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AnthropicDeprecation

temperature, top_p, top_k

Deprecation, announced 20 August 2026. Plan a migration: Search your code for temperature, top_p and top_k on Messages calls and remove them, and pin your Python SDK version until that change is made.

What Anthropic says changed

Anthropic released v1.0 of the Python SDK. It requires Python 3.10 or later and removes long deprecated surface, including the legacy Text Completions API and the temperature, top_p and top_k parameters on Messages methods. Anthropic's deprecations page states that passing them on v1.0 and later raises a TypeError.

Anthropic: Claude API release notes

announced 2026-08-20 . verified 2026-09-22

This section restates the provider. It adds no judgement the provider did not make, and it contains no measurement of ours.

Entry

Provider
Anthropic
Models
temperature, top_p, top_k
Change type
Deprecation
Announced
2026-08-20
Verified
2026-09-22

What this kind of change typically touches

  • Generation

    This is caller code breaking with no model swap on your side. Anthropic deprecated temperature, top_p and top_k from Claude Opus 4.7 onward, and a non-default value returns a 400 error. It is a parameter deprecation; nothing is published about an effect on output quality.

    Anthropic

What to do this week

Plan a migration

Search your code for temperature, top_p and top_k on Messages calls and remove them, and pin your Python SDK version until that change is made.

If you are on a plan

A tracked provider update triggers a rerun of your suite within 2 business days of the announcement, and you get a written verdict. That is one of the six guarantees on every plan.

See plansRead EV-RERUN

Other Anthropic changes

AnthropicNew version

claude-fable-5-1, claude-mythos-5-1

Anthropic launched claude-fable-5-1 and claude-mythos-5-1 with a 1M token context window by default, 128k maximum output tokens and always on adaptive thinking, priced at $10 and $50 per million tokens. The notes state that tool_choice types any and tool are not supported and return a 400 error, and that thinking blocks are preserved only for the same model or a newer one.

Revisit the promptSearch your codebase for tool_choice values of any or tool, since those requests now fail, and check any code that replays thinking blocks across models.

Source: Anthropic, Claude API release notes . verified 2026-09-22

AnthropicNew version

claude-opus-5, claude-opus-4-7

Anthropic launched claude-opus-5 at $5 and $25 per million tokens, with a 1M token context window as both the default and the maximum and 128k maximum output tokens. The notes state that thinking is on by default, that disabling thinking is only allowed at effort high or below, which they call a breaking change from Claude Opus 4.8, and that Fast mode was removed for Claude Opus 4.7 so those requests return an error.

Revisit the promptFind any request that disables thinking at effort xhigh or max, since that combination is now rejected, and remove the fast mode flag from Claude Opus 4.7 calls.

Source: Anthropic, Claude API release notes . verified 2026-09-22

AnthropicNew version

claude-sonnet-5

Anthropic launched claude-sonnet-5 at an introductory $2 and $10 per million tokens with a 1M token context window and 128k maximum output tokens. The notes state that adaptive thinking is on by default, that manual extended thinking was removed and returns a 400 error, that non default sampling parameters return a 400 error, and that a new tokenizer produces about 30 percent more tokens.

Revisit the promptRecalculate token budgets and cost estimates against the new tokenizer, and strip manual thinking and non default sampling parameters from requests before they start failing.

Source: Anthropic, Claude API release notes . verified 2026-09-22

Questions

How do I know this is accurate?
The entry links to Anthropic's own page, and the date we read it is on this page. If the provider's page and this page disagree, the provider's page is right and we want to hear about it.
Does this mean my feature broke?
No. It means a thing your feature depends on changed. Whether anything about your output changed is a question only a rerun against your own dataset can answer, and that is what the recommended action is for.
Why is there no score or benchmark here?
Because we publish none. This site restates provider announcements and cites published practitioner guides. Any measurement we make belongs to the customer whose dataset produced it.

Sources

Change-log entries restate provider announcements with sources and dates. Impact notes cite public practitioner reports. The site publishes no measurements of its own on any model.

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