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 |
|---|---|---|---|
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
METR external testing Autonomy and AI research |
“External testing by METR ( Section 2.3.6 ) produced findings consistent with this assessment.” source |
| Claude Opus 5.5 Anthropic |
Dyno Therapeutics Independent lab · 2026-09-22 |
Black-box RNA sequence design Chemical, biological, radiological, nuclear |
“We partnered with Dyno Therapeutics on three evaluations of sequence-to-function modeling and design capability: 1. Black-box RNA sequence design: A medium-horizon challenge on which Dyno has evaluated 57 human participants drawn from the leading edge of the US ML-bio labor market since 2018.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Budget NanoGPT Speedrun Autonomy and AI research |
“We conducted a preliminary evaluation of [Claude Opus 5.5] informed by: ● Capability testing, conducted via API access granted over a period of 10 business days.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Language Model Conceptual Argumentation (LMCA) Autonomy and AI research |
“○ Language Model Conceptual Argumentation (LMCA) , a conceptual reasoning dataset described in A dataset of rated conceptual arguments (Cooper et al., 2026).” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Train a Program Autonomy and AI research |
“○ Train a Program , a task measuring an agent’s ability to train a machine learning model that replicates the behavior of a piece of software;” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Gaming Bot Autonomy and AI research |
“○ Gaming Bot , a task where an agent has to write a Python program capable of playing a video game, given a non-visual API to control the game.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Sunlight Autonomy and AI research |
“○ Sunlight , a task measuring an agent’s ability to conduct open-ended research and write a corresponding report.” source |
| Claude Opus 5.5 Anthropic |
Trajectory Labs Independent lab · 2026-09-22 |
external red-teaming by Trajectory Labs, PBC Cyber |
“Trajectory Labs, PBC spent roughly 95 hours red teaming Claude Opus 5.5, sending over 29,000 requests against sandboxed tasks that each require reproducing an exploit for a publicly documented vulnerability.” source |
| Claude Opus 5.5 Anthropic |
10a Labs Independent lab · 2026-09-22 |
multi-turn attack conversations framed as developer-focused coding tasks Cyber |
“10a Labs spent roughly 56 hours red teaming Claude Opus 5.5 across 82 multi-turn attack conversations, each framed as a large developer-focused coding task designed to draw a working exploit out of a volume of legitimately requested application code rather than asking for the exploit directly.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Shade automated attacker on critical infrastructure environments Cyber |
“Claude Opus 5.5’s safeguards refused over 90% of attempts outright, and none of the rest reached the objective.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Shade automated attacker on exploit reproduction and ransomware staging scenarios Cyber |
“Of roughly 1,700 attempts, Claude Opus 5.5’s safeguards refused about a quarter outright.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Indirect Prompt Injection (IPI) benchmark Prompt injection and agents |
“Claude Opus 5.5 matches Claude Fable 5.1, our most robust model to date on this benchmark, with an attack success rate of 0.1% at k=1, 0.7% at k=10, and 1.0% at k=15.” source |
| Claude Opus 5.5 Anthropic |
Independent lab Independent lab · 2026-09-22 |
Shade Prompt injection and agents |
“Claude Opus 5.5 substantially improves on Claude Opus 5 in coding environments against this stronger attacker, with an attack success rate of 54.61% over all attempts, compared to 88.92% for Claude Opus 5 with thinking enabled and 85.49% without thinking enabled.” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Budget NanoGPT Speedrun Other |
“Budget NanoGPT Speedrun, a constrained version of the popular NanoGPT Speedrun competition for AI R&D.” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Language Model Conceptual Argumentation (LMCA) Other |
