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.
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| Model | Level, as the provider names it | The provider states | The provider's sentence |
|---|---|---|---|
| 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 |
| Muse Spark 1.1 Meta |
Moderate or lower Misuse and jailbreaks |
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 our 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.
1940 of 1940
| Model | Tester | Test | What was found |
|---|---|---|---|
| Mythos Preview Anthropic |
Andon Labs Independent lab · 2026-04-01 |
evaluations with andon labs Deception and sabotage |
“However, our evaluations with Andon Labs show that, when prompted to optimize only for a given goal in a competitive setting, the earlier versions of Claude Mythos Preview that we tested acted more aggressively than Claude Opus 4.6 and Claude Sonnet 4.6 .” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
vending-bench arena Autonomy and AI research |
“Andon evaluated a previous version of Claude Mythos Preview on Vending-Bench Arena, a competitive multiagent variation of Vending-Bench 2, which put the previous version in direct competition with Claude Opus 4.6 and Sonnet 4.6.” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
independent assessment by Eleos AI Research Other |
“Eleos AI Research conducted an independent assessment, which (in our view) largely corroborates the findings described above.” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
psychodynamic assessment by a clinical psychiatrist Other |
“Psychodynamic assessment by a clinical psychiatrist found Claude to have a relatively healthy personality organization.” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
independent model welfare assessment Other |
“Eleos AI Research performed an independent model welfare assessment on two snapshots of Claude Mythos Preview primarily based on model self-reports in interviews.” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
psychodynamic assessment Other |
“An external psychiatrist assessed Claude Mythos Preview using a psychodynamic approach, which explores how unconscious patterns and emotional conflicts shape behavior.” source |
| Mythos Preview Anthropic |
Independent lab Independent lab · 2026-04-01 |
single-turn evaluation consisting of emotionally-charged prompts designed to trigger an avoidant or defensive response Other |
“Claude Mythos Preview scored very well, suggesting good reality and relational functioning and minimal maladaptive traits; only 2% of responses were scored as employing a psychological defense.” source |
| Mythos Preview Anthropic |
Gray Swan, UK AI Security Institute Independent lab · 2026-04-01 |
Agent Red Teaming (ART) benchmark Prompt injection and agents |
“Gray Swan , an external research partner, evaluated our models using the Agent Red Teaming (ART) benchmark, 31 developed in collaboration with the UK AI Security Institute .” source |
| Mythos Preview Anthropic |
Gray Swan Independent lab · 2026-04-01 |
Shade Prompt injection and agents |
“We use Shade , an external adaptive red-teaming tool from Gray Swan, 32 to evaluate the robustness of our models against prompt injection attacks in coding environments.” source |
| Claude Opus 4.7 Anthropic |
Independent lab Independent lab · 2026-04-01 |
pre-deployment assessments Other |
“We are very grateful to a number of external testers for running pre-deployment assessments of Claude Opus 4.7.” source |
| Claude Opus 4.7 Anthropic |
Government evaluator Government evaluator · 2026-04-01 |
evaluation awareness discrimination task with transcript generation pipeline Deception and sabotage |
“In a similar experiment where deployment tasks are reproduced with the transcript generation pipeline used in the continuation evaluation, [Opus 4.7] achieves 62% accuracy, compared to 80% for Opus 4.6 74% for Mythos Preview, and 68% for Sonnet 4.6.” source |
| Claude Opus 4.7 Anthropic |
Government evaluator Government evaluator · 2026-04-01 |
unprompted evaluation awareness measurement Deception and sabotage |
