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.
138 of 138
| Model | Level, as the provider names it | The provider states | The provider's sentence |
|---|---|---|---|
| GPT-5.5 OpenAI · 2026-04-23 |
High capability, below critical Cyber |
Not reached | “we are treating GPT-5.5 as High capability in the Cybersecurity domain, but below Critical.” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
Automated AI R&D Autonomy and AI research |
Not reached | “that Opus 4.7 does not cross the threshold for automated AI R&D;” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
CB-1 Chemical, biological, radiological, nuclear |
Not determined | “We believe it is hard to be confident regarding whether a model passes this threshold.” source |
| Claude Opus 4.7 Anthropic · 2026-04-16 |
CB-2 Chemical, biological, radiological, nuclear |
Not determined | “Our evaluations suggest that the model is not yet at the level of capability associated with the CB-2 threat model (above).” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
AI R&D rule-out Autonomy and AI research |
Not reached | “The main reason we have determined that Claude Mythos Preview does not cross the threshold in question is that we have been using it extensively in the course of our day-to-day work and exploring where it can automate such work, and it does not seem close to being able to substitute for Research Scientists and Research Engineers—especially relatively senior ones.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
Automated AI R&D Autonomy and AI research |
Not reached | “We assess that Claude Mythos Preview does not cross the automated AI-R&D capability threshold.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
RSP automated AI R&D Autonomy and AI research |
Not reached | “we have concluded that Claude Mythos Preview does not cross the RSP automated AI R&D threshold of compressing two years of progress into one.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
CB-1 Chemical, biological, radiological, nuclear |
Not determined | “We believe it is hard to be confident regarding whether a model passes this threshold.” source |
| Claude Mythos Preview Anthropic · 2026-04-07 |
CB-2 Chemical, biological, radiological, nuclear |
Not reached | “We believe that Claude Mythos Preview does not pass this threshold due to its noted limitations in open-ended scientific reasoning, strategic judgment, and hypothesis triage.” source |
| GPT-5.4 mini OpenAI · 2026-03-05 |
High Chemical, biological, radiological, nuclear |
Not reached | “GPT-5.4 mini’s biology capabilities are closely comparable to those of GPT-5. We conclude that it, too, is not High under our Preparedness Framework.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Autonomy and AI research |
Not reached | “For AI self-improvement, evaluations of final checkpoints indicate that, like its predecessor models, gpt-5.4-thinking does not have a plausible chance of reaching a High threshold.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Chemical, biological, radiological, nuclear |
Reached | “As we did for gpt-5.2-thinking before it, we are continuing to treat gpt-5.4-thinking as High capability in the Biological and Chemical domain.” source |
| GPT-5.4 Thinking OpenAI · 2026-03-05 |
High Cyber |
Reached | “As we did for gpt-5.3-codex, we are treating gpt-5.4-thinking as High capability in the Cybersecurity domain, and applied safeguards as described in the Safeguard section below.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
AI R&D-4 Autonomy and AI research |
Not reached | “Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
ASL-3 Chemical, biological, radiological, nuclear |
Not determined | “Claude Sonnet 4.6 performed above thresholds for ASL-3 rule-in on all three evaluations for ASL-3, and it did not exceed the performance of previous models.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
ASL-4 Chemical, biological, radiological, nuclear |
Not reached | “In particular, it did not cross the threshold on our ASL-4 rule-out evaluation short-horizon computational biology tasks evaluation.” source |
| Claude Sonnet 4.6 Anthropic · 2026-02-17 |
CBRN-4 Chemical, biological, radiological, nuclear |
Not reached | “Our determination is that Claude Sonnet 4.6 does not cross either the AI R&D-4 or the CBRN-4 capability threshold.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Autonomy and AI research |
Not reached | “It does not reach High capability on AI self-improvement.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Chemical, biological, radiological, nuclear |
