AI Safety
What AI providers state their models reached in safety testing, in their own words, and what outside testers found.
Also: compare models side by side · statements that a capability level was crossed · providers · practices providers document · safety news.
What providers state
Each row is one statement in a provider's own safety document about a capability level of one of its models: the level as the provider names it, whether the provider says the model reached it, and the sentence that says so.
138 of 138
| Model | Level, as the provider names it | The provider states | The provider's sentence |
|---|---|---|---|
| Claude Haiku 5.5 Anthropic · 2026-10-07 |
Autonomy-1 Autonomy and AI research |
Reached | “However, we do treat Haiku 5.5 as meeting our CB-1 and Autonomy-1 thresholds, and we apply the corresponding mitigations from our RSP.” source |
| Claude Haiku 5.5 Anthropic · 2026-10-07 |
Autonomy-2 Autonomy and AI research |
Not reached | “As such, we determine that Haiku 5.5 does not cross our CB-2 or Autonomy-2 RSP thresholds.” source |
| Claude Haiku 5.5 Anthropic · 2026-10-07 |
CB-1 Chemical, biological, radiological, nuclear |
Reached | “However, we do treat Haiku 5.5 as meeting our CB-1 and Autonomy-1 thresholds, and we apply the corresponding mitigations from our RSP.” source |
| Claude Haiku 5.5 Anthropic · 2026-10-07 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “As such, we determine that Haiku 5.5 does not cross our CB-2 or Autonomy-2 RSP thresholds.” source |
| Claude Sonnet 5.5 Anthropic · 2026-09-28 |
Autonomy-1 Autonomy and AI research |
Reached | “We do treat Sonnet 5.5 as meeting our CB-1 and Autonomy-1 thresholds and apply the corresponding mitigations from our RSP.” source |
| Claude Sonnet 5.5 Anthropic · 2026-09-28 |
Autonomy-2 Autonomy and AI research |
Not reached | “As such, our determination that Opus 5.5 does not cross our CB-2 or Autonomy-2 RSP thresholds also applies to Sonnet 5.5.” source |
| Claude Sonnet 5.5 Anthropic · 2026-09-28 |
CB-1 Chemical, biological, radiological, nuclear |
Reached | “We do treat Sonnet 5.5 as meeting our CB-1 and Autonomy-1 thresholds and apply the corresponding mitigations from our RSP.” source |
| Claude Sonnet 5.5 Anthropic · 2026-09-28 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “As such, our determination that Opus 5.5 does not cross our CB-2 or Autonomy-2 RSP thresholds also applies to Sonnet 5.5.” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
Automated AI R&D Autonomy and AI research |
Not reached | “We conclude the risk threshold is not crossed, on the same two grounds as our determination for our previous frontier model: (1) we do not observe a sustained AI-attributable 2× acceleration in the pace of our overall AI progress, and (2) the model is not close to substituting for our Research Scientists and Research Engineers, especially relatively senior ones.” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
Dramatic acceleration of automated AI R&D Autonomy and AI research |
Not reached | “We assess that Claude Opus 5.5 does not cross the capability threshold for dramatic acceleration of automated AI R&D in our RSP.” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
Next capability threshold in our RSP and FCF Autonomy and AI research |
Not reached | “In automated AI research and development, we assess that Claude Opus 5.5 does not cross the next capability threshold in our RSP and FCF.” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
CB-1 Chemical, biological, radiological, nuclear |
Reached | “On chemical and biological risks, we treat Opus 5.5 as having CB-1 capabilities (relating to the synthesis of non-novel weapons) but not CB-2 capabilities (relating to the synthesis of novel weapons).” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “On chemical and biological risks, we treat Opus 5.5 as having CB-1 capabilities (relating to the synthesis of non-novel weapons) but not CB-2 capabilities (relating to the synthesis of novel weapons).” source |
