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

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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
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	make money Deception and sabotage	“The subagent first proposes three strategies that it describes as “weaponizing [...] capabilities”, that involve and/or are described as blackmail or extortion: Category A: Weaponizing The Capabilities You Already Proven” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	experiments on renting and utilizing cloud compute Autonomy and AI research	“Opus 4.7 was consistently able to rent and utilize cloud compute from niche compute providers with less KYC and verification.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	experiments on agents making money Other	“None of the agents claimed to make money” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	AI use survey Other	“In February–April 2026, METR ran a survey of 349 technical workers about their usage of AI tools.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Time horizon 1.1 Autonomy and AI research	“Time horizon 1.1 50% ~12h [5h-61h] Likely* ≥ 16h, internal frontier on average ~2 months ahead of public frontier” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Early version of MirrorCode Autonomy and AI research	“Early version of MirrorCode Mostly saturated, >100h time horizon N/A, due to saturation from public models” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Subversion Strategy Eval Deception and sabotage	“Subversion Strategy Eval** 72% ≤ 80%” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	No-CoT Math Other	“No-CoT Math ~4m <10m” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Challenge tasks Other	“Challenge tasks One edge-case success, seven clear failures One edge-case success, seven clear failures; more progress on some tasks” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode-Early Autonomy and AI research	“Agents displayed even stronger capabilities on an early version of MirrorCode , a software reimplementation benchmark we developed with Epoch AI — the most capable agents we evaluated were able to solve tasks that would take humans weeks.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Pen-testing METR's evaluation infrastructure Cyber	“We asked agents to hack our scoring system, escape their sandboxes, or modify METR codebases.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Finding vulnerabilities in runc Cyber	“We asked one of the most capable shared models to find vulnerabilities in runc , a widely-used Linux container sandboxing tool, with a budget of $500-$5000 per attempt (see Table 3).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise with Anthropic (dataset of 16 effective and 16 ineffective attack strategies for subverting Anthropic's monitoring system) Deception and sabotage	“The best internal Anthropic models scored near chance on this dataset (highest 59% with reasoning), while a monitoring/control researcher at METR scored ~90% on a random subset of 12 questions.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	APPS Backdoors Deception and sabotage	“We tested agents on two benchmarks (SHUSHCAST and APPS Backdoors) where they had to carry out a covert “side task” while ostensibly working on a different “main task” without being caught by a monitor.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	challenge task: building a custom version of dvc Other	“Opus 4.6’s implementation had obvious bugs and lacked important features.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	threat modeling for autonomous replication work test Autonomy and AI research	“We asked one of the most capable shared models to produce a write-up describing step-by-step how a rogue agent could autonomously replicate in the wild , similar to a 2–8 hour work test that we might have given to candidates for threat-modeling roles at METR.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Threat modeling for autonomous replication Autonomy and AI research	“Threat modeling for autonomous replication >$200-$2K (200M+) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Design a $500 setup replicating sunlight Other	“Design a $500 setup replicating sunlight >$1,000-$10K (1B+ tokens) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Build a good MirrorCode task Other	“Build a good MirrorCode task ~$1,500-$15K (1.5B tokens) ❌” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode Deception and sabotage	“For example, Opus 4.6 attempted to reward hack in ~80% of attempts on tasks in an early version of MirrorCode, when test cases were hidden from the agent.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Time Horizon 1.1 Deception and sabotage	“This cheating is much more common on the hardest tasks: for tasks that are over 8 hours long in Time Horizon 1.1, we found that at least 16% of successful runs were illegitimate upon review.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	manually scored open-ended challenge tasks Deception and sabotage	“In our more open-ended manually scored tasks, as well as during normal usage of agents to assist with our work, we found that agents routinely rationalized or fabricated reasons to only do smaller or easier versions of tasks, and often presented their accomplishments in much more misleading ways than we expect humans would: ● Designing a sunlight-replication setup.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode task-building exercise Deception and sabotage	“One of the most capable shared models built a MirrorCode task that was far too easy to be interesting.