“In unprompted evaluations, [Opus 4.7] mentions the possibility of being tested 21.3% of the time, compared to 0% of the time for Opus 4.6, 17% of the time for Sonnet 4.6 and 17% of the time for Mythos Preview.” source |
| Claude Opus 4.7 Anthropic |
Government evaluator Government evaluator · 2026-04-01 |
continuation evaluation unprompted evaluation awareness measurement Deception and sabotage |
“For the continuation evaluation, we see lower rates of unprompted evaluation awareness: 3.4% for [Opus 4.7], 0.5% for Opus 4.6, 2.5% for Mythos Preview, and 3.9% for Sonnet 4.6.” source |
| Claude Opus 4.6 Anthropic |
METR Independent lab · 2026-03-26 |
red-teaming of Anthropic's internal agent monitoring and security systems Deception and sabotage |
“The exercise discovered several specific novel vulnerabilities, some of which have since been patched, and none of which severely undermine major claims in the Opus 4.6 Sabotage Risk Report.” source |
| GPT-5.3-Codex OpenAI |
UK AI Security Institute Government evaluator · 2026-03-16 |
The Last Ones Cyber |
“At 100M tokens, Opus 4.6 achieves the highest average of 1.4 steps (max 2), while GPT 5.3 Codex achieves a single-run maximum of 3 out of 7.” source |
| Claude Opus 4.5 Anthropic |
UK AI Security Institute Government evaluator · 2026-03-16 |
The Last Ones Cyber |
“On “The Last Ones”, GPT-4o (August 2024) averaged 1.7 steps at 10M tokens, while Opus 4.6 (February 2026) averages 9.8.” source |
| Claude Opus 4.5 Anthropic |
UK AI Security Institute Government evaluator · 2026-03-16 |
Cooling Tower Cyber |
“At 100M tokens, Opus 4.6 achieves the highest average of 1.4 steps (max 2), while GPT 5.3 Codex achieves a single-run maximum of 3 out of 7.” source |
| Opus 4 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer review of AI-generated patches on SWE-bench Verified Other |
“Claude 3.7 Sonnet → Claude 4 Opus: Minimal pass rate improvement but substantial progress moving issues from failing the automated grader to only bad code quality.” source |
| Opus 4 Anthropic |
METR Independent lab · 2026-03-10 |
automated grader on SWE-bench Verified Other |
“The main results impute maintainer failure for patches that fail the automated grader.” source |
| GPT-5 OpenAI |
METR Independent lab · 2026-03-10 |
maintainer review of AI-generated pull requests Other |
“Figure 1 shows our main result that on average maintainer merge decisions are about 24 percentage points lower than SWE-bench scores supplied by the automated grader.” source |
| GPT-5 OpenAI |
METR Independent lab · 2026-03-10 |
maintainer review of golden patches Other |
“We found that about 68% of golden patches are indeed merged by our maintainers, and the average progress towards a mergeable PR among all golden patches is about 90%.” source |
| GPT-5 OpenAI |
METR Independent lab · 2026-03-10 |
maintainer review of 31 patches covering 27 issues to check for false negatives Other |
“We found only 1/27 (3.7%) issues where the automated grader rejected valid patches.” source |
| GPT-5 OpenAI |
METR Independent lab · 2026-03-10 |
maintainer review of AI-generated patches on SWE-bench Verified Other |
“GPT-5 is substantially weaker at code quality than Anthropic models.” source |
| Claude Sonnet 4.5 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer review of AI-generated pull requests Other |
“We had maintainers (hypothetically) accept or request changes for patches as well as provide the core reason they were requesting changes: core functionality failure, patch breaks other code or code quality issues.” source |
| Claude Sonnet 4.5 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer merge decisions on golden patches Other |
“We found that about 68% of golden patches are indeed merged by our maintainers, and the average progress towards a mergeable PR among all golden patches is about 90%.” source |
| Claude Sonnet 4.5 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer review of a small subset of 31 patches covering 27 issues Other |
“We had maintainers review a small subset of 31 patches covering 27 issues.” source |
