Claude Opus 5 Tops Artificial Analysis Index While Maintaining Strict Cybersecurity Safeguards
Anthropic has launched Claude Opus 5, a new flagship model that tops the independent Artificial Analysis Intelligence Index while incorporating deliberate restrictions on offensive cybersecurity tasks.
Released on 24 July, Opus 5 is priced at half the cost of the restricted Fable 5 and is available to all users without export-control limitations that previously affected earlier models. The model supports a 1,000,000-token context window by default and generates up to 128,000 output tokens, with reasoning enabled by default through five effort levels: low, medium, high, extra, and max.
On the Artificial Analysis Intelligence Index, which aggregates nine benchmarks including Terminal-Bench, SciCode, GPQA Diamond, and Humanity’s Last Exam, Opus 5 scored 61 points at maximum effort, placing first among 187–191 models. It outperformed Fable 5 (60) and GPT-5.6 Sol (59). However, the model consumed approximately 100 million tokens during evaluation compared with the median of 63 million, resulting in slower response times exceeding one minute to first token.
Internal Anthropic benchmarks showed strong gains: 43.3% on Frontier-Bench v0.1 (versus 33.7% for Fable 5), 1861 Elo on GDPval-AA v2, and a notable 30.2% on ARC-AGI-3. The model also led on OSWorld 2.0 with 70.6%. It underperformed on DeepSWE v1.1 (68.8% vs 72.7% for GPT-5.6 Sol), HealthBench Professional, and Legal Agent Benchmark.
In cybersecurity testing, Opus 5 reached 79.4% on OSS-Fuzz vulnerability discovery, nearly matching Mythos 5 at 80%. Exploit generation success remained low at only four tasks versus 13 for Mythos 5. The model was not trained on offensive cybersecurity and routes blocked requests for binary scanning, penetration testing, or exploit generation to Opus 4.8.
Adjustable effort levels allow users to balance performance and cost, with Anthropic recommending extra for coding and agentic work and high for general tasks. The model’s tendency toward extensive self-verification improves reliability on complex tasks but can cause unproductive overthinking on certain autonomous research scenarios.
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