Having one model review code written by a model from a different family is becoming increasingly popular.
You might build a feature with Codex, then ask Claude Code to review it. The idea is that each provider trains its models differently, so the reviewing model may catch problems the original model missed.
That sounds useful, but it raises an important question: what information are we giving the reviewing model?
Is reviewing the code enough?
A typical AI code review might include the final code, the diff, and the commit history.
