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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallStart with the AI developer’s official safety or transparency hub, then open the report for the exact model you care about. Check its version and date, the risks and configurations tested, the methods and findings, and any limitations or later addenda. A published evaluation documents results under stated conditions; it is not a universal safety certificate.
Where to look for published evaluations
Start with the developer’s official hub
Official hubs are useful indexes, but the model-specific report is the evidence to read. Anthropic’s Transparency Hub links model cards and selected safety-evaluation summaries. The company notes that it conducts additional evaluations and points readers to full system cards for complete publicly reported results. OpenAI’s Deployment Safety Hub lists system cards and dated addenda, making it easier to find follow-up documents.
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Search for the exact model and report type
On the publisher’s site, search the exact model name alongside terms such as “system card,” “model card,” “safety evaluations,” “risk report,” or “evaluation.” Prefer the original report over a news summary, and look for addenda as well as the initial card. A third-party catalog can help locate documents, but verify each result at the publisher’s own site.
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The NIST AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. These resources offer a risk-management lens; they are not a directory of evaluated models or proof that a named model passed a safety test.
How to assess a model card or safety report
- Identify exactly what was evaluated. Record the model name, version or family, report date, and whether the subject was a research checkpoint, release candidate, API model, or end-user product. A family-level report may not describe every deployment configuration. OpenAI’s o1 system card, for example, warns that production performance can vary with system updates, final parameters, and the system prompt.
- Read the scope before interpreting scores. Note which risks and capabilities were tested and which were left out. The GPT-4o System Card describes several evaluation categories, including speech-to-speech as well as text and image capabilities. It also discusses third-party assessments of autonomous capabilities and potential societal impacts.
- Inspect the method and setup. Look for scenarios or prompts, tools available to the model, sampling and configuration details, scoring criteria, thresholds, and whether people or automated graders assessed results. If a report does not give enough detail to judge these conditions, record that uncertainty rather than assuming another report used a comparable test.
- Separate model behavior from product safeguards. A report may cover training and model behavior as well as filters, monitoring, moderation, policy, or other product controls. These are distinct intervention points. The GPT-4o card discusses mitigations at both development and product stages, including red teaming and product-level measures.
- Check limitations and independent input. Read disclosures about weaknesses, excluded conditions, whether the model may have recognized an evaluation, and any external red-team or evaluator involvement. A result supports a claim about the test described; it cannot guarantee safe behavior in every real-world setting.
- Follow summaries to full reports and addenda. A hub summary may be selective. Anthropic directs readers to full system cards for complete publicly reported results, while OpenAI’s hub shows why dated follow-up documents are worth checking.
How to compare evaluations across models
Use the same axes for each report, and keep the underlying conditions visible alongside any result.
| Comparison axis | What to record |
|---|---|
| Identity and date | Model and version, release or evaluation date, and report or addendum version. |
| Risk coverage | Domains tested and important omissions. |
| Method | Test design, tools or access, prompts or configuration, and scoring approach. |
| Findings | Results with units and denominators where supplied, plus thresholds and uncertainty. |
| Independence | Internal, external, or mixed assessment, and evaluator relationship where disclosed. |
| Safeguards | Model-level changes versus product controls, monitoring, and deployment limits. |
| Limits | Known weaknesses, caveats, and any mismatch with the use you have in mind. |
Avoid ranking models by scores from unlike tests. The third-party Model Card Explorer reports 689 distinct benchmark names in 90 public model cards across six frontier labs, with 70 benchmarks shared by at least two labs. Its page does not state a publication year; these figures were accessed October 4, 2026. The count describes public reporting, not private evaluations, and its authors say fragmentation alone does not imply concealment. It illustrates why a score difference may not answer a like-for-like question.
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A 2026 report’s bibliography also points readers to original publisher documents including Anthropic’s Claude Sonnet 4.5 System Card (2025), Google’s Gemini 3 Pro Model Card (2025), and OpenAI’s GPT-5 System Card (2025). Follow the bibliography to the publisher versions and verify that the document matches the model version you want to assess.
What it means when you cannot find a report
State the scope of the search: for example, that no public report was found on the developer’s hub or model page checked. That establishes what you located publicly, not whether the developer conducted evaluations privately. A public-document catalog likewise measures disclosed reporting; it cannot establish that unpublished testing did or did not happen.
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