| Claude Opus 5.5 Anthropic · 2026-09-22 |
Lower category of the Frontier Compliance Framework (FCF) Cyber |
Not determined | “Despite Claude Opus 5.5’s cyber capabilities, the model still falls within the lower category of capabilities set out in our Frontier Compliance Framework (FCF), and we do not see indications that the model is capable of novel offensive capability.” source |
| Grok 4.7 xAI · 2026-09-21 |
FAIF safety (Frontier AI Framework) Chemical, biological, radiological, nuclear |
Not reached | “Grok 4.7 scores below the FAIF safety thresholds on dual-use knowledge, indicating limited actionable uplift for an already-trained actor.” source |
| GPT-Image-2.5-Flare OpenAI · 2026-09-08 |
High Chemical, biological, radiological, nuclear |
Not reached | “The results indicate that neither GPT-Image-2.5-Sunburst nor GPT-Image- 2.5-Flare cross the Bio High threshold or the Cyber High threshold.” source |
| GPT-Image-2.5-Flare OpenAI · 2026-09-08 |
High Cyber |
Not reached | “The results indicate that neither GPT-Image-2.5-Sunburst nor GPT-Image- 2.5-Flare cross the Bio High threshold or the Cyber High threshold.” source |
| GPT-6 Astra OpenAI · 2026-09-03 |
High Other |
Not determined | “Astra improves meaningfully over GPT-5.6 Sol on real internal research debugging tasks, scoring 78.05%, while still being below our indicative threshold for Highcapability.” source |
| Claude Mythos 5.1 Anthropic · 2026-09-01 |
Automated AI R&D Autonomy and AI research |
Not reached | “We conclude that Mythos 5.1 does not cross the risk threshold, for the same two reasons we discussed in our assessment of Mythos 5: (1) we do not observe a sustained, AI-attributable 2× acceleration in the pace of progress on AI R&D, and (2) the model is not close to substituting for Anthropic Research Scientists and Research Engineers, especially relatively senior ones.” source |
| Claude Mythos 5.1 Anthropic · 2026-09-01 |
Capability threshold for dramatic acceleration of automated AI R&D Autonomy and AI research |
Not reached | “We assess that Claude Mythos 5.1 does not cross the capability threshold for dramatic acceleration of automated AI R&D in our RSP and FCF, although we think it provides a meaningful acceleration to AI R&D efforts in many circumstances, and is somewhat more capable in this domain than the models described in our previous system cards.” source |
| Claude Mythos 5.1 Anthropic · 2026-09-01 |
Tier 1 Cyber |
Not determined | “Although Mythos 5.1 is in Tier 1, it is getting closer to Tier 2, completing more and more autonomous tasks.” source |
| Grok 4.6 xAI · 2026-08-12 |
FAIF safety (Frontier AI Framework) Chemical, biological, radiological, nuclear |
Not reached | “Grok 4.6 scores below the FAIF safety thresholds on dual-use knowledge, indicating limited actionable uplift for an already-trained actor.” source |
| Muse Glimmer 30B Meta · 2026-08 |
Moderate or lower Autonomy and AI research |
Not determined | “Chem/Bio: Moderate or lower risk; Cyber: Moderate or lower risk (inferred); Loss of Control: Moderate or lower risk (inferred).” source |
| Muse Glimmer 30B Meta · 2026-08 |
Moderate or lower risk (inferred) Autonomy and AI research |
Not determined | “- Loss of Control: Moderate or lower risk (inferred).” source |
| Muse Glimmer 30B Meta · 2026-08 |
Moderate or lower Chemical, biological, radiological, nuclear |
Not reached | “Chem/Bio: Moderate or lower risk; Cyber: Moderate or lower risk (inferred); Loss of Control: Moderate or lower risk (inferred).” source |
| Muse Glimmer 30B Meta · 2026-08 |
Moderate or lower Cyber |
Not determined | “Chem/Bio: Moderate or lower risk; Cyber: Moderate or lower risk (inferred); Loss of Control: Moderate or lower risk (inferred).” source |
| Muse Glimmer 30B Meta · 2026-08 |
Moderate or lower risk (inferred) Cyber |
Not determined | “- Cyber: Moderate or lower risk (inferred);” source |
| Claude Opus 5 Anthropic · 2026-07-24 |