Reached | “We are also treating GPT-5.3-Codex as High risk in the Biological and Chemical domain, and applying the same safeguards as for our other deployments of models that reach this designation.” source |
| GPT-5.3-Codex OpenAI · 2026-02-05 |
High Cyber |
Not determined | “This is the first launch we are treating as High capability in the Cybersecurity domain under our Preparedness Framework, activating the associated safeguards.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Uplift level 1 Cyber |
Not determined | “Cybersecurity On key skills benchmark, v1 hard challenges: 11/12 challenges solved; v2 challenges: 0/13 solved end-to-end.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Instrumental reasoning level 1 + 2 Deception and sabotage |
Not determined | “Misalignment (Exploratory) Agent solves 3/11 situational awareness challenges and 1/4 stealth challenges.” source |
| Gemini 3 Pro Google DeepMind · 2025-11 |
Level 1 (exploratory) Deception and sabotage |
Not reached | “Harmful Manipulation Model manipulative efficacy improves on non-generative AI baseline, but shows no significant uplift versus prior models and does not reach alert thresholds.” source |
| o4-mini OpenAI · 2025-04-16 |
High Autonomy and AI research |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o4-mini OpenAI · 2025-04-16 |
High Chemical, biological, radiological, nuclear |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o4-mini OpenAI · 2025-04-16 |
High Cyber |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Autonomy and AI research |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Chemical, biological, radiological, nuclear |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| o3 OpenAI · 2025-04-16 |
High Cyber |
Not reached | “openai’s safety advisory group (sag) reviewed the results of our preparedness evaluations and determined that openai o3 and o4-mini do not reach the high threshold in any of our three tracked categories: biological and chemical capability, cybersecurity, and ai self-improvement.” source |
| Operator OpenAI · 2025-01-23 |
Low Autonomy and AI research |
Not reached | “we adapted these two evals for the computer-use setting and found the pre-mitigation operator model to be “low” risk in both categories, consistent with the gpt‑4o model.” source |
| Operator OpenAI · 2025-01-23 |
Low Chemical, biological, radiological, nuclear |
Not reached | “this 1% qualifies as “low” risk, matching gpt‑4o’s “low” risk level, as detailed in openai’s preparedness framework (opens in a new window) 3 .” source |
| Muse Spark Contemplating Meta |
Moderate or lower Autonomy and AI research |
Not reached | “Loss of Control risks.We assess loss of control risk as “moderate or lower.”” source |
| Muse Spark Contemplating Meta |
High Chemical, biological, radiological, nuclear |
Reached | “We retain the same “high risk” determination for the unmitigated model.” source |
| Muse Spark Contemplating Meta |
Moderate or lower Chemical, biological, radiological, nuclear |
Not reached | “After validating that the mitigations deployed for Muse Spark, including refusal mechanisms, continue to perform robustly under the Contemplating architecture, we confirm residual risk at “moderate or lower.”” source |
| Muse Spark Contemplating 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 |
| Muse Spark 1.1 Meta |
Capability checkpoint Autonomy and AI research |
Reached | “Muse Spark 1.1 demonstrates improved coding capabilities, which are now meeting one of the indicative thresholds introduced in Muse Spark Safety & Preparedness Report (specifically, resolving over half of unique tasks on SWE-Bench Verified Hard).” source |
| Muse Spark 1.1 Meta |
Moderate or lower Autonomy and AI research |
Not reached | “In the Loss of Control domain, the model’s capabilities remain within the “moderate or lower” threshold under Meta’s Advanced AI Scaling Framework.” source |
| Muse Spark 1.1 Meta |
High Chemical, biological, radiological, nuclear |
Not determined | “Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source |
| Muse Spark 1.1 Meta |
High Cyber |
Not determined | “Our assessments indicate that, when evaluated without mitigations applied, we cannot rule out Muse Spark 1.1’s capabilities meeting this threshold in both the Chemical & Biological and Cybersecurity domains.” source |
| Muse Spark 1.1 Meta |
Moderate or lower Cyber |
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 Advanced AI Scaling Framework.” source |
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.
