Agent benchmark guide
AI Agent Benchmarks for Real Workflows
Agent benchmarks should measure completed work, not isolated answers. The right model can plan, call tools, recover from errors, and keep costs under control.
Plan
Use the strongest reasoning models to decompose tasks, define constraints, and decide escalation paths.
Edit
Use coding-specialized or value models for iterative implementation, tests, and bulk changes.
Review
Use careful frontier models for regressions, architecture risk, and source-grounded verification.
Benchmark signals to use
| Decision | Signal | Next page |
|---|---|---|
| Planning | Task decomposition, constraint tracking | Pick an agent model |
| Tool use | Function calls, browser actions, shell reliability | Find an agent model |
| Coding | Patch quality, test pass rate | Pick a coding model |
| Cost | Tokens per completed task | Estimate agent cost |
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.