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AnthropicNew version

claude-haiku-4-5, claude-haiku-4-5-20251001

New version, announced 15 October 2025. Rerun the suite: If you are considering this model to cut cost or latency, the comparison that matters is your own dataset scored on both models, not a published summary of either.

What Anthropic says changed

Anthropic launched Claude Haiku 4.5, which the release notes describe as its fastest and most intelligent Haiku model, with near-frontier performance. The release notes name the family alias claude-haiku-4-5. Anthropic's model deprecations table lists the dated snapshot claude-haiku-4-5-20251001 as Active, with retirement not sooner than 15 October 2026.

Anthropic: Claude API release notes

announced 2025-10-15 . verified 2026-09-24

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
claude-haiku-4-5, claude-haiku-4-5-20251001
Change type
New version
Announced
2025-10-15
Verified
2026-09-24

What this kind of change typically touches

No impact note on this entry. A note is published only where a cited practitioner source supports it, and for this change we have none. The entry stands on the provider's announcement alone.

What to do this week

Rerun the suite

If you are considering this model to cut cost or latency, the comparison that matters is your own dataset scored on both models, not a published summary of either.

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

AnthropicDeprecation

temperature, top_p, top_k

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.

Plan a migrationSearch 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.

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

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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