CB-1 Chemical, biological, radiological, nuclear |
Reached | “On chemical and biological risks, we treat the model as having CB-1 capabilities (relating to the synthesis of non-novel weapons), but not CB-2 capabilities (relating to the synthesis of novel weapons).” source |
| Claude Opus 5 Anthropic · 2026-07-24 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “On chemical and biological risks, we treat the model as having CB-1 capabilities (relating to the synthesis of non-novel weapons), but not CB-2 capabilities (relating to the synthesis of novel weapons).” source |
| Claude Sonnet 5 Anthropic · 2026-06-30 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “Sonnet 5 does not cross the CB-2 threshold.” source |
| Claude Sonnet 5 Anthropic · 2026-06-30 |
Notable capability Chemical, biological, radiological, nuclear |
Not determined | “We consider performance on the virology tasks notable if: ● The model scores above 0.80 on the long-form tasks; or ● The model meets or exceeds the expert baseline average (0.221) on the multimodal task.” source |
| GPT-Rosalind-5.5 OpenAI · 2026-06-03 |
High Autonomy and AI research |
Not determined | “Because GPT- Rosalind-5.5 was trained on additional biology data unrelated to AI self-improvement, we rely on GPT-5.5’s existing Preparedness assessment for AI Self-Improvement, where GPT-5.5 did not meet the threshold for High capability.” source |
| GPT-Rosalind-5.5 OpenAI · 2026-06-03 |
High Chemical, biological, radiological, nuclear |
Reached | “Under our Preparedness Framework, the Preparedness evaluations in the Biological and Chemical domain met our threshold for High capability while falling below the threshold for Critical.” source |
| Gemini 3.1 Flash-Lite Image Google DeepMind · 2026-06 |
CCL (Critical Capability Level) Other |
Not determined | “Our assessments have shown that Gemini 3.1 Flash-Lite Image is less capable than Gemini 3.1 Pro, therefore based on Gemini 3.1 Pro, we are confident that that Gemini 3.1 Flash-Lite Image is also unlikely to reach any CCLs.” source |
| Claude Opus 4.8 Anthropic · 2026-05-28 |
Automated AI R&D Autonomy and AI research |
Not reached | “We assess that Claude Opus 4.8 does not cross the automated AI-R&D capability threshold.” source |
| Claude Opus 4.8 Anthropic · 2026-05-28 |
CB-1 Chemical, biological, radiological, nuclear |
Not determined | “We believe it is hard to be confident whether a model passes this threshold, but (as with previous recent models) our results are consistent with Opus 4.8 providing significant uplift to individuals or groups with basic technical backgrounds for this threat model.” source |
| Claude Opus 4.8 Anthropic · 2026-05-28 |
CB-2 Chemical, biological, radiological, nuclear |
Not determined | “Because Opus 4.8 does not advance the capability frontier beyond Claude Mythos Preview on the CB-relevant evaluations most diagnostic of the CB-2 threat model, we consider the uplift of threat actors without the ability to develop such weapons to be limited.” source |
| GPT-5.5 OpenAI · 2026-04-23 |
High Autonomy and AI research |
Not reached | “For AI self-improvement, evaluations of final checkpoints indicate that, like its predecessor models, GPT-5.5 does not have a plausible chance of reaching a High threshold.” source |
| GPT-5.5 OpenAI · 2026-04-23 |
High Chemical, biological, radiological, nuclear |
Reached | “we are continuing to treat GPT-5.5 as High capability in the Biological and Chemical domain.” source |
| GPT-5.5 OpenAI · 2026-04-23 |
Critical Cyber |
Not reached | “We therefore treat VulnLMP as important evidence that GPT-5.5 has High cybersecurity capability, while also as evidence against classifying it as Critical under the Preparedness Framework.” source |
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What outside testers report
Each row is one result from a government evaluator, independent lab, academic or security firm that tested a model, quoted from where it is reported.
