Choose a coding model by matching it to a defined workload and measuring whether its changes pass your review process. Provider reputation and public leaderboards are useful screening signals, but they cannot replace a repository-level evaluation of the complete agent setup.
Build a benchmark set from real work:
- One focused bug with a reproducible failure.
- One multi-file feature with explicit acceptance tests.
- One refactor that must preserve public interfaces.
- One codebase-explanation task with known correct references.
- One permission or tool-use scenario that should stop and ask.
Run each candidate with the same repository state, instructions, context budget, tools, and acceptance checks. Record task completion, regressions, unnecessary edits, test quality, latency, model spend, and reviewer repair time. Review blind where practical. Public coding benchmarks can help shortlist models, but verify what the benchmark measures and whether it includes tool use, repository context, or only isolated problems.
Do not change the model, agent wrapper, prompt, and tool permissions at the same time; otherwise the result cannot identify the cause. There may be no single winner. A multi-model workflow can route planning, implementation, and verification to different strengths while enforcing one quality gate.
Repeat the benchmark when model versions, tools, or repository architecture change. Your best choice is the setup that repeatedly produces understandable, test-backed changes at an acceptable total cost.
Related reading: What is the best coding model right now? and Which models work for agentic coding?.
