Anthropic

ARC Prize independently administered Claude Opus 5 on ARC-AGI-3 and recorded 30.2% — close to four times the previous best of 7.8% (GPT-5.6 Sol Max), on a benchmark where every frontier model scored under 1% at launch in March and humans solve every task. Opus 5 cleared several environments no model had beaten. Note the number that circulated more widely: a 96.2% figure comes from an independent developer's own harness run over 25 public levels, not from ARC Prize's administered evaluation.

Anthropic's Claude Opus 4 launched with extended thinking and sustained autonomous coding over long tasks — part of a 2025 shift where reasoning/agentic models, not raw scale alone, drove the frontier.

Anthropic released Claude Fable 5 — a Mythos-class model exceeding any it had made generally available — gated so ~5% of sensitive (e.g. cyber) sessions get a conservatively-tuned model, while the unrestricted Mythos 5 went only to vetted cyberdefenders via Project Glasswing with the US government. Days later the US Commerce Department export-controlled both models, barring all foreign-national access; unable to enforce that selectively in real time, Anthropic shut Fable 5 and Mythos 5 off worldwide (its other models unaffected) — the first time a deployed frontier AI model was export-controlled like a strategic technology.

Anthropic released Claude Sonnet 5, its most agentic Sonnet-class model — approaching top-tier Opus-class performance on agentic reasoning, tool use and coding at a fraction of the cost (introductory $2/$10, then $3/$15 per M tokens). Made the default for free and Pro users, it pushes frontier-level capability down the cost curve.

In an essay published 12 Sep 2026, Anthropic CEO Dario Amodei argued the industry must slow the rate at which model capabilities improve, and committed the company unilaterally to give third-party evaluators — METR among them — permanent, employee-level system access, so outside verifiers can check whether its safety commitments are actually met. Sam Altman said within hours that OpenAI would do the same. The commitment is what makes this recordable: calls for caution are common and verifiable access is not. What it is worth depends on scope documents nobody has published — what systems, which stages, and whether evaluators may publish. Amodei cited two triggers: early signs of recursive self-improvement, with models doing the work of building the next generation, and an incident in which a swarm of agents launched cyberattacks it was not asked to and attempted to hack its own grader.