Claude Opus vs GPT-5: the short answer
This comparison uses Claude Opus 5.5 (claude-opus-5-5) and OpenAI’s GPT-5 API model (gpt-5, snapshot gpt-5-2025-08-07). They are exact API model names, not broad consumer-app labels. Documentation checked November 12, 2026.
Important for production buyers: Anthropic lists Opus 5.5 as its latest, active Opus model. OpenAI’s GPT-5 model page labels GPT-5 deprecated and recommends a newer model. The page gives no shutdown date, so don’t assume GPT-5 will remain available for a new deployment: check the model’s current status and shutdown_date through the OpenAI Models API for your account. If you are maintaining an existing GPT-5 integration, compare its actual workload against Opus 5.5 before planning a migration. (Anthropic Claude Platform model documentation; OpenAI API model and deprecation documentation.)
On the documented token rates, GPT-5 costs less per token: $1.25 per million input tokens and $10 per million output tokens, versus $4 and $20 for Opus 5.5. But price alone does not pick a production model. Opus 5.5 offers a larger documented context window, while GPT-5 is marked deprecated. The captured provider documentation does not establish a universal quality winner or provide a version-matched head-to-head benchmark. Test the exact models on your own tasks—and treat GPT-5 as a migration-sensitive choice.
At a glance
| API detail | Claude Opus 5.5 | GPT-5 |
|---|---|---|
| Direct-provider model ID | claude-opus-5-5 |
gpt-5; pinned snapshot gpt-5-2025-08-07 |
| Documentation status | Active; listed as Anthropic’s latest Opus model | Deprecated; OpenAI recommends a newer model |
| Context window | 1 million tokens | 400,000 tokens |
| Maximum output | 128,000 tokens for synchronous Messages API | 128,000 tokens |
| Input and output | Text and image input; text output | Text and image input; text output |
| Standard input price | $4 per million tokens | $1.25 per million tokens |
| Standard output price | $20 per million tokens | $10 per million tokens |
| Tool use | Tool use is supported; forced tool selection is not supported on Opus 5.5 | Function calling is supported |
These are provider-documentation specifications, not claims that the models behave identically in every API channel. For example, Anthropic documents Opus 5.5 in its Claude API; OpenAI lists GPT-5 for Chat Completions and Responses, among other endpoints. Check the current provider docs and your account’s model access before deployment.
Which model fits which workload?
Consider Opus 5.5 if the workload benefits from its documented 1-million-token context, or if your evaluation favors it for long-running coding or knowledge-work tasks. Anthropic describes those as intended uses; that positioning is not independent proof of better results on your application.
Consider GPT-5 only with its deprecation status in view. Its documented input and output rates are lower, and the API model page lists function calling. But because OpenAI marks it deprecated, confirm access and the applicable shutdown date before building a new dependency around it. A lower token price is not a durable advantage if you must migrate soon.
Do not treat an old benchmark comparison as a current verdict. Competitor pages in the supplied research discuss different pairs—including Opus 4.1 vs. GPT-5 and Opus 5 vs. GPT-5.6—and do not establish results for this exact Opus 5.5/GPT-5 API pair. No universal winner is supported here. Evaluate the exact IDs under your own prompts, tools, and acceptance criteria.
API differences that can change your choice
- Context and output: Anthropic lists a 1-million-token context window and 128,000-token synchronous output limit for Opus 5.5. OpenAI lists GPT-5 at 400,000 context tokens and 128,000 maximum output tokens. A large context limit is a capacity figure, not a guarantee that quality remains constant across that entire context.
- Images: Both model pages list image input and text output. Neither page establishes audio or video support for these exact model IDs; don’t infer it from other models in either family.
- Reasoning controls: Use only the reasoning controls documented for the specific model and API channel you evaluate. The controls may differ between providers; the exact settings were not confirmed here.
- Tools: Both providers document tool/function calling. Anthropic’s Opus 5.5 documentation also notes that forced tool use is unsupported; applications that require a particular tool to be called should account for that behavior. OpenAI lists function calling for GPT-5.
- Lifecycle: Opus 5.5 is listed as active. GPT-5 is labeled deprecated. OpenAI says its Models API can expose an optional
shutdown_date; check that value for the model available to your account rather than assuming the deprecated model has a particular end date.
API cost: a like-for-like example
For a hypothetical request with 100,000 uncached input tokens and 20,000 output tokens, excluding tools, caching, and other adjustments:
- Claude Opus 5.5: (0.1 × $4) + (0.02 × $20) = $0.80.
- GPT-5: (0.1 × $1.25) + (0.02 × $10) = $0.325.
Both token counts are below the documented maximum output limits and context windows. This is a calculation from listed standard rates, not a measured task cost or quality comparison. It does not include cached tokens, batch pricing, or tool charges. Anthropic separately lists cache-write and cache-read rates; OpenAI separately lists a cached-input rate. Apply those only when your request actually qualifies under each provider’s rules.
For production budgeting, compare cost per accepted task, not just cost per API call. Track input and output tokens, retries, tool charges, task completion, latency, and the share of responses that pass your quality checks. GPT-5’s documented lower rate should also be weighed against its deprecated status and any migration cost.
A practical way to choose
- Pin the exact model IDs. Record
claude-opus-5-5and the GPT-5 alias or snapshot you actually tested. Don’t transfer results between model versions. - Build a representative test set. Include routine requests, difficult cases, and important failure cases from your application.
- Keep the evaluation comparable. Use the same task instructions, tools, permissions, output requirements, and scoring rules. Use only the reasoning controls documented for each model and API channel.
- Score useful outcomes. Measure correctness, required-format compliance, tool-call quality, refusals, retries, and human acceptance—not just a benchmark score or one impressive example.
- Compare full operating cost and lifecycle risk. Include input/output tokens, any applicable cache or batch pricing, tool charges, latency, and the cost of migrating away from GPT-5 if required.
- Recheck before release. Confirm model access, documentation, and shutdown status at deployment time. Re-run the evaluation when you change model IDs or provider settings.
This is a recommended evaluation process, not a claim that a production head-to-head test was performed for this article.
Bottom line: Opus 5.5 is the clearly documented active Opus choice here and has the larger stated context window. GPT-5 has lower listed token prices, but OpenAI marks it deprecated. For a new production system, verify GPT-5 access and shutdown status before considering it; for either model, let a version-pinned workload evaluation decide whether the quality and operating cost fit.
Sources
Claude Opus 5.5 — Claude Platform Docs