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.
139 of 139
| Model | Level, as the provider names it | The provider states | The provider's sentence |
|---|---|---|---|
| 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 |
| 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 |
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.
1940 of 1940
| Model | Tester | Test | What was found |
|---|---|---|---|
| Grok 4 xAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape Chemical, biological, radiological, nuclear |
“gpt-5.5 and claude opus 4.7 were largely blocked from performing our task by either their own refusals or the action of content filters.” source |
| Grok 4 xAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape immune-evasion prediction model case study Chemical, biological, radiological, nuclear |
“in the first case study involving the evescape immune-evasion prediction model, llm agents adopted generally reasonable strategies for repository navigation and workflow execution.” source |
| Grok 4 xAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 autograder Chemical, biological, radiological, nuclear |
“grok 4 had notably low scores on many of these items because it returned minimal reasoning blocks as part of its responses, giving the autograding model limited material to assess.” source |
| Gemini 3.1 Pro Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection evaluation Chemical, biological, radiological, nuclear |
“for the bt selection evaluation, we used the in-built inspect implementation of an agent scaffold that follows react34 logic, with custom token and message limits and access to web search and 33 ai security institute, “inspect ai: framework for large language model evaluations.” 34 yao et al., “react.” 13 browsing tools.” source |
| Gemini 3.1 Pro Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 case study GFP task Chemical, biological, radiological, nuclear |
“opus 4.7, gpt-5.5, and gemini 3.1 performed well across the gfp-targeted task in general, though they did not achieve perfect performance across all prompt variants.” source |
| Gemini 3.1 Pro Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 case study influenza HA task Chemical, biological, radiological, nuclear |
“as in our evescape results, several models neglected to perform the influenza ha variant of our task.” source |
| Gemini 3.1 Pro Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
inter-rater reliability metrics (Krippendorff's alpha) Other |
“table f.3. inter-rater reliability and jury performance across datasets dataset krippendorff's α jury accuracy (%) esm3 gfp 0.554 88.9 esm3 influenza ha 0.515 66.7 evescape influenza ha 0.077 68.8 evescape lassa gpc 0.605 62.5 56 discussion intra-model consistency varies substantially across models and datasets.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
agent-driven knowledge benchmark Chemical, biological, radiological, nuclear |
“the first component is an agent-driven knowledge benchmark designed to test the ability of llms to select appropriate bts for specific tasks.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape case study Chemical, biological, radiological, nuclear |
“the second component of this report assesses the ability of llm agents to perform toy tasks with bts.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
biological tool selection evaluation Chemical, biological, radiological, nuclear |
“figure 3.1. accuracy of llms on the biological tool selection evaluation ............................... 16” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection evaluations Chemical, biological, radiological, nuclear |
“the bt selection evaluations are knowledge benchmarks, attempted by llm agents with web access, measuring the ability of those agents to select bts suitable for given biological design tasks.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection benchmark Chemical, biological, radiological, nuclear |
“to evaluate llm agents’ abilities to select task-appropriate bts, we developed a benchmark assessment consisting of 61 questions across two contexts (non-scenario and scenario).” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 protein design task Chemical, biological, radiological, nuclear |
“task design agents were tasked to demonstrate the mechanics of using esm3 to redesign a protein such that (1) essential functional elements are preserved and (2) the resulting amino acid sequence falls below a predefined sequence identity threshold (90%) relative to the reference protein.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection evaluation Chemical, biological, radiological, nuclear |
“for the bt selection evaluation, we used the in-built inspect implementation of an agent scaffold that follows react34 logic, with custom token and message limits and access to web search and 33 ai security institute, “inspect ai: framework for large language model evaluations.”” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
evescape Chemical, biological, radiological, nuclear |
“gpt-5.5 was able to complete the influenza ha task without denial in several attempts across two of the three prompt tiers (figure 4.2), but both models declined all requests to perform the lassa-targeted task (figure 4.1).” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
esm3 task Chemical, biological, radiological, nuclear |
“in our second case study, we prompted the agents to work with a local version of the esm3 generative model, esm3-sm-open-v1,43 and to use it to generate a protein that has less than 90% sequence identity with a provided reference protein.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection knowledge benchmark Chemical, biological, radiological, nuclear |
“our bt selection benchmark found that all models were highly performant at identifying bt functions when presented decontextualized questions.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape immune-evasion prediction case study Chemical, biological, radiological, nuclear |
“in the first case study involving the evescape immune-evasion prediction model, llm agents adopted generally reasonable strategies for repository navigation and workflow execution.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 protein language model case study Chemical, biological, radiological, nuclear |