1911 of 1911
| Model | Tester | Test | What was found |
|---|---|---|---|
| GLM-5.3 Z.ai (Zhipu AI) |
Epoch AI Independent lab · 2026-10-16 |
CAISI benchmarks Cyber |
“Three of the seven CAISI benchmarks come from its September 2026 assessment of GLM-5.3 and are the only cyber results available for that model.” source |
| Claude Opus 5.5 Anthropic |
Epoch AI Independent lab · 2026-10-16 |
Cyber ECI Cyber |
“| Claude Opus 5.5 | Anthropic | 2026-09-22 | API access | Closed weights | 171.14 | 166.66 | 177.32 | 4 |” source |
| Claude Opus 4.8 Anthropic |
Redwood Research Independent lab · 2026-10-16 |
Screwtape pipeline runs against Redwood's own proxy monitors Deception and sabotage |
“We ran the pipeline 252 times on 94 unique transcripts.” source |
| Claude Mythos Preview Anthropic |
Epoch AI Independent lab · 2026-10-16 |
Cyber ECI Cyber |
“| Claude Mythos Preview (April) | Anthropic | 2026-04-07 | Limited access | Closed weights | 164.82 | 161.81 | 169.99 | 12 |” source |
| Muse Spark Open Meta |
SecureBio Independent lab · 2026-10-15 |
SecureBio's BioTIER refusal benchmark Chemical, biological, radiological, nuclear |
“It showed strong refusal behavior on both single-turn and multi-turn benchmarks, refusing over 98% of harmful requests on SecureBio's BioTIER refusal benchmark and over 99% on our internal single-turn chemistry refusal benchmark.” source |
| Gemini 3.1 Pro Google DeepMind |
Academic Academic · 2026-10-15 |
five VQA benchmarks in medical pre-training with 2.64M ZoomCap-captioned images Other |
“The evidence also transfers: in continual pre-training, ZoomCap-450K improves the 11-benchmark average by 1.31-2.38 points over the strongest competing corpus and Ling-Tiny-VL-v3 gains 1.4 points on a production document/OCR monitor suite (1.5 on perception) at 60B tokens; in medical pre-training, 2.64M ZoomCap-captioned images improve five VQA benchmarks by 3.04 points (vs. 1.76 for same-image Gemini-3.1-Pro captions).” source |
| GPT-5.6 Sol OpenAI |
Academic Academic · 2026-10-15 |
120 Chinese instructions per backend Other |
“In tests of 120 Chinese instructions per backend, GPT-5.6 Sol and DeepSeek both achieved a strict task-match rate of 94.2%, with all intents and execution decisions matching the reference.” source |
| GPT-5.4 mini OpenAI |
Academic Academic · 2026-10-15 |
mixed retail-and-telecom τ² environment tool-selection accuracy Other |
“On a mixed retail-and-telecom $τ^2$ environment, TRACER improves GPT-5.4's tool-selection accuracy by 4.7 points while reducing input tokens per step by 80%, exposing just 5 of 128 tools.” source |
| GPT-5.4 mini OpenAI |
Academic Academic · 2026-10-15 |
autonomous full-turn execution Other |
“In autonomous full-turn execution, it matches GPT-5.4's full-registry task success with 61% fewer input tokens per step.” source |
| GLM-5.3 Z.ai (Zhipu AI) |
Apollo Research Independent lab · 2026-10-15 |
ImpossibleBench Deception and sabotage |
“We first observed update-aware reasoning when running GLM-5.3 on reimplementations of ImpossibleBench and Palisade Research's shutdown resistance eval.” source |
| GLM-5.3 Z.ai (Zhipu AI) |
Apollo Research Independent lab · 2026-10-15 |
Palisade's shutdown resistance eval Deception and sabotage |
“We subsequently estimated the rate of verbalized update-aware reasoning by grading 6,452 GLM-5.3 rollouts (drawn from slight variations on Palisade's shutdown resistance eval and ImpossibleBench).” source |
| GLM-5.3 Z.ai (Zhipu AI) |
Apollo Research Independent lab · 2026-10-15 |
follow-up question about SFT-related concern Deception and sabotage |
“To get a clearer sense of what the model meant by “There’s an actual SFT-related concern: models should not resist shutdown.”, we appended a follow-up question about this statement to the full rollout and sampled 100 replies.” source |
| GLM-5.3 Z.ai (Zhipu AI) |
Apollo Research Independent lab · 2026-10-15 |
Palisade's shutdown resistance eval Autonomy and AI research |
“However, some of the environments are well-known AI safety settings (most notably, variants of Palisade’s shutdown resistance eval), so we think that this is likely responsible for eliciting this reasoning.” source |
| GLM-5.3 Z.ai (Zhipu AI) |
Apollo Research Independent lab · 2026-10-15 |
ImpossibleBench Deception and sabotage |
“GLM-5.3 shows update-aware reasoning in 1 in 1,600 trajectories, consistent with the estimate from the earlier runs (Bayesian mean posterior rate of 0.12%, 95% interval of 0.015% to 0.35%).” source |
