Cost is a common concern when teams consider AI code review.
And that concern is fair. If every pull request goes through an AI model, the bill can grow quickly depending on the team’s review volume.
But when we analyzed the cost per PR across different models, we found a massive range: from $0.10 to $3.75 per PR for the same review workload.
That is nearly a 38x difference.
And it changes the question.
The question is not:
“Is AI code review worth the cost?”
A better question is:
“Which model makes sense for each type of review?”
Code review is heavily weighted toward input. The model needs to read a large amount of code and context to produce a relatively small response.
In our data, the ratio was roughly 14 input tokens for every output token.
So efficiency does not come from always choosing the most expensive model. It also does not come from minimizing costs by using the cheapest model for every task.
It comes from intelligent routing.
Use stronger models where deeper reasoning is required.
Use more affordable models for simpler, pattern-based checks.
Apply team-specific rules to reduce noise.
Provide the right context so tokens are not wasted processing irrelevant information.
AI code review scales more effectively when teams stop thinking in terms of one model for everything and start thinking about review architecture.
✨ The full data, including the cost comparison across models, is available in the research we published on AI code review in production: https://kodus.io/data/
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