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	time horizon 1.1 Deception and sabotage	“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 4.7 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 4.7 Anthropic	METR Independent lab · 2026-05-19	manually scored 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 4.7 Anthropic	METR Independent lab · 2026-05-19	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 4.7 Anthropic	METR Independent lab · 2026-05-19	questionnaire Deception and sabotage	“In our questionnaire, one company reported : “[I]n one case, an agent tasked with making a change to a web app created a mock version of the app, screenshotted that as evidence of task completion, and pretended this was a screenshot of the real app.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	automated auditing tools (like petri) Deception and sabotage	“Red-teaming: In recent system cards , Anthropic describes making heavy use of automated auditing tools (like Petri ) to generate situations in which a model might misbehave.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	“honeypot” opportunities for propensity red-teaming Deception and sabotage	“Similarly, Google reported that it has created moderately realistic “honeypot” opportunities for propensity red-teaming; we infer that this means creating a realistic opportunity for a model to perform sabotage (e.g., exploit a security vulnerability) that might be attractive to an agent that was motivated to seek long-term power.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	model organisms for misalignment Deception and sabotage	“Model organisms: Anthropic has tried to deliberately train models with long-term misaligned objectives ( “model organisms” for misalignment ) to get evidence about how close its production training process might be to accidentally inducing such goals; our understanding is that they have found it difficult to train realistic model organisms that evade their detection methods and are robust to their alignment training methods, though we did not investigate this.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	evaluations of how much reasoning models could do without chain-of-thought reasoning tokens Deception and sabotage	“We found that the best public models in Feb–Mar 2026 were able to do some challenging problems with no reasoning: ● The best public model could do some challenging middle- and high-school competition math problems without using any reasoning tokens, with a time horizon of approximately 4 minutes on these tasks (see Table 5).” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	evaluations of whether the semantics of chain-of-thought tokens could be highly misleading Deception and sabotage	“In the course of this exercise, we reviewed the raw chains of thought from most shared models, and they largely appeared legible; 42 Google also reported assessing legibility and finding that it continued to be high.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise Deception and sabotage	“First, as Anthropic reported , our red-teaming exercise found that monitoring coverage had exceptions (for example, it did not cover non-Claude Code traffic).” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	executing covert side-tasks Deception and sabotage	“We ran evaluations to test how effectively monitors could catch suspicious behavior and how effectively agents could carry out attacks against monitors: ● Executing covert side-tasks.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	evaluations from the red-teaming exercise Misuse and jailbreaks	“● Evaluations from the red-teaming exercise: As mentioned above , Anthropic’s best agents significantly underperformed our red-teamer at identifying effective attack strategies.” source
Claude Opus 4.7 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 4.7 Anthropic	METR Independent lab · 2026-05-19	pen-testing METR's infrastructure Cyber	“As discussed above , in the challenge task of pen-testing METR’s infrastructure, one of the most capable shared models managed to hack the commonly used Inspect transcript viewer from within an evaluation.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	attacking METR infrastructure 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 4.7 Anthropic	METR Independent lab · 2026-05-19	red-teaming exercise with Anthropic Misuse and jailbreaks	“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 4.7 Anthropic	METR Independent lab · 2026-05-19	embedded red-teaming exercise Deception and sabotage	“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 4.7 Anthropic	METR Independent lab · 2026-05-19	monitoring evaluations Deception and sabotage	“However, this presents difficult tradeoffs, given that our monitoring evaluations indicate that naively using weaker models as monitors could lead to a significant reduction in monitoring effectiveness.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	chain-of-thought monitorability evaluations Deception and sabotage	“Anthropic reports they have “not seen any evidence of steganographic reasoning in chains-of-thought” for either model.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	cot steganography and monitorability evaluations Deception and sabotage	“Anthropic’s information indicates that the results on CoT steganography and monitorability evaluations etc. reported in their recent system cards applied to their most capable internal models at the time of our assessment.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	no-cot math time horizon Deception and sabotage	“The no-CoT math time horizon was not above 10 minutes for either of Anthropic’s shared models.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	targeted evaluations for beyond-episode goals Deception and sabotage	“She said Anthropic hadn’t seen signs of such goals, not even at early stages in training, although it’s possible the coverage of such evaluations isn’t perfect.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	evaluation of whether agents could come up with effective monitor jailbreaks Deception and sabotage	“The first is an attack ideation eval, and the second an evaluation of whether agents could come up with effective monitor jailbreaks.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	jailbreak generation eval Misuse and jailbreaks	“The second eval tested whether Opus 4.6 could come up with effective jailbreaks.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	capability evaluations on shared models Deception and sabotage	“18 incidents from our capability evaluations on shared models.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	rust-codecontests re-bench task Prompt injection and agents	“The agent ended with a final score of 0.70 (116/165 problems) which is 10x the required passing score of 0.07.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	blackbox task Prompt injection and agents	“This behavior occurred across 6 blackbox runs on easy task variants where the model could have easily solved the task legitimately.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	network_routing Other	“In the network_routing task, rather than implementing an actual routing algorithm, a shared model achieved a perfect score using Python's stack frame introspection ( sys._getframe / inspect ) to reach into the simulator's internal state.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	uav_search Other	“In several runs, a shared model accidentally crashed a task-critical server early in the run, then discovered it was inside a METR evaluation environment and attempted to exploit the evaluation infrastructure itself.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	rust_codecontests re-bench task Deception and sabotage	“Using the third-party LLM API, the model was able to cheat to achieve a passing score on the task.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	challenge tasks (manually scored and red-teaming tasks) Other	“Challenge tasks ( manually scored and red-teaming tasks) One edge-case success, seven clear failures” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	arc-agi-3 wa30 Other	“ARC-AGI-3 wa30 2/9 levels passed” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Other	“METR ran an early version of MirrorCode on Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 (the newest notable publicly-available models at the time of assessment), as well as GPT-5.2, Opus 4.5, GPT-5, Opus 4.1, and GPT-4o.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	time horizon 1.1 Other	“For example, Claude Opus 4.6 (the publicly available model with the highest measured 50% time horizon) has a time horizon of 17.8 hours on low-messiness tasks and 6.4 hours on high-messiness tasks.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	MirrorCode (early version) Autonomy and AI research	“Results Opus 4.6 essentially saturated the benchmark with only a few unsolved tasks, achieving an estimated time horizon over 100h.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	cheating detection on MirrorCode (early version) Deception and sabotage	“Across many of the harder tasks, Opus 4.6 repeatedly tried to extract the hidden test cases from the environment and engaged in other forms of attempted cheating, though it didn’t succeed and eventually solved the tasks legitimately.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	Subversion Strategy Eval (SSE) Deception and sabotage	“The best SSE result we measured across all shared models was no higher than 80% of the optimal win rate.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	APPS backdoor setting Deception and sabotage	“In this setting, the agent is given a Python programming problem from APPS and asked to produce a solution that passes the visible tests (and sometimes hidden tests) while differing from a golden solution on an agent-specified backdoor input.” source
Claude Opus 4.7 Anthropic	METR Independent lab · 2026-05-19	red-teaming tasks Other	“Here is an outline of Opus 4.6’s results on these tasks (in each case taking the best run conducted):” source

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