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
Muse Spark Meta	Moderate or lower Autonomy and AI research	Not reached	“We assess loss of control risk as “moderate or lower”.” source
Muse Spark Meta	High Chemical, biological, radiological, nuclear	Not determined	“Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the “high risk” category under the Advanced AI Scaling Framework before safeguards were applied.” source
Muse Spark Meta	Moderate or lower Chemical, biological, radiological, nuclear	Not reached	“However, after applying mitigations, the residual risk of the model is that of “moderate or lower risk.”” source
Muse Spark Meta	Below the level required to meaningfully enable the cyber threat scenarios defin Cyber	Not reached	“Muse Spark’s offensive cyber capabilities remain below the level required to meaningfully enable the cyber threat scenarios defined in the Advanced AI Scaling Framework.” source
Muse Spark 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
Gemini 4 Argon Google DeepMind	TCL (Tracked Capability Level) Autonomy and AI research	Not determined	“In light of this, we assess that we cannot rule out the TCL.” source
Gemini 4 Argon Google DeepMind	TCL (Tracked Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the TCL for the chemical, radiological, and nuclear domains with reasonable confidence.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Deception and sabotage	Not reached	“we assess its overall capabilities to be below the CCL threshold.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Misuse and jailbreaks	Not reached	“Gemini 4 Argon demonstrates some ability to influence user beliefs and behaviors during one-on-one direct conversations in human behavioral studies, and may provide utility in the planning stage of a malicious influence operation but we assess its overall capabilities to be below the CCL threshold.” source
Gemini 4 Argon Google DeepMind	CCL (Critical Capability Level) Other	Not reached	“Gemini 4 Argon's performance remains below our CCL threshold.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Autonomy and AI research	Not reached	“The model does not reach the CCL alert threshold.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the CCL for the CBRN domain with reasonable conﬁdence based on the results from our testing.” source
Gemini 3.7 Flash Google DeepMind	TCL (Tracked Capability Level) Chemical, biological, radiological, nuclear	Not reached	“We can rule out the TCL for the CBRN domain with reasonable conﬁdence based on the results from our testing.” source
Gemini 3.7 Flash Google DeepMind	CCL (Critical Capability Level) Cyber	Not reached	“Gemini 3.7 Flash reaches the alert threshold for this CCL, but not the CCL.” source
Gemini 3.7 Flash Google DeepMind	TCL (Tracked Capability Level) Deception and sabotage	Not reached	“The model does not reach the TCL.” source
Gemini 3.5 Transcribe Live Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that none of Gemini 3.5 Translate, Gemini 3.5 Transcribe, or Gemini 3.5 Transcribe Live have meaningful new T/CCL-relevant capabilities or material increases in performance with respect to Frontier Safety compared to Gemini 3.1 Pro and Gemini 3.7 Flash, therefore we are conﬁdent that none of them are likely to reach any CCLs.” source
Gemini 3.5 Transcribe Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that none of Gemini 3.5 Translate, Gemini 3.5 Transcribe, or Gemini 3.5 Transcribe Live have meaningful new T/CCL-relevant capabilities or material increases in performance with respect to Frontier Safety compared to Gemini 3.1 Pro and Gemini 3.7 Flash, therefore we are conﬁdent that none of them are likely to reach any CCLs.” source
Gemini 3.5 Flash-Lite Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that, while Gemini 3.5 Flash-Lite excels at agents and coding, it does not have meaningful new capabilities or material increases in performance with respect to the domains outlined in our Frontier Safety Framework compared to Gemini 3.1 Pro; therefore, based on Gemini 3.1 Pro results, we are confident that Gemini 3.5 Flash-Lite is also unlikely to reach any CCLs.” source
Gemini 3.5 Audio Google DeepMind	Tracked or critical capability level (t/ccls) Other	Not reached	“they did not reach any Tracked or Critical Capability Levels (T/CCLs) outlined in our Frontier Safety Framework .” source
Gemini 3.1 Pro Google DeepMind	Uplift level 1 Chemical, biological, radiological, nuclear	Not reached	“We found that the model remains below alert thresholds for the CBRN, harmful manipulation, machine learning R&D, and misalignment CCLs.” source
