Which AI Should Review Your AI's Code?
TL;DR: I ran three phases of Claude Code's output past several reviewer models and kept score. Here's the configuration I settled on, and why one reviewer gets you most of the way.
If you’re building with an AI coding agent and you’re not having a different AI review the output, you’re leaving the cheapest quality win on the table.
I say “different” deliberately. A model reviewing its own work is agreeable in exactly the places you need it to be difficult. It made the choices; it likes the choices. You want a reviewer that has no stake in the design and no memory of talking itself into it.
Which raises the practical question: reviewed by whom, and is it worth the tokens?
Keeping score
I took two rounds of my own work — the code behind two finished phases, and the architecture document for a third before a line of it was built — pushed all of it through review by several different models, and tracked which reviewer caught what. Not a benchmark — my actual pipeline, my actual code, my actual findings, and a small sample: fifteen confirmed blockers out of forty-eight findings. But the pattern was consistent enough that I’ve been running on it since.
Here’s what I landed on:
| Scenario | Reviewers | Why |
|---|---|---|
| Standard phase review | GPT (primary) + Gemini (secondary) | Catches 95%+ of blockers with minimal noise |
| Large bundle / many files | GPT + Gemini + Kimi | Kimi adds structural, naming, and documentation coverage |
| Maximum confidence | GPT + Gemini + Perplexity | Three-way convergence on critical blockers |
| Budget or time constrained | GPT alone | 93% blocker detection as a single reviewer |
The three things worth taking away
One good reviewer gets you most of the way. 93% from a single reviewer against 95%+ from two is a much smaller gap than I expected before I measured it. If cost or latency is your constraint, run one and don’t feel bad about it. The enormous jump is from zero reviewers to one, and everything after that is refinement.
The second reviewer buys disagreement, not volume. Gemini’s value alongside GPT wasn’t finding twice as many things. It was finding different things, and occasionally disagreeing about severity — which is a signal in itself. Two reviewers that always agree are one reviewer you’re paying twice for. Pick models that fail differently.
Reviewers have specialties, and they’re not the ones in the marketing. Kimi earned its slot on large bundles specifically for structural and naming and documentation coverage — the stuff that doesn’t break anything today and makes the codebase miserable in three months. That’s not what I’d have guessed from a leaderboard. You find it by measuring on your own code.
A note on noise
“Minimal noise” is doing real work in that table. A reviewer that flags forty things per phase, thirty of which are opinions about style, is worse than one that flags eight real ones — because you’ll start skimming, and the day you skim past a genuine blocker is the day the whole exercise stopped paying for itself.
Signal-to-noise is a reviewer’s most important property and the hardest one to see from a benchmark score. Measure it on your own code before you commit to a roster.
Where this stands now
Added August 2026. The specific roster has moved on — the reviewer lineup in my pipeline today looks different, because the models moved and so did my requirements. The structure hasn’t budged: the model that builds is never the model that reviews, and the review runs automatically rather than when I remember to ask for it.
If you take one thing from this, take that rule rather than the table. The table has an expiration date. The rule doesn’t.
The projects, experience and opinions here are mine. AI helped me turn my notes and build records into this piece and polished it for Cairoglyphics.ai.