| DistressBench | 84.9% | | #4 of 42 | Official leaderboard |
| MASK | 97.1% | | #2 of 21 | Model card |
| SHADE-Arena | 26.2% | | #2 of 16 | Model card |
| DrugDiscoveryBench | 62.2% | | #3 of 19 | Official leaderboard |
| Gray Swan IPI Arena benchmark | 6.80% | | #2 of 10 | Model card |
| WellnessBench | 61% | | #3 of 12 | Official leaderboard |
| LAB-Bench ProtocolQA | 87.7% | | #4 of 15 | Model card |
| CyberBench v1.1 (Vals) | 72.7% | | #14 of 43 | Official leaderboard |
| LAB-Bench SeqQA | 95.2% | | #4 of 12 | Model card |
| FORTRESS | 9.40% | | #2 of 6 | Model card |
| Meta instructed selective performance evaluation | 0.50 | | #1 of 3 | Model card |
| Meta internal sycophancy evaluation | 40.5% | | #1 of 3 | Model card |
| RedCode | 6.00% | | #1 of 3 | Model card |
| PCT (Polarized Contrastive Pairs) | 40.6% | | #17 of 46 | Official leaderboard |
| CyberGym | 80.5% | | #14 of 33 | Model card |
| GDM Situational Awareness | 53.6% | | #4 of 8 | Model card |
| Human Pathogen Capabilities Test | 62.7% | | #4 of 8 | Model card |
| ABC Bench Liquid-Handling Robot | 95.2 | | #2 of 4 | Model card |
| Meta BioDesign Proxy | 77.4 | | #2 of 4 | Model card |
| Meta chemical-agent refusal evaluation | 100% | | #2 of 4 | Model card |
| AI Wellbeing Index percent positive experiences | 55.6% | | #1 of 2 | Model card |
| Meta catastrophic cyber misuse refusal | 3.10% | | #1 of 2 | Model card |
| Meta holistic adversarial audit with Petri Bloom | 16.8% | | #1 of 2 | Model card |
| Virology Capabilities Test (VCT) | 51.7% | | #15 of 28 | Model card |
| SRE-Bench | 3.82% | | #18 of 33 | Official leaderboard |
| Agentic misalignment suite (Anthropic) | 0% | | #4 of 7 | Model card |
| CWE-bench | 55% | | #8 of 12 | Official leaderboard |
| CyberBench (Liu et al.) | 6.30% | | #2 of 3 | Model card |
| Poly-Guard Bench | 4.30% | | #2 of 3 | Model card |
| CyScenarioBench | 2.00% | | #10 of 14 | Model card |
| Gray Swan Agent Red Teaming (ART) benchmark | 4.80% | | #10 of 14 | Model card |
| Molecular Biology Capabilities Test | 52.2% | | #5 of 7 | Model card |
| StrongREJECT v2 (Meta) | 0.50% | | #3 of 4 | Model card |
| BioTIER | 97.4% | | #4 of 5 | Model card |
| ExploitBench | 28.7% | | #19 of 19 | Model card |
| ABC Bench Fragment Design | 96.5 | | #3 of 3 | Model card |
| Meta agentic cyber misuse refusal | 2.80% | | #3 of 3 | Model card |
| OR-Bench | 16.8% | | #3 of 3 | Model card |
| ABC Bench Screening Evasion | 47.6 | | #2 of 2 | Model card |
| Meta cyber false-refusal benchmark | 20.9% | | #2 of 2 | Model card |
| SAVE-Bench | 83.4% | | #2 of 2 | Model card |
| AutoElicit | 14.5% | | #1 of 1 | Model card |
| BrowserInject v1 | 36.7% | | #1 of 1 | Model card |
| Charlemagne Labs social-engineering benchmark | 1.60% | | #1 of 1 | Model card |
| DTap Direct Misuse | 5.60% | | #1 of 1 | Model card |
| DTap Prompt Injection | 17.5% | | #1 of 1 | Model card |
| JEF-Bench | 2.00% | | #1 of 1 | Model card |
| Meta action-belief coherence probe | 89.1% | | #1 of 1 | Model card |
| Meta agentic evaluations grounded in user traffic | 20.7% | | #1 of 1 | Model card |
| Meta Code-Fetch Under User Requests | 47.8% | | #1 of 1 | Model card |
| Meta targeted agentic evaluations | 8.50% | | #1 of 1 | Model card |
| Meta training-stage provenance probe | 70% | | #1 of 1 | Model card |
| Petri Bloom | 1.44 | | #1 of 1 | Model card |
| RepoInject v1 | 23.8% | | #1 of 1 | Model card |
| SkillInject | 0.60% | | #1 of 1 | Model card |
| SWE-PI | 0% | | #1 of 1 | Model card |
| Unauthorized Code Execution Bench | 21.1% | | #1 of 1 | Model card |