Methodology
How we test and explain AI models
Our comparisons combine structured third-party measurements with an editorial review process designed to make model tradeoffs understandable and traceable.
Primary data source
Data provided by Artificial Analysis at https://artificialanalysis.ai/; editorial analysis by AI Model Comparison. The source dataset includes benchmark scores, pricing, output speed, latency, model creators, and release information. We retain the source values rather than inventing replacement scores.
How scores are compared
Each benchmark keeps its original scale. Higher scores generally indicate stronger measured performance, while lower price and lower latency are treated as advantages for cost and responsiveness. We show individual metrics because a single overall number can hide important workload-specific tradeoffs.
- Benchmark values are checked against the current local data snapshot.
- Pricing comparisons use the published input, output, and blended prices available in the source data.
- Speed and latency are reported separately because they answer different user questions.
Editorial policy
Articles are data-driven and may be drafted with AI assistance. A human reviews the draft, sources, numerical claims, and recommendation before publication. AI assistance does not replace the publication decision or the responsibility to correct errors.
Research and experience signals
When an article discusses developer experience, reliability, or model behavior that is not captured by benchmarks, the claim must be tied to a cited public source such as official documentation or a direct community discussion. Unsupported qualitative claims are removed.
Updates and corrections
We monitor material score and price changes. Published comparisons are marked for review when those changes could affect the article. Readers can report corrections at contact@aimodelcomparison.org.