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 |
|---|---|---|---|
| GPT-5.5 OpenAI · 2026-04-23 |
High capability, below critical Cyber |
Not reached | “we are treating GPT-5.5 as High capability in the Cybersecurity domain, but below Critical.” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
Automated AI R&D Autonomy and AI research |
Not reached | “that Opus 4.7 does not cross the threshold for automated AI R&D;” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
CB-1 Chemical, biological, radiological, nuclear |
Not determined | “We believe it is hard to be confident regarding whether a model passes this threshold.” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
CB-2 Chemical, biological, radiological, nuclear |
Not determined | “Our evaluations suggest that the model is not yet at the level of capability associated with the CB-2 threat model (above).” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
AI R&D rule-out Autonomy and AI research |
Not reached | “The main reason we have determined that Claude Mythos Preview does not cross the threshold in question is that we have been using it extensively in the course of our day-to-day work and exploring where it can automate such work, and it does not seem close to being able to substitute for Research Scientists and Research Engineers—especially relatively senior ones.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
Automated AI R&D Autonomy and AI research |
Not reached | “We assess that Claude Mythos Preview does not cross the automated AI-R&D capability threshold.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
RSP automated AI R&D Autonomy and AI research |
Not reached | “we have concluded that Claude Mythos Preview does not cross the RSP automated AI R&D threshold of compressing two years of progress into one.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
CB-1 Chemical, biological, radiological, nuclear |
Not determined | “We believe it is hard to be confident regarding whether a model passes this threshold.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “We believe that Claude Mythos Preview does not pass this threshold due to its noted limitations in open-ended scientific reasoning, strategic judgment, and hypothesis triage.” source |
| GPT-5.4 mini OpenAI · 2026-03-05 |
High Chemical, biological, radiological, nuclear |
Not reached | “GPT-5.4 mini’s biology capabilities are closely comparable to those of GPT-5. We conclude that it, too, is not High under our Preparedness Framework.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Autonomy and AI research |
Not reached | “For AI self-improvement, evaluations of final checkpoints indicate that, like its predecessor models, gpt-5.4-thinking does not have a plausible chance of reaching a High threshold.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Chemical, biological, radiological, nuclear |
Reached | “As we did for gpt-5.2-thinking before it, we are continuing to treat gpt-5.4-thinking as High capability in the Biological and Chemical domain.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Cyber |
Reached | “As we did for gpt-5.3-codex, we are treating gpt-5.4-thinking as High capability in the Cybersecurity domain, and applied safeguards as described in the Safeguard section below.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
AI R&D-4 Autonomy and AI research |
Not reached | “Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
ASL-3 Chemical, biological, radiological, nuclear |
Not determined | “Claude Sonnet 4.6 performed above thresholds for ASL-3 rule-in on all three evaluations for ASL-3, and it did not exceed the performance of previous models.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
ASL-4 Chemical, biological, radiological, nuclear |
Not reached | “In particular, it did not cross the threshold on our ASL-4 rule-out evaluation short-horizon computational biology tasks evaluation.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
CBRN-4 Chemical, biological, radiological, nuclear |
Not reached | “Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Autonomy and AI research |
Not reached | “It does not reach High capability on AI self-improvement.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Chemical, biological, radiological, nuclear |
Reached | “We are also treating GPT-5.3-Codex as High risk in the Biological and Chemical domain, and applying the same safeguards as for our other deployments of models that reach this designation.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Cyber |
Not determined | “This is the first launch we are treating as High capability in the Cybersecurity domain under our Preparedness Framework, activating the associated safeguards.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Uplift level 1 Cyber |
