Anthropic has released Claude Opus 5, positioning it as a model that approaches the frontier intelligence of Claude Fable 5 at half the price. It is now the default model on Claude Max and the strongest available on Claude Pro.

The company said Opus 5 sets new state-of-the-art results on coding and knowledge-work benchmarks including Frontier-Bench and GDPval-AA, though it remains behind Mythos 5 on cybersecurity tasks. On Frontier-Bench v0.1, Opus 5 more than doubles predecessor Opus 4.8's performance at lower cost, while on ARC-AGI 3 its score is three times that of the next-best model.

Anthropic's pre-deployment testing found Opus 5 to be its most aligned model yet, with the lowest rates of deceptive behaviour and reduced susceptibility to misuse. The model has not advanced dual-use capabilities in biology or offensive cybersecurity, and remains behind Mythos 5 in both areas, though its cyber classifiers are less restrictive than those applied to Fable 5.

Opus 5 is priced at $5 per million input tokens and $25 per million output tokens, matching Opus 4.8, and is available today across all platforms including the Claude API.

DevSecOps for AI: Why 90% Stays the Same—and the 10% That Changes Everything
Is your DevSecOps pipeline ready for AI—or just ready for the AI you tested last week? AI systems behave probabilistically. The same prompt injection attack can succeed 50 times in a row, then fail completely the next minute. Traditional shift-left testing was built for determinism. AI isn’t. That gap is where risk lives. Three members of Google’s security advocacy team break down what actually changes—and what doesn’t—when AI enters your DevSecOps pipeline. You’ll learn: • Why 90% of AI security is still traditional security—and exactly where the novel 10% creates new exposure • Why DevSecOps transformations fail within a year—and the top-down cultural shift that prevents it • How the latest DORA research shows AI agents amplify existing practices, good or bad, at scale • What AI runtime security (e.g., Model Armor) does that a WAF cannot • Why AI logs capturing PII and system instructions in plain text demand a new approach to observability Key topics: Non-determinism in AI testing • Continuous evaluation vs. pre-deployment scans • Model Armor & runtime security layers • Sensitive data redaction in logs • Prompt injection defense-in-depth • Agentic workload security • WAF limitations with AI agents • DevSecOps governance & top-down culture For CISOs, DevSecOps leads, and security architects navigating AI adoption: the pipeline you spent three years building is mostly still valid. This session tells you exactly what to add. All viewers will receive a c’heat sheet’ compiling links galore courtesy of Aron Eidelman.
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