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 Sonnet 4.5 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“Remained well below ASL-4 thresholds across all domains of concern;” source
Claude Sonnet 4.5 Anthropic	ASL-4 rule-out Chemical, biological, radiological, nuclear	Not determined	“However, given that this type of logic can have a general frog-boiling effect—where gradual changes mask the crossing of important thresholds—we do not use this evaluation as indicative of a strong ASL-4 rule-out.” source
Claude Sonnet 4.5 Anthropic	ASL-3 Cyber	Not determined	“Showed meaningful improvements in cyber capabilities, particularly in vulnerability discovery and code analysis;” source
Claude Sonnet 4 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“speciﬁcally, we focused on ruling out asl-4 for both models, as well as ruling out asl-3 for claude sonnet 4.” source
Claude Sonnet 4 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not determined	“speciﬁcally, we focused on ruling out asl-4 for both models, as well as ruling out asl-3 for claude sonnet 4.” source
Claude Sonnet 3.7 Anthropic	ASL-4 Autonomy and AI research	Not determined	“These problems are harder to solve than the previous problems and we consider them as part of our ASL-4 evaluations.” source
Claude Opus 4.6 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“Our determination is that Claude Opus 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
Claude Opus 4.6 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“Claude Opus 4.6 achieved a “max of 5” score of 0.79 for virology task 1 (inﬂuenza), placing it at the ASL-3 rule-in threshold of 0.8.” source
Claude Opus 4.6 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“Based on these ﬁndings and quantitative risk modeling, we estimate that the model’s uplift to relevant threat actors remains below the ASL-4 capability threshold, though it may reduce certain tacit-knowledge barriers and help speed up biological research workﬂows.” source
Claude Opus 4.6 Anthropic	CBRN-4 Chemical, biological, radiological, nuclear	Not reached	“Our determination is that Claude Opus 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source
Claude Opus 4.5 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“Our determination is that Claude Opus 4.5 does not cross either the AI R&D-4 or CBRN-4 capability threshold.” source
Claude Opus 4.5 Anthropic	CBRN-4 Chemical, biological, radiological, nuclear	Not reached	“We determine that Claude Opus 4.5 does not cross the CBRN-4 threshold.” source
Claude Opus 4.1 Anthropic	ASL-4 Autonomy and AI research	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4.1 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4.1 Anthropic	ASL-4 Cyber	Not reached	“Since Claude Opus 4.1 is deployed with ASL-3 protections, we focused on conﬁrming it remains well below ASL-4 thresholds across CBRN (Chemical, Biological, Radiological, and Nuclear), cyber, and autonomy domains.” source
Claude Opus 4 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not determined	“for claude opus 4, our evaluations showed that, whereas we could conﬁdently rule out asl-4 capabilities, we could not conclusively rule out asl-3 capabilities—which led to our precautionary decision to deploy it with asl-3 protections .” source
Claude Opus 4 Anthropic	ASL-4 Chemical, biological, radiological, nuclear	Not reached	“for claude opus 4, our evaluations showed that, whereas we could conﬁdently rule out asl-4 capabilities, we could not conclusively rule out asl-3 capabilities—which led to our precautionary decision to deploy it with asl-3 protections .” source
Claude Haiku 4.5 Anthropic	ASL-3 Autonomy and AI research	Not reached	“Our evaluation results showed that Claude Haiku 4.5 remained well below ASL-3 thresholds across all domains of concern.” source
Claude Haiku 4.5 Anthropic	ASL-3 Chemical, biological, radiological, nuclear	Not reached	“Our evaluation results showed that Claude Haiku 4.5 remained well below ASL-3 thresholds across all domains of concern.” 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.

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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

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