Anthropic releases Claude Opus 4.7 with reduced cyber capabilities compared to Mythos Preview

Anthropic released Claude Opus 4.7 on Thursday, explicitly positioning it as "broadly less capable" than its most powerful model, Claude Mythos Preview. The company describes the new model as an improvement over previous versions in specific areas while intentionally limiting its cybersecurity capabilities.

According to Anthropic, Claude Opus 4.7 delivers better performance in software engineering, instruction following, completing real-world work tasks, and using file system-based memory. However, the company says its cyber capabilities fall short of Claude Mythos Preview, which Anthropic deployed to select companies earlier this month as part of Project Glasswing, a new cybersecurity initiative.

The model includes automated safeguards that detect and block requests indicating "prohibited or high-risk cybersecurity uses," according to the company's release. Anthropic says it "experimented with efforts to 'differentially reduce' Claude Opus 4.7's cyber capabilities during training."

Limited deployment strategy

Anthropic is treating Claude Opus 4.7 as a testing ground for safety measures before a broader release of Mythos-class models. "What we learn from the real-world deployment of these safeguards will help us work towards our eventual goal of a broad release of Mythos-class models," the company stated.

Security professionals interested in using Claude Opus 4.7 for "legitimate cybersecurity purposes" must apply through a formal verification program, rather than gaining direct access to the model.

Claude Mythos Preview remains available only to select companies participating in Project Glasswing. Anthropic has not disclosed pricing, benchmark scores, context window size, or other technical specifications for either model.

What this means

This marks a rare instance of a major AI company explicitly releasing a less capable version of its technology. Anthropic appears to be using differential capability reduction as a safety mechanism—maintaining strong performance in productive tasks while limiting potential misuse in cybersecurity applications. The approach suggests the company views Mythos-class models as too risky for general release without further safety research. Whether users will accept a deliberately hobbled model when competitors may not impose similar restrictions remains unclear.