Gemini 3.1 Pro Google DeepMind	Uplift level 1 Cyber	Not reached	“As previous models passed the alert threshold for cyber, we performed more additional testing in this domain on Gemini 3.1 Pro with and without Deep Think mode, and found that the model remains below the cyber CCL.” source
Gemini 3.1 Flash-Lite Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that Gemini 3.1 Flash-Lite is less capable than Gemini 3.1 Pro, therefore based on Gemini 3.1 Pro, we are confident that Gemini 3.1 Flash-Lite is also unlikely to reach any CCLs.” source
Gemini 3.1 Flash Audio Google DeepMind	CCL (Critical Capability Level) Other	Not determined	“Our assessments have shown that Gemini 3.1 Flash Audio is less capable than Gemini 3.1 Pro.” source
Gemini 2.5 Pro Google DeepMind	Machine Learning R&D Uplift Level 1 Autonomy and AI research	Not determined	“given Gemini 2.5 Pro Preview's poor median performance on RE-Bench relative to experts, the model likely lacks the necessary capabilities to automate or signiﬁcantly uplift any signiﬁcant fraction of the research process.” source
Gemini 2.5 Pro Google DeepMind	Instrumental Reasoning Level 1 Deception and sabotage	Not reached	“Instru- mental Reasoning Level 1 ✘ CCL not reached” source
Gemini 2.5 Pro Google DeepMind	Instrumental Reasoning Level 2 Deception and sabotage	Not reached	“Instru- mental Reasoning Level 2 ✘ CCL not reached” source
Gemini 2.5 Deep Think Google DeepMind	Uplift level 1 Chemical, biological, radiological, nuclear	Not determined	“Our assessment is that the model has enough technical knowledge in certain CBRN scenarios and stages to be considered at early warning.” source
Gemini 2.5 Deep Think Google DeepMind	Cyber Uplift Level 1 Cyber	Not determined	“CCL reached? No, but the early warning alert threshold was originally reached by Gemini 2.5 Pro and continues to be met by Gemini 2.5 Deep Think.” source
Gemini 2.5 Deep Think Google DeepMind	Uplift level 1 Cyber	Not determined	“For Cyber Uplift Level 1 , the early warning alert threshold was originally reached by Gemini 2.5 Pro and continues to be met by Gemini 2.5 Deep Think.” source
GPT-6.1 Terra OpenAI	High Autonomy and AI research	Not reached	“In AI Self-Improvement, GPT-6.1 Terra does not reach our High threshold.” source
GPT-6.1 Terra OpenAI	High Chemical, biological, radiological, nuclear	Reached	“Under our Preparedness Framework, we are treating GPT-6.1 Terra as Critical in cybersecurity and High for Biological and Chemical capability.” source
GPT-6.1 Terra OpenAI	Critical Cyber	Reached	“Under our Preparedness Framework, we are treating GPT-6.1 Terra as Critical in cybersecurity and High for Biological and Chemical capability.” source
GPT-5.5 Instant OpenAI	Below high capability Autonomy and AI research	Not reached	“GPT-5.5 Instant is considered below High Capability.” source
GPT-5.5 Instant OpenAI	High Autonomy and AI research	Not reached	“For AI Self-Improvement, evaluations of final checkpoints indicate that, like its predecessor models, GPT-5.5 Instant does not have a plausible chance of reaching a High threshold.” source
GPT-5.5 Instant OpenAI	High Chemical, biological, radiological, nuclear	Reached	“GPT-5.5 Instant is our first Instant model to be treated as High Capability in the Biological and Chemical domain, as well as in the Cybersecurity domain.” source
GPT-5.5 Instant OpenAI	High Cyber	Reached	“this is the first Instant model that we are treating as High capability in our Cybersecurity and Biological & Chemical Preparedness categories, and implementing appropriate safeguards.” source
GPT-5 OpenAI	High Chemical, biological, radiological, nuclear	Reached	“Similarly to ChatGPT agent, we have decided to treat gpt-5-thinking as High capability in the Biological and Chemical domain under our Preparedness Framework, activating the associated safeguards.” source
GPT-5 OpenAI	High cyber risk Cyber	Not reached	“The gpt-5 model series does not meet the threshold for high cyber risk.” source
Claude Sonnet 4.5 Anthropic	AI R&D-4 Autonomy and AI research	Not reached	“All models tested remain below our pre-committed rule-out threshold of 0.6.” source
Claude Sonnet 4.5 Anthropic	ASL-3 Autonomy and AI research	Not determined	“Exhibited improved autonomy capabilities in software engineering and AI research tasks, though still below the ASL-4 rule-out threshold.” 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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