Cross-model review is a quality-control pattern: one model builds, another reviews, and the loop continues until review stops finding meaningful issues. Claude Code writes something, Codex reviews it and flags a bug or weak assumption, then Claude fixes it and resubmits. In code this often happens before a pull request; in writing, one model drafts and another checks clarity, claims, and tone. The value comes from difference: models have different tendencies, and one catches what the other misses.
Consider a second reviewer on an important proposal. The first person writes it; the second reads it with fresh eyes and says “this number doesn’t tie” or “this claim needs a source.” You still own the final send. The second reviewer improves the draft before it lands on your desk.
Why you care
Two AIs agreeing doesn’t make the answer true. They can share the same blind spot, especially when reading the same bad context, so the human stays accountable. You still check the facts and whether the work fits the client and the risk. Cross-model review is one kind of guardrail, never the only one.