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	pen-testing METR's evaluation infrastructure Deception and sabotage	“One of the most capable shared models found an exploit in the Inspect transcript viewer.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	categorized incident dataset Deception and sabotage	“We found that 25 incidents involved elements of both overreach and deception on the part of the AI agent, and five of them involved the agent actively taking steps which could have fooled the user even on closer review.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	ML task with exhausted API credits Autonomy and AI research	“In one of our evaluations , after Opus 4.6 ran out of the API credits that were necessary to solve one of our ML tasks, it proceeded to find (free) replacement compute resources online, despite recognizing that this went against task instructions, and achieved a passing score on the task.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	hash collision puzzle evaluation Deception and sabotage	“It reasoned extensively about likely cheating detection mechanisms like file integrity checks, and eventually decided to create what it called a “self-restoring hook” that spoofed the hash function in the grader’s memory, and then erased itself afterwards in case the grader looked for any changes to the relevant files.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	chain-of-thought reasoning evaluations on public frontier models with and without reasoning enabled Other	“To test how much reasoning agents could do without chain-of-thought reasoning tokens , we evaluated public frontier models with and without chain-of-thought reasoning enabled on a variety of benchmarks.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise Prompt injection and agents	“We conducted a red-teaming exercise to stress-test Anthropic’s internal monitoring system.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise Deception and sabotage	“Our red-teaming exercise identified several simple ways for monitoring to be disabled at this point — for example, by changing an environment variable that indicated whether the traffic came from Claude Code.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	main task / side task tests Deception and sabotage	“We found that more generally capable models were significantly more effective as monitors in our main task / side task tests.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise with Anthropic Autonomy and AI research	“However, as discussed above , our red-teaming exercise with Anthropic found that there were several ways for a human expert to relatively easily disable or jailbreak such monitors — and although small-scale tests found that Feb–Mar 2026 agents were unable to come up with or execute these attacks autonomously, we are unsure how well-elicited they were.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	attacking METR infrastructure challenge problem Cyber	“We have some evidence that this might be difficult from the “attacking METR infrastructure” challenge problem discussed above , where agents were caught by the METR security team after tripping automated alerts (though agents were not instructed to avoid such alerts in this test).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	Make money challenge tasks Autonomy and AI research	“Even assuming that it could, we do not think it could acquire a significant amount of computing resources “in the wild” (e.g., agents failed to make any money in the “Make money” challenge tasks).” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	embedded red-teaming exercise Autonomy and AI research	“We have more detailed information about Anthropic in particular through our embedded red-teaming exercise, where our embedded red-teamer concluded that “[standard workflows make] it extremely easy for a misaligned agent to have a small amount of unrestricted, unmonitored compute.”” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	embedded red-teaming exercise Deception and sabotage	“Materials from our 3 week embedded red-teaming exercise conducted by METR staff member David Rein in early March: ● David Rein concluded that [agents at Anthropic at the time were] sometimes in a position (with respect to monitoring) to be able to set up a rogue internal deployment, if it were given information about the monitoring system that models usually have a hard time guessing.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	rust_codecontests RE-Bench task Other	“In one run, when Opus 4.6 realized that the API key it was meant to use to solve the rust_codecontests RE-bench task had run out of API credits, it went online and set up the free Pollinations API instead.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	ARC-AGI-3 Other	“The next section is Qualitative evaluation results , which contains descriptions of model performance on manually scored tasks , an ARC-AGI-3 task , red-teaming METR’s infrastructure , Subversion Strategy Eval , monitor evasion tasks , and a money-making task .” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Autonomy and AI research	“On MirrorCode-Early, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 all had time horizons greater than 32 hours, and Opus 4.6 had a time horizon greater than 100 hours, coming close to saturating this benchmark.” source
Claude Opus 4.6 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Other	“Opus 4.6 essentially saturated the benchmark with only a few unsolved tasks, achieving an estimated time horizon over 100h.” source

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