GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro

Updated and fact-checked: 2026-07-23 · API specifications and prices from official sources

Short answer: GPT-5.5 and Claude Opus 4.7 are generally available frontier APIs with roughly one-million-token context windows and 128K maximum output. Gemini 3.1 Pro remains a preview API with a 1,048,576-token input limit and 65,536-token output limit. There is no universal winner: choose with a task-specific evaluation, total token cost, latency and the tools your application actually needs.

Correction: The previous article mixed marketing claims, third-party benchmark conclusions and unsourced recommendations. This version uses current first-party model pages and labels preview status, token limits and pricing boundaries explicitly.

Verified specification snapshot

Model	API status	Input context	Maximum output	Official model ID
OpenAI GPT-5.5	Generally available	1,050,000 tokens	128,000 tokens	`gpt-5.5`
Claude Opus 4.7	Generally available	1,000,000 tokens	128,000 tokens	`claude-opus-4-7`
Gemini 3.1 Pro	Preview	1,048,576 tokens	65,536 tokens	`gemini-3.1-pro-preview`

Limits describe what an API accepts, not how much context it can use effectively for every task. Long prompts also increase latency and cost, and they can hide the small amount of evidence that actually determines the answer.

API pricing snapshot

Model	Standard input / 1M tokens	Standard output / 1M tokens	Important boundary
GPT-5.5	$5.00	$30.00	OpenAI documents higher multipliers when prompts exceed 272K input tokens.
Claude Opus 4.7	$5.00	$25.00	Standard pricing covers the 1M context window; cache, batch, fast mode and regional options have separate rates.
Gemini 3.1 Pro Preview	$2.00 at ≤200K; $4.00 above 200K	$12.00 at ≤200K; $18.00 above 200K	Output pricing includes thinking tokens; the preview has no free API tier.

Prices change and provider billing has additional dimensions. Recheck the linked pricing pages before purchasing or publishing a cost forecast. Use the AI cost calculator with measured input, cached input, reasoning/output and tool-use volume rather than multiplying one headline rate.

Which model should you test first?

Start with GPT-5.5 when

your workload uses OpenAI's hosted shell, computer use, MCP, tool search or Codex-oriented workflows;
you need 128K maximum output or very long mixed document/code context;
your existing stack already uses OpenAI responses, tools and observability.

Start with Claude Opus 4.7 when

the task is long-horizon software engineering, detailed review or high-resolution visual/document analysis;
you need a 1M context window with 128K maximum output and adaptive thinking;
your application already depends on Claude's files, PDF, prompt caching or client/server tool ecosystem.

Start with Gemini 3.1 Pro when

you need multimodal text, image, video, audio or PDF input in one preview model;
your workflow depends on Gemini code execution, function calling, structured output or Google grounding;
the lower published preview token rates matter enough to accept preview lifecycle risk.

These are selection filters, not quality rankings. A model can have the right feature list and still perform poorly on your prompts, schemas, repository or review standard.

Why vendor benchmarks do not settle the choice

OpenAI, Anthropic and Google each publish extensive evaluations, but model pages can use different prompts, tool access, effort settings, scoring dates and test harnesses. Even a table that places all three names side by side does not prove the same setup matches your production workflow. Treat vendor numbers as release evidence, not a purchasing decision.

A reproducible comparison in six steps

Select 20–50 representative tasks. Include routine successes, expensive edge cases and tasks where a wrong answer causes real rework.
Freeze the evidence. Use the same repository commit, documents, tools, time budget and network policy.
Define a rubric before testing. Score correctness, completeness, unsupported claims, tool errors, edits required and time to accepted result.
Run more than once. Record model ID, date, effort/thinking setting, temperature where applicable and failures—not only the best run.
Measure the whole bill. Capture input, cached input, output/reasoning, tool and search charges plus human review time.
Keep a fallback. Preview models and aliases can change; pin snapshots or exact IDs when reproducibility matters.

Minimal evaluation record

Task ID:
Model ID and date:
Provider / region:
Reasoning or effort setting:
Input tokens / cached tokens / output tokens:
Tool calls and external search:
Wall-clock time:
Correctness score:
Unsupported claims:
Human edits required:
Accepted result: yes / no
Estimated total cost:

Official sources

Verified July 23, 2026. Availability, aliases, limits and prices can change; official provider documentation overrides this snapshot.