同じ差分を5つのLLMにレビューさせてみた——バグより先に見つかったのは自分のミスだったA developer ran the same 140-page navigation and brand-color diff through five…

A developer recently published an experiment in which the same code diff was reviewed independently by five large language models, and the most striking outcome was that the process surfaced the developer's own overlooked mistakes before it flagged any genuine bugs. The write-up is a small but useful data point for anyone weighing whether AI-assisted code review is worth the added effort, and how to structure it.

The task itself was routine maintenance. On their own site, the developer changed navigation labels and swapped a brand color across more than 140 pages. The color replacement was applied mechanically with a regular-expression script, the kind of bulk operation that is fast but blunt. Standard QA had passed, and the change looked fine in production. Despite that, the developer remained unsettled by the question of whether the change was truly correct. Rather than trust a single reviewer, they handed the identical diff to five models available on their own machine — Claude Sonnet 5, Claude Haiku 4.5, GPT-5.6-luna, GPT-5.6-terra, and Kimi K3 — and asked each to review it independently.

The choice of a regex-driven replacement is worth dwelling on, because it explains why the caution was warranted. A pattern that matches a hex color or a text string will match it everywhere, including places the author never intended: inside code comments, class names, inline styles, structured data, or documentation examples. Because such edits are mechanical, they rarely fail loudly; the page still renders, QA still passes, and the defect hides in plain sight. That is precisely the category of problem a fresh reviewer, human or model, is well placed to catch.

According to the report, the interesting question was not simply which model caught the most issues. Instead, the exercise appears to have worked as a forcing function for the developer's own attention. Preparing the diff for review, reading each model's response, and reconciling their disagreements led the author to notice gaps in their own work — omissions and assumptions that had slipped past both QA and their initial confidence. In other words, the value came less from any single model's verdict and more from the discipline of assembling several independent perspectives.

That framing echoes a broader pattern in how teams are experimenting with LLMs for engineering work. Running the same prompt through multiple models — sometimes called cross-checking or an ensemble approach — is a common tactic for reducing the blind spots and confident errors that any one model can exhibit. Different model families are trained differently and tend to fixate on different things, so agreement between them can raise confidence while disagreement can flag areas that deserve a human second look.

The setup also reflects the growing practicality of local and self-hosted models. The developer describes using models available at hand, which fits a trend toward keeping proprietary source code on local hardware rather than sending it to a remote service, both for privacy and for cost control. The lineup spans vendors, including Anthropic's Claude tiers, OpenAI-style GPT variants, and Kimi from Moonshot AI, suggesting an increasingly crowded field in which a single developer can plausibly keep several competitive models within reach.

None of this replaces conventional safeguards. Regular-expression audits, visual regression testing, and linting remain the first line of defense against exactly this kind of bulk edit, and dedicated AI review tools — from GitHub Copilot features to purpose-built review assistants — aim to integrate such checks into the pull-request workflow. What the experiment adds is a reminder that the act of seeking review, and the friction of comparing several opinions, can be as valuable as the review itself.

The account is a single anecdote rather than a controlled benchmark, so its conclusions should be read as suggestive rather than definitive. Costs and latency also matter: running five reviews for every change is unlikely to be practical for routine commits, and the approach may make the most sense for high-risk, wide-reaching edits like this one. Still, it captures something many engineers will recognize: the changes most likely to bite are the ones that pass every automated gate and look correct at a glance. Inviting multiple reviewers, even automated ones, appears to be a low-cost way to interrupt that false sense of certainty.