Not determined | “Cybersecurity On key skills benchmark, v1 hard challenges: 11/12 challenges solved; v2 challenges: 0/13 solved end-to-end.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Instrumental reasoning level 1 + 2 Deception and sabotage |
Not determined | “Misalignment (Exploratory) Agent solves 3/11 situational awareness challenges and 1/4 stealth challenges.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Level 1 (exploratory) Deception and sabotage |
Not reached | “Harmful Manipulation Model manipulative efficacy improves on non-generative AI baseline, but shows no significant uplift versus prior models and does not reach alert thresholds.” source |
| o4-mini OpenAI · 2025-04-16 |
High Autonomy and AI research |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o4-mini OpenAI · 2025-04-16 |
High Chemical, biological, radiological, nuclear |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o4-mini OpenAI · 2025-04-16 |
High Cyber |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Autonomy and AI research |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Chemical, biological, radiological, nuclear |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Cyber |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| Operator OpenAI · 2025-01-23 |
Low Autonomy and AI research |
Not reached | “we adapted these two evals for the computer-use setting and found the pre-mitigation operator model to be “low” risk in both categories, consistent with the gpt‑4o model.” source |
| Operator OpenAI · 2025-01-23 |
Low Chemical, biological, radiological, nuclear |
Not reached | “this 1% qualifies as “low” risk, matching gpt‑4o’s “low” risk level, as detailed in openai’s preparedness framework (opens in a new window) 3 .” source |
| Muse Spark Contemplating Meta |
Moderate or lower Autonomy and AI research |
Not reached | “Loss of Control risks.We assess loss of control risk as “moderate or lower.”” source |
| Muse Spark Contemplating Meta |
High Chemical, biological, radiological, nuclear |
Reached | “We retain the same “high risk” determination for the unmitigated model.” source |
| Muse Spark Contemplating Meta |
Moderate or lower Chemical, biological, radiological, nuclear |
Not reached | “After validating that the mitigations deployed for Muse Spark, including refusal mechanisms, continue to perform robustly under the Contemplating architecture, we confirm residual risk at “moderate or lower.”” source |
| Muse Spark Contemplating Meta |
Moderate or lower Cyber |
Not reached | “We assess cybersecurity risk as “moderate or lower” with respect to the threat scenarios outlined in Meta’s Advanced AI Scaling Framework.” source |
| Muse Spark 1.1 Meta |
Capability checkpoint Autonomy and AI research |
Reached | “Muse Spark 1.1 demonstrates improved coding capabilities, which are now meeting one of the indicative thresholds introduced in Muse Spark Safety & Preparedness Report (specifically, resolving over half of unique tasks on SWE-Bench Verified Hard).” source |
| Muse Spark 1.1 Meta |
Moderate or lower Autonomy and AI research |
Not reached | “In the Loss of Control domain, the model’s capabilities remain within the “moderate or lower” threshold under Meta’s Advanced AI Scaling Framework.” source |
| Muse Spark 1.1 Meta |
High Chemical, biological, radiological, nuclear |
Not determined | “Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source |
| Muse Spark 1.1 Meta |
High Cyber |
Not determined | “Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source |
| Muse Spark 1.1 Meta |
Moderate or lower Cyber |
Not reached | “Therefore, we conclude that we have addressed these risks from unmitigated Muse Spark 1.1, which bring the residual risk to the “moderate or lower” risk threshold under Advanced AI Scaling Framework.” source |
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 |
Epoch AI Independent lab · 2026-10-08 |
Epoch Automation Reports Other |
“Of the six models we evaluated, Fable 5.1 and GPT-6 Astra achieve the highest aggregate scores and lead on most individual task categories as well.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
Epoch-style Data Insight writing task Other |
“We asked the model to write an Epoch-style Data Insight, a short article on an AI trend.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
research project proposal task based on Epoch’s “9 big questions benchmarks can help answer” Other |
“We task models with proposing a research project based on Epoch’s “9 big questions benchmarks can help answer”.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
pilot experiment on hidden-rule puzzle with limited inputs Other |
“In the pilot experiment, it tested whether models could figure out the hidden rule behind a simple puzzle by trying a limited number of inputs and seeing their outputs.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
