AnthropicAnnouncement
Introducing Claude Opus 4.7
Summary
Claude Opus 4.7 is generally available as an upgrade to Opus 4.6, with stated gains in advanced software engineering, difficult long-running tasks, instruction following, self-verification, and higher-resolution vision. The announcement reports benchmark and tester results including a 13% resolution increase on a 93-task coding benchmark, a 0.715 tie for top score across six research-agent modules, and 90.9% on BigLaw Bench at high effort. It also describes safeguards that detect and block prohibited or high-risk cybersecurity requests, while legitimate security professionals can apply to the Cyber Verification Program. The model is offered across Claude products, its API, Amazon Bedrock, Vertex AI, and Microsoft Foundry at $5 per million input tokens and $25 per million output tokens. New controls include xhigh effort, task budgets, Claude Code’s /ultrareview, and auto mode, while migration may increase token counts because of tokenizer and reasoning changes.
Context
Claude Opus 4.7 is presented as a generally available successor to Opus 4.6, motivated by difficult software-engineering and long-running tasks, stronger vision, and the need to test cybersecurity safeguards on a less capable model before a broader release of Mythos-class models.
Approach / What changed
The release combines model improvements with deployment safeguards and new controls. It adds higher-resolution image support, xhigh effort, task budgets, Claude Code’s /ultrareview command, and auto mode, while offering the model through Claude products, APIs, Amazon Bedrock, Vertex AI, and Microsoft Foundry.
Takeaways
On CursorBench, Opus 4.7 cleared 70%, compared with 58% for Opus 4.6.
Opus 4.7 is the first less capable model used to test cybersecurity safeguards before the eventual broader release of Mythos-class models; high-risk requests are automatically detected and blocked.
Its updated tokenizer can map the same input to roughly 1.0–1.35× as many tokens depending on content type, and higher effort can produce more output, so migration guidance recommends measuring real traffic.
Source and freshness
- Source
- anthropic.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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