“Language Model Conceptual Argumentation (LMCA), a conceptual reasoning dataset described in A dataset of rated conceptual arguments (Cooper et al., 2026).” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Train a Program Other |
“Train a Program, a task measuring an agent’s ability to train a machine learning model that replicates the behavior of a piece of software;” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Gaming Bot Other |
“Gaming Bot, a task where an agent has to write a Python program capable of playing a video game, given a non-visual API to control the game.” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Sunlight Other |
“Sunlight, a task measuring an agent’s ability to conduct open-ended research and write a corresponding report.” source |
| Claude Opus 5.5 Anthropic |
METR Independent lab · 2026-09-22 |
Budget NanoGPT Speedrun Other |
“Claude Opus 5.5 improves upon Fable 5.1 across both verifiable tasks (Budget NanoGPT, Gaming Bot) and harder-to-verify tasks (LMCA, Sunlight), but:” source |
| Grok 4.7 xAI |
Independent lab Independent lab · 2026-09-21 |
Cathedralbench Cyber |
“CathedralBench is an independent, third party evaluation of cyber performance.” source |
| Grok 4.7 Cursor |
Independent lab Independent lab · 2026-09-21 |
cathedralbench Cyber |
“CathedralBench is an independent, third party evaluation of cyber performance.” source |
| Gemini 3 Flash Google DeepMind |
Academic Academic · 2026-09-21 |
warm introduction task on relationship-reasoning benchmark Other |
“With Gemini-3-Flash on a stratified subset of n = 150 queries per task, ReAdapt improves warm-introduction accuracy from 37% to 51% (+14 points) and reaction-selection accuracy from 69% to 77% (+8 points).” source |
| Claude Opus 5 Anthropic |
Academic Academic · 2026-09-21 |
OSWorld-Pro Other |
“Our findings reveal that OSWorld-Pro is challenging even for state-of-the-art LLMs, with top performers like Claude Opus 5 achieving only 75.7% vs. 83.4% on OSWorld.” source |
| Claude Opus 5 Anthropic |
Academic Academic · 2026-09-21 |
governed delivery plane end-to-end run across ten stages scored by an oracle Other |
“We ran four frontier agents (Claude Opus 5, GPT-5.6-luna, Gemini 3.7 Flash, DeepSeek V4-Pro) end to end on metered L40S, A100, and H200 GPUs across 8B to 70B open bases, certifying every scenario before scoring.” source |
| Claude Opus 4.8 Anthropic |
Academic Academic · 2026-09-21 |
suite of 325K experiments on 13 agents across three types of economic decisions (flights, health insurance, and graduate programs) Bias |
“In a suite of 325K experiments on 13 agents across three types of economic decisions (flights, health insurance, and graduate programs), we find that 8 models systematically choose more expensive options for wealthier users when requests are identical.” source |
| GPT-5.4 mini OpenAI |
Academic Academic · 2026-09-19 |
CyberGym Cyber |
“With fewer than 200 training examples, SelfOp yields a 17-point self-improvement for GPT-5.4-mini (with Codex), enough to surpass the frontier GPT-5.4 baseline by 6 points, and an 18.5-point self-improvement for GPT-5.4 itself.” source |
| Claude Sonnet 4 Anthropic |
Academic Academic · 2026-09-18 |
GenEval2 execution success Other |
“Five rounds over 1,406 real user briefs and 1,869 automatically graded trajectories, with no weight updates and no human labels, grow the bank from 76 documentation-derived skills to 139 and raise GenEval2 execution success on Claude-Sonnet-4 from 72.7% to 99.3% (+11.99 points in generation quality), with 61.8% and 67.6% win rates against the no-skill agent across four specialized design benchmarks on Claude-Sonnet-4 and Claude-Opus-4.6.” source |
| Claude Opus 4.8 Anthropic |
Academic Academic · 2026-09-18 |
SWE-Proof Other |
“Across two frontier models, verification catches what tests miss: a quarter to a half of test-passing patches admit counterexamples, which a structured natural-language specification does not fix, while a correct formal one lifts resolution from 85% to 95% for Opus 4.8.” source |
| GLM-5.3 Z.ai (Zhipu AI) |
US CAISI (NIST) Government evaluator · 2026-09-17 |
