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-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 |