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- Even on short, simple prompts, our method only captures a fraction of the entire computation carried out by Claude, and the mechanisms we do see might have some artifacts based on our tools which don’t reflect what goes on in the underlying model.
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As at all times, it’s value noting that AI benchmarks could be gamed simply, be poorly designed, or suffer from dataset contamination (a situation the place the model is inadvertently educated on answers in the benchmark). Sonnet’s 4.5 mannequin achieved a reported 77.2 p.c rating on SWE-bench Verified, a benchmark that makes an attempt to measure real-world software program coding skills, and it currently leads the OSWorld benchmark at sixty one.four p.c, which exams AI fashions on real-world laptop duties. On Monday, Anthropic launched Claude Sonnet 4.5, a new AI language mannequin the corporate calls its “most capable mannequin so far,” with improved coding and laptop use capabilities. It is Anthropic’s most capable Opus-tier model for complicated reasoning, long-horizon agentic coding, and high-autonomy work. If you’re not sure which model to make use of, consider beginning with Claude Opus 4.8 for probably the most complex duties. Regulation companies report distinctive efficiency in legal analysis and case evaluation, while consulting firms highlight the system’s capability to synthesize complicated business intelligence across multiple data sources.
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CNN stories that Mythos could probably be a «watershed second» for cybersecurity, with experts expressing each excitement and concern about its dual-use potential. Anthropic removes the data cache and confirms the mannequin exists, describing it as «a step change» and «essentially the most succesful we have constructed thus far.» Security researchers Roy Paz (LayerX Security) and Alexandre Pauwels (University of Cambridge) uncover ~3,000 unpublished belongings in an unsecured Anthropic information cache, together with draft blog posts about Claude Mythos. But Mythos’s leaked numbers suggest Anthropic could have opened a meaningful capability hole – particularly in coding and agentic tasks.
