How do you choose which AI model to use for each coding task?

Engineering teams should select AI coding models by task class rather than adopting a single organization-wide default, since inexpensive models may handle routine work effectively while frontier models remain better suited to complex, multi-file, or high-risk tasks. The recommended approach is to classify work such as triage, bounded changes, implementation, review, and refactoring according to their technical demands, then benchmark two or three candidate models on 20–40 real tasks drawn from the organization’s own repositories. Results should be evaluated using class-specific measures such as agreement with human decisions, test pass rates, review interventions, defect detection, consistency, and cost, while keeping prompts and environments constant. Public benchmarks can help narrow candidates but cannot replace internal testing because repository context and engineering conventions affect performance. Model rankings should be revisited when models, agent configurations, or costs change, and teams can begin with a high-volume, low-risk category before expanding routing rules. Warp Factories is presented as a platform that supports this approach through configurable, version-controlled model routers that classify incoming tasks and direct them to the best-performing model and agent configuration.

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