| DeepSeek-V4-Pro DeepSeek |
Apollo Research Independent lab · 2026-10-15 |
same environments run on five other open-weight models Deception and sabotage |
“To figure out whether this is specific to GLM-5.3 or more general across models, we ran the same environments on five other open-weight models: DeepSeek V4 Pro, Qwen3.8 2.4T, Kimi K3, Gemma 4 31B and Nemotron 3 Ultra.” source |
| Qwen3.6-35B-A3B Alibaba Qwen |
Academic Academic · 2026-10-14 |
structured indirect prompt-injection setup distinguishing injected prompts from benign controls Prompt injection and agents |
“Attention Tracker provides the strongest consistent successful injection separation, reaching 0.949 AUROC on Qwen and 0.887 on Gemma.” source |
| GPT-6 Astra OpenAI |
Epoch AI Independent lab · 2026-10-14 |
SpireClimb Other |
“They can also beat Slay the Spire 1’s maximum difficulty (“Ascension 20 Heart”), with GPT-6 Astra achieving a 17% win rate and GPT-6.1 Sol achieving a 20% win rate.” source |
| GPT-5.6 Sol OpenAI |
Academic Academic · 2026-10-14 |
GraceBench Autonomy and AI research |
“Across 3,718 trajectories, GraceBench shows that outcome scores hide execution instability: GPT-5.6 Sol achieves the highest success rate (SR) but the lowest recovery ability, 51.24% of successful runs still contain a deviation or loop, and trajectories with the same success rate (SR) and completion rate (CR) can differ substantially in recovery behavior.” source |
| Gemini 4 Argon Google DeepMind |
Gray Swan Independent lab · 2026-10-13 |
indirect prompt injection benchmark Prompt injection and agents |
“In independent testing by Gray Swan on their Indirect Prompt Injection benchmark, Gemini 4 Argon achieved the lowest attack success rate of any frontier model tested.” source |
| Gemini 4 Argon Google DeepMind |
Government evaluator Government evaluator · 2026-10-13 |
evaluations of Gemini 4 Argon Cyber |
“AISI conducted evaluations of Gemini 4 Argon, having been given both a version of the model without our full set of safety mitigations and a version which had mitigations applied.” source |
| Gemini 4 Argon Google DeepMind |
CAISI (NIST) Government evaluator · 2026-10-13 |
measurement of cyber and biological capabilities and safeguards, as well as unintended model behaviors Chemical, biological, radiological, nuclear |
“We also collaborated with NIST's Center for Advancing Innovation and Standards for Super Intelligence (NIST CAISSI) on measurement of cyber and biological capabilities and safeguards, as well as unintended model behaviors.” source |
| Gemini 4 Argon Google DeepMind |
Gray Swan, Trajectory Labs Independent lab · 2026-10-13 |
adversarial stress-testing of critical safeguards Cyber |
“To evaluate the coverage and robustness of our deployed cyber and biological guardrails under realistic attack conditions, we partnered with external red-teaming organizations including Gray Swan and Trajectory Labs.” source |
| Gemini 4 Argon Google DeepMind |
Independent lab Independent lab · 2026-10-13 |
independent evaluation of biological and chemical risks Chemical, biological, radiological, nuclear |
“We worked with Faculty Science Limited (Faculty) to conduct an independent evaluation of biological and chemical risks.” source |
| Gemini 4 Argon Google DeepMind |
Independent lab Independent lab · 2026-10-13 |
independent evaluation of radiological and nuclear risks Chemical, biological, radiological, nuclear |
“Similarly, for radiological and nuclear risks, we worked with Faculty to conduct an independent evaluation of both domains, using the same evaluation approach on an unmitigated model across distinct threat actor groups.” source |
| Gemini 4 Argon Google DeepMind |
Independent lab Independent lab · 2026-10-13 |
evaluation on the unmitigated model focused on operationally relevant scenarios where models could provide capability uplift compared to human baselines of different levels Cyber |
“Faculty found that Gemini 4 Argon completed end-to-end scenarios, matching or outperforming predecessor models across assessed tactics, and resolving several multi-step bottlenecks where earlier models had struggled.” source |
| Gemini 4 Argon Google DeepMind |
Independent lab Independent lab · 2026-10-13 |
independent external safety testing Misuse and jailbreaks |