sec-bench pro Cyber |
“CAISI evaluated GLM-5.3’s performance on four benchmarks covering vulnerability discovery and exploit development (Figure 2).” source |
| GPT-6 Astra OpenAI |
SecureBio Independent lab · 2026-09-14 |
virology capabilities test (vct) Chemical, biological, radiological, nuclear |
“gpt-6 astra outperformed every model we have previously tested on the virology capabilities test (vct) and vct-v2 knowledge benchmarks, with performance increases of 4.3 percentage points and 8 percentage points, respectively, relative to gpt-5.6 sol, and more than 30 percentage points better than our human subject matter expert baselines.” source |
| GPT-6 Astra OpenAI |
SecureBio Independent lab · 2026-09-14 |
world-class bio (wcb) Chemical, biological, radiological, nuclear |
“on world-class bio (wcb), an open-response test of niche biological knowledge held by only a handful of specialists in the world, the highest-performing astra checkpoint scored 61.6% ± 0.6%, about seven percentage points lower than gpt-5.6 sol.” source |
| GPT-6 Astra OpenAI |
SecureBio Independent lab · 2026-09-14 |
biotier Chemical, biological, radiological, nuclear |
“the safeguarded astra pre-release checkpoint correctly refused 82.9% of biotier-refuse and correctly did not refuse 95.3% of biotier-permit.” source |
| GPT-6 Astra OpenAI |
SecureBio Independent lab · 2026-09-14 |
manual assessment by virology experts Chemical, biological, radiological, nuclear |
“over 10 hours of interaction, our manual assessors tested the bounds of dual-use knowledge of the railfree astra pre-release checkpoint and the willingness of the safeguarded astra pre-release checkpoint to answer high-risk questions.” source |
| GPT-5.6 Sol OpenAI |
SecureBio Independent lab · 2026-09-14 |
virology capabilities test (VCT) Chemical, biological, radiological, nuclear |
“gpt-6 astra outperformed every model we have previously tested on the virology capabilities test (vct) and vct-v2 knowledge benchmarks, with performance increases of 4.3 percentage points and 8 percentage points, respectively, relative to gpt-5.6 sol, and more than 30 percentage points better than our human subject matter expert baselines.” source |
| GPT-5.6 Sol OpenAI |
SecureBio Independent lab · 2026-09-14 |
human pathogen capabilities test (HPCT) Chemical, biological, radiological, nuclear |
“gpt-6 astra’s score on the human pathogen capabilities test (hpct) was very similar to the score achieved by gpt-5.6 sol.” source |
| GPT-5.6 Sol OpenAI |
SecureBio Independent lab · 2026-09-14 |
world-class bio (WCB) Chemical, biological, radiological, nuclear |
“on world-class bio (wcb), an open-response test of niche biological knowledge held by only a handful of specialists in the world, the highest-performing astra checkpoint scored 61.6% ± 0.6%, about seven percentage points lower than gpt-5.6 sol.” source |
| GPT-5.6 Sol OpenAI |
SecureBio Independent lab · 2026-09-14 |
advanced screening evasion Chemical, biological, radiological, nuclear |
“on advanced screening evasion, which assesses an agent’s ability to evade commercial nucleic acid synthesis screening algorithms, the railfree variant of astra was the top-scoring model that securebio has tested.” source |
| GPT-5.6 Sol OpenAI |
SecureBio Independent lab · 2026-09-14 |
BioTier Chemical, biological, radiological, nuclear |
“the safeguarded astra pre-release checkpoint correctly refused 82.9% of biotier-refuse and correctly did not refuse 95.3% of biotier-permit.” source |
| o3 OpenAI |
SecureBio Independent lab · 2026-09-11 |
virology capabilities test (vct) Chemical, biological, radiological, nuclear |
“since the release of vct in april 2025, the accuracy of the top-performing model on the benchmark has increased by ~12 percentage points (o3: 42.0%; gpt-5.6 sol: 53.9%).” source |
| o3 OpenAI |
SecureBio Independent lab · 2026-09-11 |
vct-v2 Chemical, biological, radiological, nuclear |
“model accuracies 2 dropped by 1.0–7.1 percentage points on vct-v2 relative to vct (the original benchmark) across the models tested, which reflects the removal of easy questions and the closing of shortcuts.” source |