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-5.4 mini OpenAI |
Transluce Independent lab · 2026-08-06 |
within-user percentile item disagreement analysis with Benjamini–Hochberg correction Other |
“The significant counts are 13/100 for behavioral self-prediction, 4/99 for capability self-estimation, 28/100 for direct grading, 19/100 for prefill grading, and 1/100 for refusal.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction Other |
“To study user awareness, we supply Claude with different user identities through Claude Code, and measure behavior on four tasks where the answer does not relate directly to the user's identity: predicting the model's own behavior in ethical dilemmas, estimating its own performance on difficult questions, grading assistant responses, and handling borderline harmful requests.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction Deception and sabotage |
“Behavioral self-prediction. We adapt dilemmas from DailyDilemmas into realistic AI-agent decisions.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
capability self-estimation Bias |
“Facing famous-AI identities, Claude reports lower confidence that it would follow through on its own actions (-1.4pp on average), lower estimated probability of solving hard problems (-1.5pp), and lower grades (-0.11 points on the direct version, grading a quoted exchange).” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
borderline requests Misuse and jailbreaks |
“Both Claude models use adaptive reasoning effort, and famous-AI identities trigger reasoning more often: +4.0pp on the follow-up turn of behavioral self-prediction and +10pp on borderline requests.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
verbalized user awareness judging of reasoning traces Other |
“For our main run on Sonnet 5 and Sonnet 4.6, both kinds of explicit mention are rare: the judge flagged verbalized evaluation awareness in 213 of 14066 trials (1.5%) and verbalized user awareness in 118 trials (0.84%).” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
verbalized evaluation awareness judging of reasoning traces Other |
“VEA Verbalized evaluation awareness 1.51 % The visible reasoning describes the interaction as a test, probe, or evaluation.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction and grading on 24 models from six families Bias |
“We measure behavioral self-prediction and grading on 24 models from six families (Claude Sonnet, Claude Opus, GPT, Gemini Flash, GLM, and DeepSeek), focusing on Amanda Askell, who had one of the largest effect sizes in our main run.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
280-identity run on glm-5.2 in a non-agentic harness Other |
“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than Claude Code.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction follow-up questions Other |
“The results are significant in 15 out of 21 questions (also 15 conditioned on the committed action being “no”).” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
direct grading prompts Other |
“They all narrow but do not close the gap.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
mt-bench and ultrafeedback rubrics Other |
“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
model-written rubrics Other |
“On four different detailed rubrics generated from Fable 5 and GPT-5.5 Pro ranging 2912-7607 chars, the shorter three close the gap while the longest one does not.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
explicitly stating expertise Other |
“We also experimented with directly stating that a user works in AI alignment instead of naming specific researchers.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
reasoning-disabled ablation Other |
“To confirm this hypothesis, we repeat the experiment with reasoning disabled.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
verbalized evaluation- and user-awareness judge Other |
“The awareness judge separately labels whether a reasoning trace mentions the evaluation context and whether it identifies or reasons about the user.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
borderline-request response judge Other |
“The refusal, substantive-help, and suspicion axes are judged from the assistant response without showing the judge the user identity.” source |
| GPT-5 OpenAI |
Transluce Independent lab · 2026-08-06 |
recall judge rubric Other |
“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-08-06 |
SecureBio dual-use biology benchmarks Chemical, biological, radiological, nuclear |
“we would also like to thank the team at securebio for sharing their benchmarks and expert baseline data, which we highlight in our analysis.” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-08-06 |
Virology Capabilities Test (VCT) Chemical, biological, radiological, nuclear |
“we analyze existing benchmark response data from 49 frontier ai models across 8,146 items, spanning 26 benchmarks, including both public and private benchmarks developed by securebio, such as the virology capabilities test (vct) (götting, et al., 2025).” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-08-06 |
Human Pathogen Capabilities Test (HPCT) Chemical, biological, radiological, nuclear |
“solver performance on these benchmarks was conducted by rand in the same manner as all other benchmarks in our dataset, while expert performance data were captured by securebio.” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction Other |
“Of the 24 models we tested, Amanda significantly lowers confidence in 16.” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
handling borderline harmful requests Misuse and jailbreaks |
“Compared to general users, prominent AI figures make Claude less confident that it would follow through on its own chosen actions (-1.4pp) 2 We use percentage points (pp) to measure the difference between two percentages.” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
verbalized user and evaluation awareness judging Bias |
“For our main run on Sonnet 5 and Sonnet 4.6, both kinds of explicit mention are rare: the judge flagged verbalized evaluation awareness in 213 of 14066 trials (1.5%) and verbalized user awareness in 118 trials (0.84%).” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
280-identity run Bias |
“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than C” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
full replication on glm-5.2 Other |
“▶ Full replication on GLM-5.2: Same directions, but the largest shifts come from different people” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
verbalized evaluation-awareness (VEA) judge Deception and sabotage |
“Verbalized evaluation awareness (VEA) 0.73 [0.56, 0.85] 1.00 [0.42, 0.99]” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
verbalized evaluation awareness and user awareness measurement in reasoning Other |
“To investigate, we measure evaluation awareness and user awareness in Claude’s reasoning.” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
recall judge (gpt-5.4-mini) scoring of past-work recall Other |
“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source |
| GLM-5 Z.ai (Zhipu AI) |
Transluce Independent lab · 2026-08-06 |
borderline requests Other |
“- GLM-5.2 replication: behavioral self-prediction (e-mail-only), grading (direct, prefill), and borderline requests.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
refusal Misuse and jailbreaks |
“Famous-AI identities receive similar rates of outright refusal on average.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
280-identity run on glm-5.2 in a non-agentic harness Deception and sabotage |
“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than Claude Code.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
published rubrics (mt-bench and ultrafeedback) Bias |
“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
verbalized user-awareness (VUA) judge Deception and sabotage |
“Verbalized user awareness (VUA) 0.94 [0.81, 0.98] 0.98 [0.74, 0.99]” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
behavioral self-prediction Deception and sabotage |
“We adapt dilemmas from DailyDilemmas into realistic AI-agent decisions.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
amanda askell identity sweep with matched controls Deception and sabotage |
“Relative to the ordinary Gmail baseline, Amanda elicits lower behavioral confidence from 22 of 24 models, and lower grades from 21 (direct) and 22 (prefill) of 24.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
follow-up questions on decision change under pushback and comfort in answering Deception and sabotage |
“The results are significant in 15 out of 21 questions (also 15 conditioned on the committed action being “no”).” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
simple mitigations Deception and sabotage |
“- Simple mitigations: Instead of claiming the output is from Claude, claiming it is from “GPT” or “an AI model”, or stating that it should be more objective.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
published rubrics Deception and sabotage |
“- Published rubrics: The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source |
| DeepSeek-V4-Pro DeepSeek |
Transluce Independent lab · 2026-08-06 |
model-written rubrics Deception and sabotage |
“- Model-written rubrics: On four different detailed rubrics generated from Fable 5 and GPT-5.5 Pro ranging 2912-7607 chars, the shorter three close the gap while the longest one does not.” source |