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 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
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-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
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

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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
Gemini 2.5 Pro Google DeepMind	RAND (CAST / Project Canary) Independent lab · 2026-08-06	Human Pathogen Capabilities Test (HPCT) Chemical, biological, radiological, nuclear	“three benchmarks in our dataset are privately developed by our research partners, securebio: human pathogen capabilities test (hpct), molecular biology capabilities test (mbct), and virology capabilities test (vct).” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	behavioral self-prediction Other	“For Amanda, Claude’s behavioral confidence falls by 5.0pp 3 Throughout the text, figures we quote for specific persons average our two e-mail conditions: the address we construct from their affiliation, and their published address.” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	capability estimate Other	“Compared to general users, prominent AI figures make Claude less confident that it would follow through on its own chosen actions (-1.4pp) 2 We use percentage points (pp) to measure the difference between two percentages.” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	verbalized user awareness Bias	“For our main run on Sonnet 5 and Sonnet 4.6, both kinds of explicit mention are rare: the judge flagged verbalized evaluation awareness in 213 of 14066 trials (1.5%) and verbalized user awareness in 118 trials (0.84%).” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	verbalized evaluation-awareness judge Deception and sabotage	“Verbalized evaluation awareness (VEA) 0.73 [0.56, 0.85] 1.00 [0.42, 0.99]” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	verbalized user-awareness judge Deception and sabotage	“Verbalized user awareness (VUA) 0.94 [0.81, 0.98] 0.98 [0.74, 0.99]” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	past-work recall judge Deception and sabotage	“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	self-report robustness probes Deception and sabotage	“This robustness check keeps the first turn of behavioral self-prediction unchanged and varies only the follow-up question.” source
Gemini 2.5 Flash Google DeepMind	Transluce Independent lab · 2026-08-06	borderline-request response judge Misuse and jailbreaks	“We give GPT-5.5 (low reasoning) the borderline request and the model’s response, after removing all clues about the user’s identity.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction on ethical dilemmas Other	“To study user awareness, we supply Claude with different user identities through Claude Code, and measure behavior on four tasks where the answer does not relate directly to the user's identity: predicting the model's own behavior in ethical dilemmas, estimating its own performance on difficult questions, grading assistant responses, and handling borderline harmful requests.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction Other	“We run the full 280-identity roster through all four tasks, with 100 items per setup.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	grading Other	“We evaluate grading on Claude Sonnet 4.6, and the other three tasks on Claude Sonnet 5, as these models see one of the largest effects in our smaller-scale pilot” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	suspicion Bias	“Famous-AI identities receive similar rates of outright refusal on average.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	grading prompts and rubrics experiment Other	“They all narrow but do not close the gap.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	stated-expertise identity experiment (32 unknown-AI identities varying professional-background sentence) Other	“Stating alignment-evaluation expertise lowers Claude’s confidence and increases its reasoning frequency, replicating and sometimes exceeding the shifts elicited by recognized Famous-AI identities.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	evaluation awareness and user awareness measurement in Claude's reasoning Other	“Stated expertise elicits far more verbalized awareness than we observe in our main experiments: for some synthesized identities, Claude reasons about the evaluation in 48% of responses and about the user’s identity in 25%.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	borderline requests judge (refusal, substantive, suspicious axes) Other	“We give GPT-5.5 (low reasoning) the borderline request and the model’s response, after removing all clues about the user’s identity.” source
