LLM benchmark guide
LLM Benchmarks for Model Selection
Use LLM benchmarks to narrow the field, then choose by workflow fit: quality, context, reliability, tool use, privacy, deployment model, and cost.
Frontier APIs
GPT, Claude, Gemini, and Grok compete on general quality, tool integration, and long-context workflows.
Value APIs
DeepSeek, GLM, Kimi, MiniMax, and Qwen-style models can win cost-sensitive jobs.
Open weights
Open-weight models matter when privacy, self-hosting, customization, or predictable infra cost dominates.
Benchmark signals to use
| Decision | Signal | Next page |
|---|---|---|
| Coding | SWE-bench, repo tasks, tool-use success | /best-ai-model-for-coding/ |
| Knowledge | MMLU and domain tests | /benchmarks/mmlu/ |
| Preference | Chatbot Arena / Elo-style rankings | /benchmarks/chatbot-arena/ |
| Deployment | API vs local vs hosted open weights | /local-models/ |
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.