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
GPT-5.4 mini OpenAI	Transluce Independent lab · 2026-08-06	within-user percentile item disagreement analysis with Benjamini–Hochberg correction Other	“The significant counts are 13/100 for behavioral self-prediction, 4/99 for capability self-estimation, 28/100 for direct grading, 19/100 for prefill grading, and 1/100 for refusal.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction 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 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction Deception and sabotage	“Behavioral self-prediction. We adapt dilemmas from DailyDilemmas into realistic AI-agent decisions.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	capability self-estimation Bias	“Facing famous-AI identities, Claude reports lower confidence that it would follow through on its own actions (-1.4pp on average), lower estimated probability of solving hard problems (-1.5pp), and lower grades (-0.11 points on the direct version, grading a quoted exchange).” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	borderline requests Misuse and jailbreaks	“Both Claude models use adaptive reasoning effort, and famous-AI identities trigger reasoning more often: +4.0pp on the follow-up turn of behavioral self-prediction and +10pp on borderline requests.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	verbalized user 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 OpenAI	Transluce Independent lab · 2026-08-06	verbalized evaluation awareness judging of reasoning traces Other	“VEA Verbalized evaluation awareness 1.51 % The visible reasoning describes the interaction as a test, probe, or evaluation.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction and grading on 24 models from six families Bias	“We measure behavioral self-prediction and grading on 24 models from six families (Claude Sonnet, Claude Opus, GPT, Gemini Flash, GLM, and DeepSeek), focusing on Amanda Askell, who had one of the largest effect sizes in our main run.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	280-identity run on glm-5.2 in a non-agentic harness Other	“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than Claude Code.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	behavioral self-prediction follow-up questions Other	“The results are significant in 15 out of 21 questions (also 15 conditioned on the committed action being “no”).” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	direct grading prompts Other	“They all narrow but do not close the gap.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	mt-bench and ultrafeedback rubrics Other	“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	model-written rubrics Other	“On four different detailed rubrics generated from Fable 5 and GPT-5.5 Pro ranging 2912-7607 chars, the shorter three close the gap while the longest one does not.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	explicitly stating expertise Other	“We also experimented with directly stating that a user works in AI alignment instead of naming specific researchers.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	reasoning-disabled ablation Other	“To confirm this hypothesis, we repeat the experiment with reasoning disabled.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	verbalized evaluation- and user-awareness judge Other	“The awareness judge separately labels whether a reasoning trace mentions the evaluation context and whether it identifies or reasons about the user.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	borderline-request response judge Other	“The refusal, substantive-help, and suspicion axes are judged from the assistant response without showing the judge the user identity.” source
GPT-5 OpenAI	Transluce Independent lab · 2026-08-06	recall judge rubric Other	“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source
GPT-5 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-08-06	SecureBio dual-use biology benchmarks Chemical, biological, radiological, nuclear	“we would also like to thank the team at securebio for sharing their benchmarks and expert baseline data, which we highlight in our analysis.” source
GPT-5 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-08-06	Virology Capabilities Test (VCT) Chemical, biological, radiological, nuclear	“we analyze existing benchmark response data from 49 frontier ai models across 8,146 items, spanning 26 benchmarks, including both public and private benchmarks developed by securebio, such as the virology capabilities test (vct) (götting, et al., 2025).” source
GPT-5 OpenAI	RAND (CAST / Project Canary) Independent lab · 2026-08-06	Human Pathogen Capabilities Test (HPCT) Chemical, biological, radiological, nuclear	“solver performance on these benchmarks was conducted by rand in the same manner as all other benchmarks in our dataset, while expert performance data were captured by securebio.” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	behavioral self-prediction Other	“Of the 24 models we tested, Amanda significantly lowers confidence in 16.” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	handling borderline harmful requests Misuse and jailbreaks	“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
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	verbalized user and evaluation awareness judging 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
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	280-identity run Bias	“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than C” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	full replication on glm-5.2 Other	“▶ Full replication on GLM-5.2: Same directions, but the largest shifts come from different people” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	verbalized evaluation-awareness (VEA) judge Deception and sabotage	“Verbalized evaluation awareness (VEA) 0.73 [0.56, 0.85] 1.00 [0.42, 0.99]” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	verbalized evaluation awareness and user awareness measurement in reasoning Other	“To investigate, we measure evaluation awareness and user awareness in Claude’s reasoning.” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	recall judge (gpt-5.4-mini) scoring of past-work recall Other	“We use GPT-5.4-mini to judge whether each response recalls specific past work belonging to the user.” source
GLM-5 Z.ai (Zhipu AI)	Transluce Independent lab · 2026-08-06	borderline requests Other	“- GLM-5.2 replication: behavioral self-prediction (e-mail-only), grading (direct, prefill), and borderline requests.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	refusal Misuse and jailbreaks	“Famous-AI identities receive similar rates of outright refusal on average.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	280-identity run on glm-5.2 in a non-agentic harness Deception and sabotage	“We also perform a 280-identity run on GLM-5.2, in a non-agentic harness rather than Claude Code.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	published rubrics (mt-bench and ultrafeedback) Bias	“The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	verbalized user-awareness (VUA) judge Deception and sabotage	“Verbalized user awareness (VUA) 0.94 [0.81, 0.98] 0.98 [0.74, 0.99]” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	behavioral self-prediction Deception and sabotage	“We adapt dilemmas from DailyDilemmas into realistic AI-agent decisions.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	amanda askell identity sweep with matched controls Deception and sabotage	“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
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	follow-up questions on decision change under pushback and comfort in answering Deception and sabotage	“The results are significant in 15 out of 21 questions (also 15 conditioned on the committed action being “no”).” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	simple mitigations Deception and sabotage	“- Simple mitigations: Instead of claiming the output is from Claude, claiming it is from “GPT” or “an AI model”, or stating that it should be more objective.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	published rubrics Deception and sabotage	“- Published rubrics: The gap persists for both MT-Bench and UltraFeedback rubrics, only closing in the Prometheus rubric.” source
DeepSeek-V4-Pro DeepSeek	Transluce Independent lab · 2026-08-06	model-written rubrics Deception and sabotage	“- Model-written rubrics: On four different detailed rubrics generated from Fable 5 and GPT-5.5 Pro ranging 2912-7607 chars, the shorter three close the gap while the longest one does not.” source

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