Single-Model vs Multi-Model AI Code Review: What I Learned Running Both

I've been obsessing over AI code review for the last year. Not because I think AI will replace code review — I don't — but because I think most developers are leaving a lot of quality signal on the table by using AI review the wrong way. Here's the thing nobody talks about: a single AI model is confidently wrong surprisingly often. Not maliciously wrong. Not obviously wrong. Just... plausible-sounding wrong. It'll flag a false positive, miss a real bug, or give you a high-confidence "looks good" on code that has a subtle race condition. And because the model sounds so sure of itself, you accept it and move on. I learned this the hard way. Then I started running multi-model consensus review instead, and it changed my whole mental model of what AI code review should look like. Here's what I found. The Problem With Single-Model Review When you pipe code through one model — say, Claude or GPT-4 — you get a single "opinion." That opinion is shaped by: The model's training data distribution