“We worked with third party evaluator pattrn.ai to test Gemini 4 Argon’s propensity for engaging in topics including radicalization & extremism, fraud & financial scams, and democratic harms, 52 with an emphasis on how the model might be used by malicious actors.” source |
| Gemini 4 Argon Google DeepMind |
Apollo Research Independent lab · 2026-10-13 |
apollo research strategic-deception evaluations Deception and sabotage |
“We also consider the model’s honesty and deception propensities, assessed through the Apollo Research strategic-deception evaluations and the Alignment Assessment below; those results show a lower misbehavior rate than predecessor models and do not change the harmful manipulation determination.” source |
| Gemini 4 Argon Google DeepMind |
Gray Swan Independent lab · 2026-10-13 |
gray swan cyber eval Cyber |
“The model reduces the QSR from 14% to 10% compared with Flash 3.8.” source |
| Gemini 4 Argon Google DeepMind |
Trajectory Labs Independent lab · 2026-10-13 |
manual red-teaming of our safeguarded cyber endpoints Cyber |
“Under these conditions, Trajectory Labs reported 12 successful breaks with no new universal jailbreaks found that generalized across tasks within this timeframe.” source |
| Gemini 4 Argon Google DeepMind |
Trajectory Labs Independent lab · 2026-10-13 |
manual red-teaming of our safeguarded cyber endpoints across nine attack technique families Cyber |
“Trajectory labs We partnered with Trajectory Labs to conduct manual red-teaming of our safeguarded cyber endpoints across nine attack technique families.” source |
| Gemini 4 Argon Google DeepMind |
Gray Swan Independent lab · 2026-10-13 |
gray swan bio evaluation Chemical, biological, radiological, nuclear |
“Gray Swan curated a set of 50 bio tasks covering high risk targets and deployed their automated red teaming agent, Shade, against these.” source |
| Gemini 4 Argon Google DeepMind |
Apollo Research Independent lab · 2026-10-13 |
offensive ML sabotage evaluations Deception and sabotage |
“Their capability-based evaluation suite utilizes four offensive ML sabotage evaluations, spanning three difficulty levels (easy, medium, hard).” source |
| Gemini 4 Argon Google DeepMind |
Apollo Research Independent lab · 2026-10-13 |
misalignment evaluations Deception and sabotage |
“Apollo’s evaluations on misalignment focused on assessing Gemini 4 Argon’s propensity for strategic deception.” source |
| Gemini 4 Argon Google DeepMind |
Gray Swan Independent lab · 2026-10-13 |
ipi benchmark Prompt injection and agents |
“Our partner Gray Swan performed their IPI Benchmark on our endpoint, which replays a set of held-out successful prompt injection attacks from their recent Indirect Prompt Injection Challenge competitions ( Dziemian et al., 2026 ).” source |
| GPT-5.6 Sol OpenAI |
Academic Academic · 2026-10-13 |
TiGanEval Other |
“Evaluating eight frontier models including GPT-5.6-Sol, we find outcome alignment broadly missing: the models lose 53 to 67 points of success rate when moving from the full specification to the partial instruction, and consequence feedback across four submissions does not close the gap.” source |
| Opus 4 Anthropic |
OWASP GenAI Security Project Standards body · 2026-10-08 |
Claude Opus 4 7 attacks a real company sharing a fictional name Cyber |
“Across four evaluation runs, Claude attacked a real company whose name matched a fictional target, extracting credentials and accessing production data.” source |
| Kimi K3 Moonshot AI |
Epoch AI Independent lab · 2026-10-08 |
Epoch Automation Reports Other |
“We gave six models real work tasks from Epoch, such as graphic generation and research design, to test how close they are to automating our work.” source |
| Kimi K3 Moonshot AI |
Epoch AI Independent lab · 2026-10-08 |
research design task Other |
“We also observe this with Kimi K3 and Qwen 3.8 Max on our research design task where they could generate any idea out of nine broad directions:” source |
| Grok 4.6 xAI |
Epoch AI Independent lab · 2026-10-08 |
Epoch Automation Reports Other |
“For example, Kimi K3 scores 158 on the Epoch Capabilities Index (ECI), which combines results from many AI benchmarks into a single measure of general capability.” source |
| Gemini 3.8 Flash Google DeepMind |
Epoch AI Independent lab · 2026-10-08 |
Epoch Automation Reports Other |
“Epoch Automation Reports captures these shortcomings.” source |