Qodo Compares LLMs for Code Review: Bugs Found, Noise, and Cost per Review

Frontier models now ship almost every month, and each one shifts the balance between quality, price, and speed. On October 12, 2026, Qodo, a company that builds products for code review and governance, published a blog post titled "Which LLM should review your code?". In it, the company compares models on three parameters: how many bugs a model finds, how much noise it creates, and how much a single review costs. The available announcement contains no final numbers or ranking, so below we walk through the framing of the question itself and what matters in it.

Why code review

Review is a good fit for comparing models because the trade-offs show up immediately. A missed bug reaches production and is expensive. Superfluous comments waste developers' time and, over time, teach them to ignore the bot. The token bill grows with the size of the pull request and the depth of the model's reasoning. Chat benchmarks don't give this picture: they have no cost of error and no live team that has to trust the result.

The task also has a verifiable outcome. A bug was either in the diff or it wasn't, and a comment was either accepted by the author or rejected.

Three axes to compare on

Qodo puts three measures at the center. Speed, which the announcement mentions separately, is added as a fourth.

• Recall: what share of real defects is found. A breakdown by type matters here: logic errors, security, edge cases, violations of the team's own rules.
• Precision (noise): how many comments are useful and how many are meaningless, duplicate the linter, or concern style. A model that finds more bugs but produces three times as much junk can lose out to a more cautious one.
• Review cost: the price of one pass on a typical PR, including input context and reasoning tokens. For a team with hundreds of PRs a week, a several-fold difference in price per review quickly becomes a noticeable budget line.
• Speed: time to comments. A review that arrives after 20 minutes fits into the workflow worse than one that is ready in two.

The main difficulty is that the axes pull in different directions. A more powerful model usually finds more, but costs more and runs slower. A cheap, fast model saves budget and more often misses subtle errors.

What the announcement doesn't include

The RSS excerpt cuts off in the introduction. We don't know which models took part, what set of PRs and bugs the test ran on, how a "found bug" was defined, or what reasoning settings were used. All of this strongly affects the conclusions. In addition, Qodo sells a review product, which means it has an interest in making its methodology look convincing. That is not a reason to distrust the data, but it is a reason to check the methodology in the full version of the post once you get to the tables.

How to choose a model for your team

Someone else's ranking gives you a starting point, not an answer. In practice, the following order works.

1. Collect 50–100 of your own closed PRs in which the bugs are already known: from postmortems, rollbacks, hotfix commits. This is your test set.

2. Run them through 2–3 models with the same prompt and context. Count the bugs found, the share of accepted comments, and the total cost.

3. Consider a two-tier setup: an inexpensive model looks at all PRs, and a strong one is brought in for risky changes, such as authorization, payments, and migrations. 4.

Revisit the choice once a quarter or when a notable release comes out. If your pipeline isn't tied to a single model, switching takes hours, not weeks. 5.

Track the share of rejected comments. If it grows, the team starts ignoring the bot, and that matters more than any line in a benchmark.

Qodo's full report is worth reading for its methodology and specific numbers. The decision about which model will review your code is best made based on results from your own repository.