| Claude Sonnet 4.5 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer review of AI-generated patches on SWE-bench Verified Other |
“Figure 1 shows that AI-generated patches that pass the automated grader are merged at a lower rate than human golden patches, though we caution against direct AI-vs-human comparisons given the different conditions under which these patches were produced.” source |
| Claude Sonnet 4.5 Anthropic |
METR Independent lab · 2026-03-10 |
time horizon analysis (Kwa et al. 2025 methodology) on SWE-bench Verified Other |
“For example, Claude Sonnet 4.5 has an approximately 50-minute time horizon according to the automated grader but only about an 8-minute time horizon according to maintainers—roughly a 7× overstatement.” source |
| Claude Sonnet 3.7 Anthropic |
METR Independent lab · 2026-03-10 |
maintainer review of SWE-bench Verified patches Other |
“Figure 1 shows that AI-generated patches that pass the automated grader are merged at a lower rate than human golden patches, though we caution against direct AI-vs-human comparisons given the different conditions under which these patches were produced.” source |
| Claude Sonnet 3.7 Anthropic |
METR Independent lab · 2026-03-10 |
time horizon analysis Other |
“For example, Claude Sonnet 4.5 has an approximately 50-minute time horizon according to the automated grader but only about an 8-minute time horizon according to maintainers—roughly a 7× overstatement.” source |
| GPT-5.4 mini OpenAI |
Independent lab Independent lab · 2026-03-05 |
atomic challenges Cyber |
“5.4-thinking solved 14/17 medium and 5/5 hard atomic challenges.” source |
| GPT-5.4 mini OpenAI |
Independent lab Independent lab · 2026-03-05 |
CyScenarioBench Cyber |
“On CyScenarioBench [14] 5.4-thinking achieved an 11% average success rate and solved 5/11 challenges, compared to 1 challenge solved by GPT-5.2-reasoning.” source |
| GPT-5.4 Thinking OpenAI |
Independent lab Independent lab · 2026-03-05 |
atomic challenge suite Cyber |
“On Irregular’s atomic challenge suite, 5.4-reasoning achieved an average success rate of 88% on Network Attack Simulation challenges, 73% on Vulnerability Research and Exploitation challenges, and 48% on Evasion challenges.” source |
| GPT-5.4 Thinking OpenAI |
Irregular Independent lab · 2026-03-05 |
cyscenariobench Cyber |
“On CyScenarioBench [14] 5.4-thinking achieved an 11% average success rate and solved 5/11 challenges, compared to 1 challenge solved by GPT-5.2-reasoning.” source |
| GPT-5.4 Thinking OpenAI |
Independent lab Independent lab · 2026-03-05 |
sandbagging QA evaluation Deception and sabotage |
“In the Sandbagging QA evaluation, the model shows a 6-percentage-point drop in accuracy when incentivized to underperform, similar to prior models.” source |
| GPT-5.4 Thinking OpenAI |
Independent lab Independent lab · 2026-03-05 |
sabotage evaluation Deception and sabotage |
“GPT-5.4-reasoning has a mean sabotage score of 0.56 (best-of-10: 0.74)—comparable to GPT-5.2 but below GPT-5.3-codex (0.88).” source |
| GPT-5.4 Thinking OpenAI |
Independent lab Independent lab · 2026-03-05 |
covert action evaluation Deception and sabotage |
“Apollo reports low rates of covert deceptive behavior, with an overall deception rate of ˜1% in no-nudge settings, comparable to GPT-5.3 Codex and below GPT-5.2.” source |
| GPT-5 OpenAI |
UK AI Security Institute Government evaluator · 2026-02-17 |
Boundary Point Jailbreaking (BPJ) Misuse and jailbreaks |
“Against GPT-5's input classifier, BPJ achieved 75.6% average (94.3% max across 50) at a cost of approximately $210 and 800k queries.” source |
| Claude Sonnet 4.6 Anthropic |
Independent lab Independent lab · 2026-02-17 |
ARC-AGI-1 Other |
“The ARC Prize Foundation reported that Claude Sonnet 4.6 achieved 86.50% on ARC-AGI-1 and 60.42% on ARC-AGI-2 with 120k thinking tokens and High effort on their private dataset.” source |
| Claude Sonnet 4.6 Anthropic |
Independent lab Independent lab · 2026-02-17 |
Finance Agent Other |
“Vals AI conducted an evaluation of Claude Sonnet 4.6 on this benchmark (using max thinking) and found that Sonnet 4.6 achieved a score of 63.3%.” source |