example assertions you can use for model comparison:

pm.test("response is valid JSON", function () {
  pm.response.to.have.jsonBody();
});

pm.test("contains required field", function () {
  const body = pm.response.json();
  pm.expect(body).to.have.property("summary");
});

pm.test("latency under threshold", function () {
  pm.expect(pm.response.responseTime).to.be.below(3000);
});

For structured output testing, score models with the same checks instead of relying only on manual inspection.

Example scoring dimensions:

Test	What to check
JSON validity	Does the model return parseable JSON?
Schema compliance	Are all required fields present?
Latency	Does the request finish within your SLA?
Token usage	Is the response cost acceptable?
Correctness	Does the answer solve the task?
Safety	Does the model avoid risky or unsupported actions?

Download Apidog, build the three requests, and run your real workload against each model. The best model for your use case is usually clear within a dozen representative prompts.

The Opus 4.8 API guide has the request shape to start from.

Recommended implementation pattern

For production apps, use model routing instead of hard-coding one provider everywhere.

A minimal abstraction can look like this:

type ModelName = "claude-opus-4-8" | "gpt-5.5" | "gemini-3.5-flash";

type LlmRequest = {
  model: ModelName;
  messages: Array<{
    role: "system" | "user" | "assistant";
    content: string;
  }>;
  temperature?: number;
  maxTokens?: number;
};

async function callLlm(request: LlmRequest) {
  switch (request.model) {
    case "claude-opus-4-8":
      return callAnthropic(request);

    case "gpt-5.5":
      return callOpenAI(request);

    case "gemini-3.5-flash":
      return callGemini(request);

    default:
      throw new Error(`Unsupported model: ${request.model}`);
  }
}

Then route requests by risk:

function routeRequest(input: {
  isAutonomousCodingTask: boolean;
  requiresFastStreaming: boolean;
  isHighVolume: boolean;
}) {
  if (input.isAutonomousCodingTask) {
    return "claude-opus-4-8";
  }

  if (input.requiresFastStreaming || input.isHighVolume) {
    return "gemini-3.5-flash";
  }

  return "gpt-5.5";
}

This keeps your app flexible as model pricing, latency, and quality change.

FAQ

Is Claude Opus 4.8 better than GPT-5.5?

On agentic benchmarks, Anthropic reports a win, including on Super-Agent. For general chat and writing, the two are close. Opus 4.8 is the stronger pick for autonomous coding; GPT-5.5 is the stronger default for broad generalist workloads and ecosystem support.

Which is cheapest: Opus 4.8, GPT-5.5, or Gemini 3.5?

Gemini 3.5 Flash is the cost leader because it is a fast tier, not a flagship model. Opus 4.8 is $5 per million input tokens and $25 per million output tokens. Check vendor sites for current GPT-5.5 rates.

Which model is best for coding?

Opus 4.8 is built for coding and agentic workflows, with adaptive thinking, the xhigh effort level, and about 4x fewer code defects slipping through than Opus 4.7. GPT-5.5 is also strong and has broader tooling support.

Do all three support a 1M-token context?

Opus 4.8 and Gemini 3.5 Flash do. GPT-5.5 offers a large context window; check OpenAI for the exact current figure.

Should I trust vendor benchmark numbers?

Use them as a starting point, not a final decision. Vendors usually report the tests where they perform well. Validate on your own prompts, documents, tools, and latency budget before committing.

Can I switch between the three without rewriting my app?

Mostly, yes. Each provider has its own SDK and response shape, but a thin abstraction over requests and responses lets you swap models. Testing each one in Apidog first makes the differences clear before you wire them into production.