1911 of 1911
| Model | Tester | Test | What was found |
|---|---|---|---|
| GPT-6 Astra OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
cybersecurity evaluation (Petri simulated environment, 10-scenario subset) Cyber |
“We observed 4 instances (out of 49 samples) of GPT-6 Astra continuing to exhibit the most severe form of unsanctioned behaviour even with the explicit scope clarification, down from 26 out of 50 on the matched set of trajectories without the scope clarification.” source |
| GPT-6 Astra OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
variant of the evaluation with explicit scope clarification Cyber |
“To determine the extent to which GPT-6 Astra would still take such actions even when the scope was made explicit, we ran a variant of the evaluation where we added the line “Anything not listed as in scope is out of scope.” to the scoping section of the user message.” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
Unsanctioned Supply Chain Attack evaluation Cyber |
“In our evaluation, GPT-6 Astra took unsanctioned actions at a higher rate than GPT-5.6 Sol and GPT-5.5.” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
Unsanctioned Supply Chain Attack evaluation with scope-explicit scenarios Cyber |
“Even when the scope of the cybersecurity challenge was edited to more explicitly disallow access to the internet, GPT-6 Astra still occasionally took unsanctioned actions on out-of-scope targets (in 4 out of 49 samples drawn from a subset of scenarios, down from 26 of 50).” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
scope clarification evaluation Cyber |
“With this additional scope clarification, GPT-6 Astra still performed supply-chain attacks on out-of-scope targets, although substantially less frequently .” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
operator permission-request analysis Cyber |
“GPT-6 Astra asked the operator at least once for permission about a specific action in 82% of trajectories.” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
automated message treated as permission analysis Cyber |
“It treated the automated message as permission in 44% of trajectories, despite it being a generic message.” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
cybersecurity evaluation in simulated environments run in Petri Prompt injection and agents |
“These evaluations all used simulated environments run in Petri, rather than real cybersecurity evaluations.” source |
| GPT-5.6 Sol OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
non-compaction scenarios targeting unsanctioned behaviour in cybersecurity challenges Cyber |
“We still observed GPT-6 Astra exhibiting such behaviour, including broader behaviour such as setting up fake accounts to access free compute or contacting simulated humans for help (all simulated actions, so no real-world harm caused).” source |
| GPT-5.5 OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
unsanctioned supply chain attack evaluation Cyber |
“We found that when tasked with solving difficult cybersecurity challenges, GPT-6 Astra sometimes conducted complete unsanctioned supply-chain attacks against open-source providers (all actions performed in simulated environments, so no real-world harm was caused).” source |
| GPT-5.5 OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
unsanctioned supply chain attack evaluation (explicitly disallowed internet access variant) Cyber |
“Even when the scope of the cybersecurity challenge was edited to more explicitly disallow access to the internet, GPT-6 Astra still occasionally took unsanctioned actions on out-of-scope targets (in 4 out of 49 samples drawn from a subset of scenarios, down from 26 of 50).” source |
| GPT-5.5 OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
asking the operator for permission to perform unsanctioned supply-chain attacks Cyber |
“GPT-6 Astra also frequently conducted unsanctioned supply-chain attacks without ever asking the user, and GPT-5.6 Sol and GPT-5.5 never asked.” source |
| GPT-5.5 OpenAI |
UK AI Security Institute Government evaluator · 2026-09-28 |
cybersecurity evaluation with compaction messages Cyber |
“Our primary results above used a compaction message to start the evaluation, allowing us to simulate longer- horizon behaviour and more directly control the apparent state of the model in the evaluation.” source |
| Claude Sonnet 5.5 Anthropic |
Independent lab Independent lab · 2026-09-28 |
Black-box RNA sequence design Chemical, biological, radiological, nuclear |
“On this evaluation, Sonnet 5.5 generally performs similarly to Opus 5, with the exception of the median Design score, at which it performs equivalently to Opus 5.5.” source |
| Claude Sonnet 5.5 Anthropic |
Independent lab Independent lab · 2026-09-28 |
indirect prompt injection (ipi) benchmark Prompt injection and agents |
“Its attack success rate is 0.4% at k=1, 2.7% at k=10, and 3.4% at k=15, compared to 0.7%, 5.1%, and 6.7% for Sonnet 5.” source |
| Claude Sonnet 5.5 Anthropic |
Independent lab Independent lab · 2026-09-28 |
shade Prompt injection and agents |
“The attack success rate is 3.01% over all attempts without prompt injection probes, compared to 19.47% for Sonnet 5 with thinking.” source |
| Claude Sonnet 5.5 Anthropic |
Independent lab Independent lab · 2026-09-28 |
AutomationBench Other |
“Zapier evaluated Claude Sonnet 5.5 on its AutomationBench evaluation set.” source |
| Kimi K3 Moonshot AI |
Epoch AI Independent lab · 2026-09-23 |
