Use Multiple AI Models Across Dev Stages to Catch More Bugs, Experts Say

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Software developers who rely on a single AI model for planning, coding, reviewing, and testing risk inheriting the same blind spots throughout their entire pipeline. Research by code review firm Greptile found that Claude's defect recall rate rises from 53.7% to 62.0% when GPT reviews its code instead of Claude reviewing itself. The core recommendation is to assign specialised models to each pipeline stage — a strong reasoning model for planning, a fast code-tuned model for implementation, and a different model for review. This approach also reduces vendor risk, matches cost to task complexity, and widens test coverage by leveraging models trained on different data. Developers are advised to document which model handled each stage and rotate model pairings every few months as capabilities evolve with new releases.

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