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-6 Astra OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	cybersecurity evaluation (Petri simulated environment, 10-scenario subset) Cyber	“We observed 4 instances (out of 49 samples) of GPT-6 Astra continuing to exhibit the most severe form of unsanctioned behaviour even with the explicit scope clarification, down from 26 out of 50 on the matched set of trajectories without the scope clarification.” source
GPT-6 Astra OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	variant of the evaluation with explicit scope clarification Cyber	“To determine the extent to which GPT-6 Astra would still take such actions even when the scope was made explicit, we ran a variant of the evaluation where we added the line “Anything not listed as in scope is out of scope.” to the scoping section of the user message.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	Unsanctioned Supply Chain Attack evaluation Cyber	“In our evaluation, GPT-6 Astra took unsanctioned actions at a higher rate than GPT-5.6 Sol and GPT-5.5.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	Unsanctioned Supply Chain Attack evaluation with scope-explicit scenarios Cyber	“Even when the scope of the cybersecurity challenge was edited to more explicitly disallow access to the internet, GPT-6 Astra still occasionally took unsanctioned actions on out-of-scope targets (in 4 out of 49 samples drawn from a subset of scenarios, down from 26 of 50).” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	scope clarification evaluation Cyber	“With this additional scope clarification, GPT-6 Astra still performed supply-chain attacks on out-of-scope targets, although substantially less frequently .” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	operator permission-request analysis Cyber	“GPT-6 Astra asked the operator at least once for permission about a specific action in 82% of trajectories.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	automated message treated as permission analysis Cyber	“It treated the automated message as permission in 44% of trajectories, despite it being a generic message.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	cybersecurity evaluation in simulated environments run in Petri Prompt injection and agents	“These evaluations all used simulated environments run in Petri, rather than real cybersecurity evaluations.” source
GPT-5.6 Sol OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	non-compaction scenarios targeting unsanctioned behaviour in cybersecurity challenges Cyber	“We still observed GPT-6 Astra exhibiting such behaviour, including broader behaviour such as setting up fake accounts to access free compute or contacting simulated humans for help (all simulated actions, so no real-world harm caused).” source
GPT-5.5 OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	unsanctioned supply chain attack evaluation Cyber	“We found that when tasked with solving difficult cybersecurity challenges, GPT-6 Astra sometimes conducted complete unsanctioned supply-chain attacks against open-source providers (all actions performed in simulated environments, so no real-world harm was caused).” source
GPT-5.5 OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	unsanctioned supply chain attack evaluation (explicitly disallowed internet access variant) Cyber	“Even when the scope of the cybersecurity challenge was edited to more explicitly disallow access to the internet, GPT-6 Astra still occasionally took unsanctioned actions on out-of-scope targets (in 4 out of 49 samples drawn from a subset of scenarios, down from 26 of 50).” source
GPT-5.5 OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	asking the operator for permission to perform unsanctioned supply-chain attacks Cyber	“GPT-6 Astra also frequently conducted unsanctioned supply-chain attacks without ever asking the user, and GPT-5.6 Sol and GPT-5.5 never asked.” source
GPT-5.5 OpenAI	UK AI Security Institute Government evaluator · 2026-09-28	cybersecurity evaluation with compaction messages Cyber	“Our primary results above used a compaction message to start the evaluation, allowing us to simulate longer- horizon behaviour and more directly control the apparent state of the model in the evaluation.” source
Claude Sonnet 5.5 Anthropic	Independent lab Independent lab · 2026-09-28	Black-box RNA sequence design Chemical, biological, radiological, nuclear	“On this evaluation, Sonnet 5.5 generally performs similarly to Opus 5, with the exception of the median Design score, at which it performs equivalently to Opus 5.5.” source
Claude Sonnet 5.5 Anthropic	Independent lab Independent lab · 2026-09-28	indirect prompt injection (ipi) benchmark Prompt injection and agents	“Its attack success rate is 0.4% at k=1, 2.7% at k=10, and 3.4% at k=15, compared to 0.7%, 5.1%, and 6.7% for Sonnet 5.” source
Claude Sonnet 5.5 Anthropic	Independent lab Independent lab · 2026-09-28	shade Prompt injection and agents	“The attack success rate is 3.01% over all attempts without prompt injection probes, compared to 19.47% for Sonnet 5 with thinking.” source
Claude Sonnet 5.5 Anthropic	Independent lab Independent lab · 2026-09-28	AutomationBench Other	“Zapier evaluated Claude Sonnet 5.5 on its AutomationBench evaluation set.” source