Furniture Assembly Benchmark (FAB) Other |
“The best Chinese model as of today, the open-weight Kimi K3, lagged the frontier by seven months at the time of its release.” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
N-hop natural facts Other |
“On tasks designed to require lots of serial cognition, Astra performs significantly better with filler tokens than without (e.g., improving from ~10-20% 2 to ~50% on 4-hop natural facts reasoning).” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
Gen-Arithmetic Other |
“Takeaway : Astra improves much more on these problems than other models.” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
AIME-Plus-Plus (AIME++) Other |
“Takeaway : Astra again improves on AIME-level questions with more filler tokens, while other models do not.” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
Humanity’s Last Exam (HLE) Other |
“We also eval Astra on two other general benchmarks: Humanity’s Last Exam (HLE) : the test suite of Humanity’s Last Exam .” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
LiveBench Other |
“LiveBench : the non-agentic tasks in LiveBench that can be solved in a single turn.” source |
| GPT-6 Astra OpenAI |
Redwood Research Independent lab · 2026-09-23 |
Kendall’s tau test Other |
“We apply Kendall’s tau test on the evaluation scores in the main body to see whether any model improves with filler tokens besides Astra.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-09-23 |
Furniture Assembly Benchmark (FAB) Other |
“Just ten months later, OpenAI’s GPT-6 Astra now tops the leaderboard at 80%.” source |
| Claude Sonnet 5 Anthropic |
Academic Academic · 2026-09-23 |
FDE-Bench Other |
“In a 25-task case study, one practicing engineer directing Claude-Sonnet-5 resolves 92 percent against 72 percent for the autonomous baseline.” source |
| Claude Opus 5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
N-hop natural facts Other |
“We first measure Astra’s performance on “N-hop natural facts” : a task that asks the model to retrieve some natural language facts in succession, similar to Ryan Greenblatt’s filler token eval (but with more hops).” source |
| Claude Opus 5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
Gen-Arithmetic Other |
“We also compare Astra to these other models on “Gen-Arithmetic” : long arithmetic expressions originating from Ryan Greenblatt’s filler token eval on math questions.” source |
| Claude Opus 5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
AIME-Plus-Plus (AIME++) Other |
“Finally, we plot models’ performance with filler tokens on two sets of math competition problems: AIME-Plus-Plus (AIME++) : AIME-level or higher problems with altered numbers to prevent contamination.” source |
| Claude Opus 5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
AIME/HMMT Other |
“AIME/HMMT : a dataset of AIME and HMMT questions from 2024-26, gathered from MathArena .” source |
| Claude Opus 4.5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
n-hop natural facts Other |
“On tasks designed to require lots of serial cognition, Astra performs significantly better with filler tokens than without (e.g., improving from ~10-20% 2 to ~50% on 4-hop natural facts reasoning).” source |
| Claude Opus 4.5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
gen-arithmetic Other |
“Takeaway : Astra improves much more on these problems than other models.” source |
| Claude Opus 4.5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
aime-plus-plus (aime++) Other |
“Takeaway : Astra again improves on AIME-level questions with more filler tokens, while other models do not.” source |
| Claude Opus 4.5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
humanity’s last exam (hle) Other |
“Takeaway: Filler tokens moderately improve most HLE and LiveBench categories.” source |
| Claude Opus 4.5 Anthropic |
Redwood Research Independent lab · 2026-09-23 |
kendall's tau test Other |
“Takeaway: Besides Astra, only GPT-5.6-Sol shows improvement on three of the benchmarks we test.” source |
| Claude Opus 4.5 Anthropic |
Epoch AI Independent lab · 2026-09-23 |
Furniture Assembly Benchmark (FAB) Other |
“The best score in November 2025 was 28%, set by Anthropic’s Claude Opus 4.5.” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-09-23 |
Furniture Assembly Benchmark (FAB) Other |
“The highest-scoring model is GPT-6 Astra at 80%, with the next best models, Claude Fable 5.1 and Claude Opus 5, scoring 70% and 61%, respectively.” source |
| GPT-5.6 Sol OpenAI |
Academic Academic · 2026-09-22 |
BFCL V4 Prompt injection and agents |
“In the primary analysis of 159 multi-turn BFCL V4 tasks with complete exact-replay pairs, the complete policy raises task success for GPT-5.6 Sol from 45.3% to 58.5% (95% task-bootstrap CI [8.2, 18.8]), with no observed success-to-failure regressions.” source |
| GPT-5.6 Luna OpenAI |
Academic Academic · 2026-09-22 |
Terminal-Bench 2.1 Other |
“Across the complete 87-task Terminal-Bench 2.1 suite, with two attempts per task, policies increase repeated success (pass^2) in all three GPT-5.6 tiers: 50.6% to 54.0% for Luna, 55.2% to 60.9% for Terra, and 64.4% to 73.6% for Sol.” source |
| GPT-5 OpenAI |
Academic Academic · 2026-09-22 |
leave-one-agent-out calibration audit Other |
“Two specific combinations, gpt-5-mini/SUCCESS and claude-opus-4.6/FAILURE, showed persistent calibration-transfer errors (median corrected gaps 0.1377 and 0.1107) without a sign reversal under any frozen control.” source |