For a new production deployment, neither is the default pick today: as of November 9, 2026, both providers label these exact models deprecated. Anthropic says Claude Sonnet 4.5 retires on November 30, 2026. OpenAI marks GPT-5 deprecated; a GPT-5 retirement date is not established by the evidence used here. Before choosing a replacement, consult each provider’s current model recommendations. If you are maintaining an existing integration, compare the models on your actual workload and plan a migration. (platform.claude.com; OpenAI API model documentation)
If you specifically need these versions, GPT-5 has the lower listed uncached API token rates and the larger documented context and output limits. Claude Sonnet 4.5 costs more per token, but its documented context and output limits may be sufficient for many tasks. Those specifications and prices do not tell you which model will complete your particular task better; run a matched evaluation before committing.
Version scope: This comparison is for the provider API models
gpt-5/gpt-5-2025-08-07andclaude-sonnet-4-5/claude-sonnet-4-5-20250929. It is not a comparison of GPT-5.1, GPT-5.2, GPT-5 Codex, ChatGPT plans, Claude subscriptions, or a third-party model router. (OpenAI API model documentation; Claude Platform Docs)
Exact models and production status
Anthropic documents the Claude API alias as claude-sonnet-4-5 and its pinned model ID as claude-sonnet-4-5-20250929. Its model reference lists the model as deprecated, with retirement scheduled for November 30, 2026. OpenAI documents gpt-5 and the pinned snapshot gpt-5-2025-08-07; both are marked deprecated. The GPT-5 retirement date is not established by the evidence used here. Check OpenAI’s current model recommendations before selecting a replacement. These lifecycle notices matter more for a new integration than a small difference in token price: you may need to migrate, retest, and update model-specific settings. (Claude Platform Docs; OpenAI API model documentation)
The APIs also use different request surfaces. OpenAI lists GPT-5 on its Responses API and Chat Completions API, among other endpoints. Anthropic’s direct model API is its Messages API. An application using one provider’s request format should not assume it can switch model names and send the same payload unchanged. (OpenAI API model documentation; Claude Platform Docs)
Quick API comparison
| API detail | GPT-5 | Claude Sonnet 4.5 |
|---|---|---|
| Provider model ID | gpt-5; pinned snapshot gpt-5-2025-08-07 |
claude-sonnet-4-5; pinned ID claude-sonnet-4-5-20250929 |
| Lifecycle shown in provider docs | Deprecated; check OpenAI’s current model recommendations | Deprecated; retires November 30, 2026 |
| Context window | 400,000 tokens | 200,000 tokens |
| Maximum output | 128,000 tokens | 64,000 tokens |
| Standard API price per 1 million tokens | $1.25 input; $10 output | $3 input; $15 output |
| Cached input / cache reads | $0.125 per million cached input tokens | $0.30 per million cache reads; separate cache-write rates apply |
| Provider API surface | Includes Responses and Chat Completions | Messages API |
Limits, lifecycle status, IDs, and prices above are from the providers’ model documentation and are for their respective direct API listings. Cloud-platform billing and access rules can differ; check the relevant platform’s model listing if you call either model through a cloud provider rather than the provider’s own API. (OpenAI API model documentation; Claude Platform Docs)
Which one fits your workload?
- If uncached token spend is the main constraint: GPT-5 has the lower listed input and output rates. That is a rate-card comparison, not a promise of lower total cost for every task: retries, output length, caching, and whether the model gets the job right all affect spend.
- If your request needs more than 200,000 but no more than 400,000 context tokens, or more than 64,000 but no more than 128,000 output tokens: GPT-5 has the larger documented limits. Check the exact request and any provider constraints before relying on a maximum; having capacity does not mean using it is economical or improves quality. (OpenAI API model documentation)
- If your existing system already uses Anthropic’s Messages API or Claude-specific tool flow: Sonnet 4.5 may be simpler to retain in the short term, provided its retirement timeline fits your migration plan. Anthropic documents tool use as a model/API interaction in which your application executes client-side tools; do not confuse that with a model independently executing arbitrary actions. (Claude Platform Docs)
- If the decision is about writing quality, coding success, tool reliability, or latency: the specifications alone do not establish a winner. Score the exact models on your prompts and application tool loop. Do not transfer results for GPT-5.1, GPT-5.2, or GPT-5 Codex to GPT-5; those are different model versions or variants.
API pricing: a reproducible example
The listed direct API rates are in USD per million tokens. For a simple uncached text request with 100,000 input tokens and 10,000 output tokens, using the listed standard rates:
- GPT-5: (100,000 × $1.25 ÷ 1,000,000) + (10,000 × $10 ÷ 1,000,000) = $0.225.
- Claude Sonnet 4.5: (100,000 × $3 ÷ 1,000,000) + (10,000 × $15 ÷ 1,000,000) = $0.45.
On this deliberately simple, uncached token mix, GPT-5’s token charge is half Claude Sonnet 4.5’s. It is not a forecast for your workload: it excludes cached-token treatment, cache writes, retries, and any separately billed tools or services. Anthropic lists Sonnet 4.5 cache writes at $3.75 per million tokens for a 5-minute cache and $6 per million for a 1-hour cache; cache reads are $0.30 per million. OpenAI’s GPT-5 page lists cached input at $0.125 per million. Apply cache rates only to tokens eligible under each provider’s rules. (OpenAI API model documentation; Claude Platform Docs)
These are API rates, not the price of ChatGPT or Claude consumer/team subscriptions. Subscription access, usage limits, and API billing are different products; do not use a subscription price to estimate API cost. (Claude pricing)
How to test before production
Use the same representative workload for both models, and keep the model versions and settings fixed during the comparison:
- Select real tasks, including ordinary cases and difficult edge cases. Include the tools, schemas, and context your application actually sends.
- Set success criteria before running the test. For code, for example, score whether tests pass, changes integrate with the existing code, and critical edge cases are handled—not just whether the answer looks plausible.
- Run both APIs with comparable instructions and tool availability. Record model ID, settings, input/output tokens, failures, and elapsed time. Repeat enough trials to see whether results are consistent for your use case.
- Compare task success and severity of failures alongside latency and estimated cost per successful task. A lower token bill is not a saving if the model needs more retries or produces unusable output.
- Check operational fit separately: endpoint and request format, rate limits for your account tier, required regions or data handling, and the work needed to migrate away from a deprecated model.
A third-party article supplied for this comparison reports coding outcomes from two tasks in one observability codebase, using GPT-5 Codex/GPT-5.1 Codex and Claude Sonnet 4.5. That is a narrow, author-reported test of different GPT variants—not evidence that base GPT-5 is generally better. Treat such reports as ideas for test cases, not as a production guarantee.
Bottom line
For an existing integration that must use these exact versions, GPT-5 is cheaper on the listed uncached token rates and has higher documented context and output ceilings. Claude Sonnet 4.5 may make sense as a temporary continuation choice when your integration already depends on Anthropic’s API, but its scheduled November 30, 2026 retirement makes migration timing urgent. For a new production choice, consult the providers’ current model recommendations and run the same workload tests; do not select a deprecated model solely because its old rate card looks attractive. (OpenAI API model documentation; Claude Platform Docs)
Sources
Claude Sonnet 4.5 | Claude Platform Docs
Pricing | Claude Platform Docs