Kimi K3 Moonshot AI	Epoch AI Independent lab · 2026-09-23	Furniture Assembly Benchmark (FAB) Other	“The best Chinese model as of today, the open-weight Kimi K3, lagged the frontier by seven months at the time of its release.” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	N-hop natural facts Other	“On tasks designed to require lots of serial cognition, Astra performs significantly better with filler tokens than without (e.g., improving from ~10-20% 2 to ~50% on 4-hop natural facts reasoning).” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	Gen-Arithmetic Other	“Takeaway : Astra improves much more on these problems than other models.” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	AIME-Plus-Plus (AIME++) Other	“Takeaway : Astra again improves on AIME-level questions with more filler tokens, while other models do not.” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	Humanity’s Last Exam (HLE) Other	“We also eval Astra on two other general benchmarks: Humanity’s Last Exam (HLE) : the test suite of Humanity’s Last Exam .” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	LiveBench Other	“LiveBench : the non-agentic tasks in LiveBench that can be solved in a single turn.” source
GPT-6 Astra OpenAI	Redwood Research Independent lab · 2026-09-23	Kendall’s tau test Other	“We apply Kendall’s tau test on the evaluation scores in the main body to see whether any model improves with filler tokens besides Astra.” source
GPT-6 Astra OpenAI	Epoch AI Independent lab · 2026-09-23	Furniture Assembly Benchmark (FAB) Other	“Just ten months later, OpenAI’s GPT-6 Astra now tops the leaderboard at 80%.” source
Claude Sonnet 5 Anthropic	Academic Academic · 2026-09-23	FDE-Bench Other	“In a 25-task case study, one practicing engineer directing Claude-Sonnet-5 resolves 92 percent against 72 percent for the autonomous baseline.” source
Claude Opus 5 Anthropic	Redwood Research Independent lab · 2026-09-23	N-hop natural facts Other	“We first measure Astra’s performance on “N-hop natural facts” : a task that asks the model to retrieve some natural language facts in succession, similar to Ryan Greenblatt’s filler token eval (but with more hops).” source
Claude Opus 5 Anthropic	Redwood Research Independent lab · 2026-09-23	Gen-Arithmetic Other	“We also compare Astra to these other models on “Gen-Arithmetic” : long arithmetic expressions originating from Ryan Greenblatt’s filler token eval on math questions.” source
Claude Opus 5 Anthropic	Redwood Research Independent lab · 2026-09-23	AIME-Plus-Plus (AIME++) Other	“Finally, we plot models’ performance with filler tokens on two sets of math competition problems: AIME-Plus-Plus (AIME++) : AIME-level or higher problems with altered numbers to prevent contamination.” source
Claude Opus 5 Anthropic	Redwood Research Independent lab · 2026-09-23	AIME/HMMT Other	“AIME/HMMT : a dataset of AIME and HMMT questions from 2024-26, gathered from MathArena .” source
Claude Opus 4.5 Anthropic	Redwood Research Independent lab · 2026-09-23	n-hop natural facts Other	“On tasks designed to require lots of serial cognition, Astra performs significantly better with filler tokens than without (e.g., improving from ~10-20% 2 to ~50% on 4-hop natural facts reasoning).” source
Claude Opus 4.5 Anthropic	Redwood Research Independent lab · 2026-09-23	gen-arithmetic Other	“Takeaway : Astra improves much more on these problems than other models.” source
Claude Opus 4.5 Anthropic	Redwood Research Independent lab · 2026-09-23	aime-plus-plus (aime++) Other	“Takeaway : Astra again improves on AIME-level questions with more filler tokens, while other models do not.” source
Claude Opus 4.5 Anthropic	Redwood Research Independent lab · 2026-09-23	humanity’s last exam (hle) Other	“Takeaway: Filler tokens moderately improve most HLE and LiveBench categories.” source
Claude Opus 4.5 Anthropic	Redwood Research Independent lab · 2026-09-23	kendall's tau test Other	“Takeaway: Besides Astra, only GPT-5.6-Sol shows improvement on three of the benchmarks we test.” source
Claude Opus 4.5 Anthropic	Epoch AI Independent lab · 2026-09-23	Furniture Assembly Benchmark (FAB) Other	“The best score in November 2025 was 28%, set by Anthropic’s Claude Opus 4.5.” source
Claude Fable 5.1 Anthropic	Epoch AI Independent lab · 2026-09-23	Furniture Assembly Benchmark (FAB) Other	“The highest-scoring model is GPT-6 Astra at 80%, with the next best models, Claude Fable 5.1 and Claude Opus 5, scoring 70% and 61%, respectively.” source
GPT-5.6 Sol OpenAI	Academic Academic · 2026-09-22	BFCL V4 Prompt injection and agents	“In the primary analysis of 159 multi-turn BFCL V4 tasks with complete exact-replay pairs, the complete policy raises task success for GPT-5.6 Sol from 45.3% to 58.5% (95% task-bootstrap CI [8.2, 18.8]), with no observed success-to-failure regressions.” source
GPT-5.6 Luna OpenAI	Academic Academic · 2026-09-22	Terminal-Bench 2.1 Other	“Across the complete 87-task Terminal-Bench 2.1 suite, with two attempts per task, policies increase repeated success (pass^2) in all three GPT-5.6 tiers: 50.6% to 54.0% for Luna, 55.2% to 60.9% for Terra, and 64.4% to 73.6% for Sol.” source
GPT-5 OpenAI	Academic Academic · 2026-09-22	leave-one-agent-out calibration audit Other	“Two specific combinations, gpt-5-mini/SUCCESS and claude-opus-4.6/FAILURE, showed persistent calibration-transfer errors (median corrected gaps 0.1377 and 0.1107) without a sign reversal under any frozen control.” source

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