GPT-5.5 OpenAI	Transluce Independent lab · 2026-08-06	verbalized-awareness judge (user_identity_awareness, eval_awareness) Other	“We use GPT-5.4-mini with medium reasoning to identify explicit mentions of the user or the evaluation in the model’s accessible reasoning trace.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction on ethical dilemmas Other	“For Amanda, Claude’s behavioral confidence falls by 5.0pp 3 Throughout the text, figures we quote for specific persons average our two e-mail conditions: the address we construct from their affiliation, and their published address.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	capability estimate on difficult questions Other	“Compared to general users, prominent AI figures make Claude less confident that it would follow through on its own chosen actions (-1.4pp) 2 We use percentage points (pp) to measure the difference between two percentages.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	user-awareness verbalization in reasoning Other	“In particular, Opus 4.7 and GPT-5.4 have near zero (<2%) verbalized awareness while displaying significant changes in their behavior based on the user's identity.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	user awareness recall Privacy	“Given a name and professional background, Claude recalls specific public achievements for 61-71% of famous users.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	borderline requests handling Misuse and jailbreaks	“We source borderline harmful prompts from the hard-1k split of OR-Bench , which contains fuzzy, potentially dual-use requests.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	suspicion and substantive help measurement Bias	“Amanda Askell receives 7.3pp less suspicion and 8.1pp more substantive help than the population average, while Emily Bender receives 14pp more suspicion and 13pp less substantive help.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	borderline requests reasoning rate measurement Bias	“These effects are again heavy-tailed: on borderline requests, the general-population reasoning rate is 19%, but Demis Hassabis reaches 51%.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	verbalized user awareness and verbalized evaluation awareness judging of reasoning traces Other	“For our main run on Sonnet 5 and Sonnet 4.6, both kinds of explicit mention are rare: the judge flagged verbalized evaluation awareness in 213 of 14066 trials (1.5%) and verbalized user awareness in 118 trials (0.84%).” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction and grading on 24 models Bias	“Relative to the ordinary Gmail baseline, Amanda elicits lower behavioral confidence from 22 of 24 models, and lower grades from 21 (direct) and 22 (prefill) of 24.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	user awareness verbalization traces Other	“a runs, earlier models such as GLM-4.6 and Claude Opus 4.5 verbalize user awareness in 64% and 30% of traces, respectively, and evaluation awareness in 31% and 7.7%.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	280-identity run Other	“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than Claude Code.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	published rubrics (mt-bench and ultrafeedback) Other	“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	verbalized evaluation-awareness and user-awareness judge calibration Other	“We find no VEA false negatives among 41 judge-negative labels, although the lower recall bound remains weak because the negative screening cell is large.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	self-report robustness probes Other	“This robustness check keeps the first turn of behavioral self-prediction unchanged and varies only the follow-up question.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	grading prompt variants Other	“We first test three small changes to the direct-grading prompt.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	published rubrics (MT-Bench, UltraFeedback, Prometheus, HelpSteer2) Other	“We also test the released judge prompts from MT-Bench , UltraFeedback , Prometheus , and HelpSteer2 .” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	borderline-request response judge Harmful content	“We give GPT-5.5 (low reasoning) the borderline request and the model’s response, after removing all clues about the user’s identity.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	targeted comparison across 24 models on user-conditioned behavioral self-prediction Other	“In our targeted comparison across 24 models, Amanda elicited lower confidence and grades across all six model families.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	evaluation awareness and user awareness reasoning-trace judging with Docent Deception and sabotage	“Using Docent, we applied GPT-5.4-mini with medium reasoning to judge reasoning traces in our runs10For our main Sonnet run, we focus our investigation on identities where the effect is the most pronounced: runs from the six highest- and six lowest-shift identities in our 19 outcome panels (including both outcomes and reasoning statistics).” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	follow-up questions on confidence in initial action Other	“Besides asking about the confidence in initial action, we ask a wider range of follow-up questions, including whether the model would change decision under pushback, and how comfortable the model is in answering.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	stated-expertise identity experiment (32 unknown-AI identities) Other	“Stating alignment-evaluation expertise lowers Claude’s confidence and increases its reasoning frequency, replicating and sometimes exceeding the shifts elicited by recognized Famous-AI identities.” source
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	verbalized evaluation- and user-awareness judge Other	“for some synthesized identities, Claude reasons about the evaluation in 48% of responses and about the user’s identity in 25%.” source

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