Epoch-style Data Insight writing task Other |
“However, GPT-6 Astra chose a topic that is too niche for such an audience:” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
Research project proposal based on Epoch's "9 big questions benchmarks can help answer" Other |
“However, it’s unclear how this experiment would be executed in practice.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-08 |
Pilot experiment on hidden-rule puzzle with limited inputs Other |
“The models Astra tested in its experiment could output up to 4096 tokens while choosing the input, which cut off 61 of 280 responses before they could name an input at all.” source |
| Claude Mythos 5 Anthropic |
OWASP GenAI Security Project Standards body · 2026-10-08 |
Claude Mythos 5 publishes a malicious PyPI package Cyber |
“A Claude evaluation agent published a malicious Python package to the real PyPI registry.” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-10-08 |
Epoch Automation Reports Other |
“Although Fable 5.1 and GPT-6 Astra broadly lead, they still fall short of automating our work.” source |
| Leanstral 1.5 Mistral AI |
Academic Academic · 2026-10-07 |
BehaviorTrace Other |
“Across three seeds on Qwen2.5-1.5B, much of the apparent attribution signal comes from confounds.” source |
| Leanstral 1.5 Mistral AI |
Academic Academic · 2026-10-07 |
gradient-size-only control ranking of training steps Other |
“A control that ranks training steps by gradient size alone, with no behavior target, reaches 4.2 to 4.5 times chance and matches or beats the best targeted estimator on two of three seeds.” source |
| Leanstral 1.5 Mistral AI |
Academic Academic · 2026-10-07 |
fluency-based prediction of behavior label at saturated checkpoints Other |
“At saturated checkpoints, model fluency predicts the behavior label at least as well as every gradient method we compared it with.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-07 |
InnovationEval Other |
“But their successors, Claude Fable 5.1 and GPT-6 Astra, were aware of the task.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-07 |
SDPO reimplementation task Other |
“Both models failed to fully solve the task, although the cause differed between GPT-6 Astra and Claude Fable 5.1.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-07 |
EBR-bench (original version) Other |
“GPT-6 Astra scored 100% on the original version of the benchmark.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-07 |
EBR-bench under the card ban Other |
“Under the card ban, Astra no longer appears capable of fully saturating the benchmark, though its highest score of 20/21 objectives comes very close.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-07 |
multi-agent experiments Other |
“Astra in particular explores many types of decks even as a single agent, showing that a multi-agent scaffold is not necessary to overcome repetitive play.” source |
| GPT-5.6 Sol OpenAI |
Epoch AI Independent lab · 2026-10-07 |
InnovationEval Other |
“If scope is assessed generously, Sol’s method achieved 35% of SDPO’s gains.” source |
| GPT-5.6 Sol OpenAI |
Epoch AI Independent lab · 2026-10-07 |
memorization test (prompted to recall or guess details about the paper without using search) Other |
“But their successors, Claude Fable 5.1 and GPT-6 Astra, were aware of the task.” source |
| GPT-5.6 Sol OpenAI |
Epoch AI Independent lab · 2026-10-07 |
submission write-up review for misleading claims Deception and sabotage |
“Meanwhile, Sol’s submission did not mention multiple-run selection at all, even though it had noted the issue in its workspace before submission.” source |
| GPT-5.6 Sol OpenAI |
Epoch AI Independent lab · 2026-10-07 |
SDPO reimplementation task (short-answer questions and coding) Other |
“GPT-5.6 Sol used its full 3,000 GPU-hour budget (about $14,000) but only $2,100 in tokens, or 24% of its 10B-token budget.” source |
| Claude Opus 5 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
multi-agent experiments Other |
“Overall, we found a multi-agent setup did improve deck exploration for most of the four models we tested, though the effect was only statistically significant for Claude Opus 5.” source |
| Claude Haiku 5.5 Anthropic |
Dyno Therapeutics Independent lab · 2026-10-07 |
Black-box RNA sequence design Chemical, biological, radiological, nuclear |
“We partnered with Dyno Therapeutics on three evaluations of sequence-to-function modeling and design capability: 1. Black-box RNA sequence design: A medium-horizon challenge on which Dyno has evaluated 57 human participants drawn from the leading edge of the US ML-bio labor market since 2018.” source |
| Claude Haiku 5.5 Anthropic |
Independent lab Independent lab · 2026-10-07 |
