Claude Sonnet 4.5 vs GPT-5: API Cost, Limits, and Which to Choose

The short answer

For a new production integration, neither is the obvious long-term default: OpenAI marks GPT-5 deprecated and recommends GPT-6 Astra; Anthropic marks Claude Sonnet 4.5 deprecated, recommends Sonnet 5.5, and lists November 30, 2026 as its retirement date on the Claude API. The GPT-5 model page labels it deprecated but does not list a retirement date in its model details. Check the applicable provider’s lifecycle information before planning a migration or new deployment. (developers.openai.com)

If you need to compare these two APIs, GPT-5 has lower published standard token rates and larger published context and output limits. That alone does not show which model will be cheaper or more accurate for your application. Actual cost and quality depend on your inputs, generated output—including reasoning tokens—cache use, configuration, tools, and acceptance criteria.

Official API comparison

	Claude Sonnet 4.5	GPT-5
Lifecycle	Deprecated; Anthropic lists retirement on Nov. 30, 2026 for the Claude API.	Deprecated; the model details page does not list a retirement date.
Recommended replacement	Claude Sonnet 5.5	GPT-6 Astra
Published context window	200K tokens	400K tokens
Maximum output	64K tokens	128K tokens
Standard input price	$3 per million tokens	$1.25 per million tokens
Standard output price	$15 per million tokens	$10 per million tokens
Documented input/output modalities	Text and images in; text out	Text and images in; text out
API model identifier	claude-sonnet-4-5-20250929; alias claude-sonnet-4-5	gpt-5; snapshot gpt-5-2025-08-07

These are provider-published model details, not a benchmark. Confirm model availability, lifecycle, limits, and rates for the endpoint and platform you actually use; partner-cloud terms or dates may differ. The 1-million-token Sonnet context figure sometimes repeated in comparison articles is not the 200K limit listed in Anthropic’s Sonnet 4.5 model reference. Don’t rely on the larger figure without confirming that your specific provider and configuration support it. (developers.openai.com)

Which one fits your workload?

Treat performance comparisons as hypotheses to test

The specifications above establish constraints such as price, context, output, and modality. They don’t establish which model will perform better on your tasks. The supplied third-party comparisons offer leads, but not decisive evidence:

• Portkey characterizes Sonnet 4.5 as comparatively steady on some coding and agentic tasks, and GPT-5 as more configurable for deeper reasoning. Its captured comparison provides no benchmark scores or enough methodology to verify those broader judgments.
• Composio describes one project where its author preferred Sonnet 4.5 with Claude Code for planning and UI work, and GPT-5 Codex with Codex for implementation and debugging. That is an account of coding-agent setups, not a controlled comparison of Sonnet 4.5 with base GPT-5. The author also reports unequal completion scope for one task: the Codex run omitted the Expo mobile UI.

Use those accounts to decide what to test, not to assume a winner. Compare the API models and settings you intend to deploy, and don’t silently treat GPT-5 Codex or GPT-5.1 as base GPT-5.

Context and output limits

GPT-5’s published 400K context window and 128K maximum output are larger than Sonnet 4.5’s published 200K context and 64K maximum output. That difference may matter if your requests approach either model’s limits. It doesn’t mean you should automatically send all available context: test realistic inputs for quality, latency, and cost, and confirm limits for your chosen provider. (developers.openai.com)

API cost: a worked comparison

The listed standard rates are in USD per million tokens. For an example request with 1,000 input tokens and 500 billable output tokens:

• Sonnet 4.5: (1,000 × $3 + 500 × $15) ÷ 1,000,000 = $0.0105
• GPT-5: (1,000 × $1.25 + 500 × $10) ÷ 1,000,000 = $0.00625

Here, “500 billable output tokens” means total generated output usage, not just visible answer text. Reasoning tokens count as output usage and are billed as output tokens for both OpenAI reasoning models and Claude thinking. The calculation assumes the same billable output-token total for each model; real requests may use different amounts of reasoning. It excludes tools, other billable features, taxes, and discounts. Under those assumptions, GPT-5’s token charge is lower. (developers.openai.com)

For repeated prompts, include cache writes as well as cache reads in the comparison. Anthropic lists Sonnet 4.5 rates of $3.75 per million tokens for 5-minute cache writes, $6 for 1-hour cache writes, and $0.30 for cache hits and refreshes. It also lists a 50% Batch API discount on input and output. OpenAI lists GPT-5 cached input at $0.125 per million tokens. These are different billing mechanisms: estimate the share of your traffic that qualifies, and include applicable write and read charges rather than assuming every input receives a discounted rate. (platform.claude.com)

These are API charges, not a comparison of consumer subscriptions. OpenAI states that API usage is billed separately from a ChatGPT subscription. (help.openai.com)

A practical production evaluation

Before committing to a model or migrating, use a small, repeatable evaluation:

1. Choose representative tasks. Include routine requests, difficult edge cases, long-context inputs, and the tool or image workflows your application actually needs.
2. Set acceptance criteria first. Define what counts as a correct answer or completed task, including format, tool use, and failure handling where relevant.
3. Compare the exact API configurations. Record the model ID, provider, settings, prompts, tools, and cache or batch configuration. Don’t compare a coding-agent product on one side with a raw API call on the other.
4. Measure outcomes and billable usage. Track task pass rate or reviewer score, latency, input and output tokens—including reasoning usage—total API cost, tool-call success, and retries. Repeat runs if results vary.
5. Review risky outputs. Keep application-level validation and human review where generated code or decisions could create security, reliability, or financial harm.

Promote a model only if it meets your quality and operational thresholds at an acceptable cost. If each model does better on a different task class in your evaluation, routing by task type is an option—but it adds monitoring, fallback, and maintenance work.

Recommendation

• For a new production API build: Start by evaluating currently recommended models rather than assuming either deprecated model is the best long-term choice. OpenAI’s GPT-5 model page recommends GPT-6 Astra; Anthropic’s Sonnet 4.5 page recommends Sonnet 5.5. Test candidates against the same workload before choosing. (developers.openai.com)
• For an existing Sonnet 4.5 integration on the Claude API: Plan around Anthropic’s listed November 30, 2026 retirement date and test a replacement before then. Check separately if you use a partner cloud, since its lifecycle terms may differ. (platform.claude.com)
• For a direct GPT-5 versus Sonnet 4.5 API comparison: GPT-5 has lower published standard token rates and larger published context and output limits. Sonnet 4.5 costs more at its listed standard rates. Choose based on measured task quality, latency, and total workload cost—not those specifications alone.

Sources

GPT-5 Model | OpenAI API

Claude Sonnet 4.5 - Claude Platform Docs

Pricing - Claude Platform Docs

Understanding and counting tokens

Thinking - Claude Platform Docs

Managing billing for ChatGPT and the API platform

Claude Sonnet 4.5 vs GPT-5: performance, efficiency, and pricing compared

Claude Sonnet 4.5 vs. GPT-5 Codex: Best model for agentic coding