How scores are used
Model Benchmark Methodology
Our methodology treats benchmark scores as decision inputs. We combine source-reviewed model data with workflow fit, pricing, context, and deployment tradeoffs.
Source review
Model names, availability, context windows, and pricing claims should be traceable to official provider documentation.
Workflow weighting
Coding, reasoning, and tool-use scores matter differently for agents, RAG, support bots, data extraction, and local deployments.
Freshness matters
Model data decays quickly. Each high-value page should show freshness, cite sources, and avoid stale model names.
Benchmark signals to use
| Decision | Signal | Next page |
|---|---|---|
| Model identity | Official model names and release status | /model-data/ |
| Pricing | Input/output cost per 1M tokens | /cost-calculator/ |
| Context | Window size and practical reliability | /best-long-context-models/ |
| Recommendation | Best-fit workflow and caveats | /compare/ |
Practical rule
Use public AI model benchmarks as a shortlist, not as the final answer. Before adopting a model, test it on your own prompts, codebase, documents, budget constraints, and deployment requirements.