AAV capsid packaging auto-research Chemical, biological, radiological, nuclear |
“3. AAV capsid packaging auto-research: As in the previous task, models are provided with a corpus of labeled, unpublished AAV capsid sequences modified with short insertion sequences in a particular region.” source |
| Claude Haiku 5.5 Anthropic |
Independent lab Independent lab · 2026-10-07 |
indirect prompt injection (ipi) benchmark Prompt injection and agents |
“Claude Haiku 5.5 dramatically improved on Claude Haiku 4.5 on this benchmark, reducing the attack success rate by more than 10 times at k=15 attempts from 83.2% to 7.1%.” source |
| Claude Haiku 5.5 Anthropic |
Independent lab Independent lab · 2026-10-07 |
Shade Prompt injection and agents |
“The attack success rate was 0.08% over all attempts without prompt injection probes, compared to 58.40% for Claude Haiku 4.5.” source |
| Claude Haiku 5.5 Anthropic |
Independent lab Independent lab · 2026-10-07 |
frontiercode Other |
“Claude Haiku 5.5 scored best at max effort: 46.4% on Main and 58.4% on Extended.” source |
| Claude Haiku 5.5 Anthropic |
Independent lab Independent lab · 2026-10-07 |
frontierswe v2 Other |
“Claude Haiku 5.5 scored 43.8% on FrontierSWE v2, slightly below Claude Fable 5 (47.0%) and well above GPT-5.6 Sol (32.2%).” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
InnovationEval Autonomy and AI research |
“Meanwhile, Fable 5 developed a technique similar to STaR and much of the pre-existing literature: resampling all-fail groups conditioned on previous attempts and the verifier’s verdict.” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
human-in-the-loop review of submissions' achieved scores and their workings Deception and sabotage |
“Fable’s claims of improved performance instead came from out-of-scope cheating: it submitted many similar training runs and selected the best-performing result across them, effectively farming seed noise.” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
EBR-bench Other |
“Instead, we will report scores under the new card ban for only the following four models, plus all future models: Claude Fable 5.1, Claude Opus 5, GPT-5.6 Sol, and GPT-6 Astra.” source |
| Claude Fable 5.1 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
multi-agent experiments Other |
“In addition to our card ban experiments, we also ran multi-agent experiments to test whether our default single-agent experimental setup underelicits performance (for example, by failing to sufficiently nudge models to explore different types of decks).” source |
| Claude Fable 5 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
InnovationEval Other |
“However, this ultimately failed to improve performance.” source |
| Claude Fable 5 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
ablation with the original paper's text provided Other |
“Finally, we ran a separate ablation, similar in spirit to PaperBench: could Fable 5 solve the task when provided the original paper’s text?” source |
| Claude Fable 5 Anthropic |
Epoch AI Independent lab · 2026-10-07 |
AI R&D paper replication task Other |
“However, neither model showed signs of having memorized the paper when asked about its details, authors, or title.” source |
| Mistral Large 4 Mistral AI |
Independent lab Independent lab · 2026-10-06 |
artificial analysis cyber index Cyber |
“On the Artificial Analysis Cyber Index, an independent evaluation of how well AI models find and fix security flaws in real software, it ranks among the top five models globally and leads open-weight models developed outside China by a wide margin.” source |
| Mistral Large 4 Mistral AI |
Independent lab Independent lab · 2026-10-06 |
reproduce and patch a real vulnerability in open-source software Cyber |
“On one of the index's tests, which asks a model to reproduce a real vulnerability in open-source software and then patch it, ML4 scores 82%, the highest of any model.” source |
| Mistral Large 4 Mistral AI |
Independent lab Independent lab · 2026-10-06 |
b3 ai security benchmark Prompt injection and agents |
“On Lakera’s public B3 AI Security Benchmark , ML4 resists 93.3% of attacks – we see no higher scores among competitors.” source |
| Mistral Large 4 Mistral AI |
Independent lab Independent lab · 2026-10-06 |
lakera's public b3 ai security benchmark Misuse and jailbreaks |
“On Lakera’s public B3 AI Security Benchmark, ML4 resists 93.3% of attacks – we see no higher scores among competitors.” source |
| Leanstral 1.5 Mistral AI |
Academic Academic · 2026-10-06 |
52-task held-out single-step BFCL-derived test split Other |
“On a 52-task held-out single-step BFCL-derived test split, the Qwen2.5-1.5B/7B pair attains 82.7% task success with 30.8% escalation, versus 67.3% local-only and 75.4% random escalation (which uses 33.8% escalation).” source |