Stop using one AI model for every coding stage
There is a quiet mistake baked into most AI-assisted development workflows: one model does everything. It plans the feature, writes the code, reviews the diff, and generates the tests. The problem is that the same blind spots that cause a model to write a defect are exactly the blind spots that cause it to miss that defect in review.
The Data Behind the Pattern
Code review vendor Greptile measured this directly. Claude-authored pull requests get a 53.7% defect recall rate when Claude reviews them. Route the same code to GPT for review and recall climbs to 62.0%. The same pattern holds in reverse for GPT-authored code.
The author, Maxi Contieri, attributes this to how the models approach problems differently. Claude models tend toward broad, breadth-first scanning. GPT models tend toward narrow, depth-first evidence gathering. Different training histories produce different blind spots, and different blind spots catch different bugs.
️ The Four-Stage Split
The recommendation is to assign a specific model to each stage of the pipeline rather than locking one model into all four roles:
- Plan: Use a model with strong reasoning (the article cites Opus) to scope the task in read-only mode. No code written at this stage.
- Code: Hand the plan to a fast, code-tuned model (Sonnet) working inside a test harness. Keep diffs under 200 lines.
- Review: Route the diff to a model that did not write the code. GPT reviewing Claude-authored code is the concrete example given.
- Test: Have the reviewing model extend the test suite with edge cases the original author never considered.
Practical Notes
Each stage runs as its own subagent with fresh context and only the information that stage needs. Record which model handled each stage and what assumptions it made in an Architecture Decision Record (ADR) so repeating blind spots surface over time.
Rotate model pairings every few months as provider releases shift model strengths. Track model versions alongside names, because a provider’s default model can change silently and a rotation configured in January may mean something different by June.
The approach adds coordination overhead. The author notes it fits multi-day features better than one-line fixes. You still need a human checkpoint before merge. Cross-model review catches more defects but does not guarantee zero defects.
Claude Code, Codex CLI, Cursor, and OpenRouter all support configuring a different model for each agent or task inside the same workflow.
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