Claude, GPT or Gemini for AI Code Review? How to Choose a Model Per Pull Request

Every team that brings its own keys to an AI code reviewer eventually asks the same question in a planning meeting: which model should actually read our pull requests? Claude, GPT or Gemini? The question is reasonable, and the answer most vendors give, “ours, obviously, or whichever is top of this week’s leaderboard”, is not. This post lays out how to think about the choice, why it is a routing decision rather than a loyalty decision, and how to run the comparison on your own code instead of trusting anyone’s benchmark, including ours.

We will not tell you that one provider’s model is the best reviewer. We do not know that for your codebase, and neither does anyone who has not run it on your pull requests. What we can do is describe the dimensions that differ between models in practice, and show how PURA is built so you never have to pick just one.

Why “which model is best” is the wrong question

Code review is not one task. A review of a payments change is a careful, slow read where a missed edge case costs real money. A review of a dependency bump is a glance: did the lockfile change match the manifest, did anything else sneak in. A review of a 2,000-line refactor is mostly about whether behaviour was preserved. Those three jobs want different amounts of reasoning, different context windows and very different budgets, and a single model that is right for all three is almost certainly overpaying on two of them.

The second reason is that the ranking moves. Each provider ships new models several times a year, prices change, and the model that was clearly strongest at tracing control flow six months ago may now be merely expensive. Any decision that hard-codes one model into your review pipeline has a shelf life measured in months. A decision that encodes which kinds of changes deserve which tier of model survives every release, because you can swap the model behind a tier without touching the rules.

The dimensions that actually differ

When teams compare models on their own PRs, the differences they notice tend to fall into a small number of buckets. None of them is a benchmark score.

Depth of reasoning on risky diffs

The top-tier model from each provider is noticeably better than its smaller siblings at the reviews that matter most: following a value through three files to find the place it is used unsafely, noticing that a migration drops a column something else still reads, spotting that an error path leaks a resource. The smaller and faster models are not useless here, but they find the obvious issues and miss the ones that take several inferential steps. If your reviewer only ever runs a lean model, you are buying the cheap half of the value.

Context window and how it is used

Large pull requests, monorepo changes and cross-repo context all push on the context window. Providers differ in how much a model can take in one call and, more importantly, in how well it uses the back half of a long context. A reviewer that has the whole diff plus the files it touches is in a different position from one that saw a truncated slice; our post on why diff-only reviewers miss real bugs covers why that matters more than raw model quality.

Instruction following

A review tool is only as good as its ability to follow your rules: ignore generated files, treat anything under payments/ as high risk, never comment on formatting. Models differ in how reliably they obey long, specific instruction sets, and the difference shows up as noise. A model that finds one more real bug but also leaves eight style nitpicks you told it to skip is a net loss, because the team learns to scroll past it. We wrote about measuring that in reducing false positives.

Latency and cost per review

The top model from any provider costs many times what the lean one does per token, and a thorough review of a large PR can run to a lot of tokens. On a repository with hundreds of PRs a month, the difference between “top model on everything” and “top model where it counts” is the difference between a bill that gets questioned and one that does not. Latency matters less for review than for chat, but a model that takes several minutes on a small PR slows the merge queue in a way engineers feel.

Language and framework coverage

Models are not uniformly good across languages. The one that reviews TypeScript brilliantly may be weaker on Rust lifetimes or Terraform semantics. If your stack is unusual, this is worth testing directly, and it is one of the strongest arguments for being able to route by path: the infrastructure directory can go to whichever model handles HCL best, and the application code to another.

Route by risk, not by brand

Put those dimensions together and the sensible policy writes itself. Changes that are risky, subtle or expensive to get wrong go to a top-tier model. Routine product and backend work goes to a balanced model. Docs, typos, dependency bumps and generated code go to a lean model, or skip review entirely. Which provider sits behind each tier is a separate choice, and one you can revisit every time a provider ships something new.

This is how PURA is designed. You bring your own OpenAI, Anthropic or Google keys, and you write routing rules in plain English in a review skill: what counts as a payments change, which paths are infrastructure, which authors or labels mean “take this seriously”. PURA reads each PR against those rules and picks the model accordingly: a top model for risky changes, a cheap one for routine work. The rules match on changed files and the PR description, so the policy is readable by the whole team rather than buried in a config.

Because the keys are yours, the spend is visible per repo, team, model and provider, and you can put a ceiling on any of them. Our post on budgets by provider and model walks through setting a monthly cap for, say, the top-tier model so that a burst of large PRs cannot run the bill away, and bring-your-own-key code review covers why the key being yours changes the privacy and cost picture at the same time.

How to run the comparison on your own code

Do not pick a model from a blog post, including this one. Run a two-week comparison on real pull requests and let the results decide. The method is the same one we recommend for trialling any AI code reviewer, narrowed to the model question.

1. Pick twenty merged PRs with known outcomes. Include a few that shipped a bug that was later found, a few large refactors, a few trivial changes. You want to know whether each model catches the bugs you already know are there, and how much noise it adds on the changes where there was nothing to find.
2. Review each PR with each candidate model, same instructions. Route by label or path so the only variable is the model. Keep the review rules identical.
3. Score three things per review. Real findings (would a senior engineer have flagged this), false positives (comments the author would dismiss), and cost in tokens. Ignore style; count substance.
4. Compare by tier, not overall. The lean model will lose on the risky PRs and that is fine. The question is whether it is good enough on the routine ones to justify its price, and whether the top model finds enough extra on the risky ones to justify its price there.
5. Write the result into the routing rules. The output of the trial is not “we use model X”, it is a set of rules that say which changes get which tier. Then set a budget per provider and model so the trial’s cost assumptions are enforced rather than hoped for.

Two honest caveats

First, multi-model routing adds a small amount of operational surface: three keys to rotate instead of one, three providers whose status pages you care about, three billing relationships. For a two-person team on one repo it may not be worth it, and the right answer may be one balanced model for everything with a budget cap. We covered that trade-off in AI code review for small teams.

Second, the model is not the biggest lever. In our experience the quality of a review depends more on how much relevant context the reviewer is given, how specific the instructions are, and whether findings are ranked so the important ones come first, than on which provider’s flagship produced the text. Switching from a lean model to a top model on a diff-only reviewer with vague rules buys you a more eloquent miss. Fix the context and the rules first; then let the model comparison decide the rest.

If you want to run that comparison without building the routing yourself, PURA installs on GitHub in a few minutes, takes your own keys, and lets you write the rules in a SKILL.md file your whole team can read. The Starter plan is $39 a month for 100 reviews with a one-month trial, and the model behind each tier is yours to change whenever the leaderboard does.