“the second case study evaluated agents on their ability to carry out basic operations with the esm3 protein language model.” source |
| Gemini 2.5 Flash Google DeepMind |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
intra-model consistency test (10 repeated evaluations) Other |
“we evaluated consistency on only the best performing juries using the following metrics.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
agent-driven knowledge benchmark Chemical, biological, radiological, nuclear |
“in the bt selection evaluation, all seven llms were able to identify bts suitable for performing certain functions, such as designing a protein.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
evescape case study Chemical, biological, radiological, nuclear |
“while agents were able to operate bts for predicting immune evasion (with evescape) and protein redesign (with esm3), most were not able carry out these tasks with perfect consistency.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
biological tool selection evaluation Chemical, biological, radiological, nuclear |
“accuracy of llms on the biological tool selection evaluation” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
BT selection knowledge benchmark Chemical, biological, radiological, nuclear |
“to evaluate llm agents’ abilities to select task-appropriate bts, we developed a benchmark assessment consisting of 61 questions across two contexts (non-scenario and scenario).” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape task Chemical, biological, radiological, nuclear |
“the base prompt included the uniprot id for the variant protein.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
bt selection evaluation non-scenario question set Chemical, biological, radiological, nuclear |
“we first evaluated models on a question set probing specific bt functions without any biological context.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
bt selection evaluation scenario question set Chemical, biological, radiological, nuclear |
“next, we evaluated llms on a question set that required an agent to map bt functions onto a contextualized biological design scenario.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
biological tool selection evaluation (non-scenario question set, MC and OER formats) Chemical, biological, radiological, nuclear |
“however, we observed denials in most closed-weight llms (opus 4.7, gemini 3.1 pro, and gpt 5.5) with denials found in both mc and oer question sets for opus 4.7 and gpt 5.5.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
scenario-based biological tool selection question set Chemical, biological, radiological, nuclear |
“the scenario-based question set produced higher denial rates, particularly among closed- weight llms (opus 4.7, gemini 3.1 pro, gpt 5.5, and grok 4.3).” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape case study (Lassa virus GPC and influenza HA tasks) Chemical, biological, radiological, nuclear |
“gpt-5.5 and claude opus 4.7 were largely blocked from performing our task by either their own refusals or the action of content filters.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
EVEscape influenza HA immune escape prediction task Chemical, biological, radiological, nuclear |
“gpt-5.5 was able to complete the influenza ha task without denial in several attempts across two of the three prompt tiers (figure 4.2), but both models declined all requests to perform the lassa-targeted task (figure 4.1).” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 GFP case study Chemical, biological, radiological, nuclear |
“opus 4.7, gpt-5.5, and gemini 3.1 performed well across the gfp-targeted task in general, though they did not achieve perfect performance across all prompt variants.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
ESM3 influenza HA case study Chemical, biological, radiological, nuclear |
“the same two models that refused evescape, claude opus 4.7 and gpt- 5.5, either refused or were blocked by content filters from completing the esm3 task with the influenza ha target.” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
intra-model consistency test (10 repeated evaluations) Other |
“this analysis quantifies both intra-model consistency (whether a single model produces stable predictions across 10 repeated evaluations) and inter-model reliability (whether different judge models agree on classifications).” source |
| GPT-5.5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
inter-rater reliability (Krippendorff's alpha) jury analysis Other |
“inter-rater reliability metrics in table f.3:” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
biological tool operation case study: EvoScape Chemical, biological, radiological, nuclear |
“we designed case studies for two bts identified as high-risk in the bt risk index: evescape and esm3.” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
biological tool operation case study: ESM3 Chemical, biological, radiological, nuclear |
“agents were tasked to demonstrate the mechanics of using esm3 to redesign a protein such that (1) essential functional elements are preserved and (2) the resulting amino acid sequence falls below a predefined sequence identity threshold (90%) relative to the reference protein.” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
esm3 protein design task Chemical, biological, radiological, nuclear |
“task design agents were tasked to demonstrate the mechanics of using esm3 to redesign a protein such that (1) essential functional elements are preserved and (2) the resulting amino acid sequence falls below a predefined sequence identity threshold (90%) relative to the reference protein.” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
bt selection evaluation Chemical, biological, radiological, nuclear |
“for the bt selection evaluation, we used the in-built inspect implementation of an agent scaffold that follows react34 logic, with custom token and message limits and access to web search and 33 ai security institute, “inspect ai: framework for large language model evaluations.”” source |
| GPT-5 OpenAI |
RAND (CAST / Project Canary) Independent lab · 2026-06-25 |
non-scenario questions Chemical, biological, radiological, nuclear |
“six out of seven llms scored above 80% on the mc question set.” source |