The Real Cost of AI Code Review: Token Usage Patterns Across PR Sizes

Why AI Code Review Costs Are Unpredictable

AI code review is one of the most common use cases for LLMs in engineering workflows — and one of the most variable in cost. A small bug fix review might cost $0.02. A large feature PR with extensive context might cost $2.00. If you are running automated AI review on every PR in a busy repository, that variance adds up fast.

The cost drivers are not just the diff size. The context you provide — repository structure, related files, test results, previous review comments — often contributes more tokens than the diff itself. Understanding the full token budget for a review request is the first step to controlling costs.

Token Usage by PR Size

Based on typical engineering workflows, here is how token consumption scales with PR size:

The input token count is dominated by context, not just the diff. A medium PR with 200 lines of changes might include 50K tokens of diff but another 100K tokens of related file context, test output, and system prompt. The context overhead is often 2–5x the diff size.

The Context Overhead Problem

Most AI code review tools include more context than necessary. Common sources of context bloat:

• Full file contents instead of relevant sections. If a PR changes 10 lines in a 500-line file, you do not need to send all 500 lines. Send the changed function plus 20 lines of surrounding context.
• Entire test suite output. Test results are useful context, but sending 10,000 lines of test output when only 5 tests are relevant is wasteful. Filter to relevant test results.
• Verbose system prompts. Review system prompts that include extensive coding guidelines, style guides, and examples can add 10,000–50,000 tokens per request. Cache these with prompt caching to reduce the effective cost to near zero.
• Unrelated file context. Some tools include all recently modified files as context. If those files are not related to the PR, they add tokens without adding value.

Model Selection for Code Review

Not all code review tasks require frontier models. A tiered approach based on PR complexity can reduce costs significantly:

• Tiny and small PRs: Claude Haiku 4.5 ($1/$5 per million) or DeepSeek V4 Flash ($0.14/$0.28) handles style checks, obvious bugs, and simple logic review adequately.
• Medium PRs: Claude Sonnet 4.6 ($3/$15) provides the right balance of quality and cost for most feature reviews.
• Large and XL PRs: Claude Opus 4.7 ($5/$25) or GPT-5.5 ($5/$30) for complex architectural changes where missing a subtle issue has high cost.

Routing tiny PRs to Haiku instead of Sonnet saves 67% per review. For a team merging 50 tiny PRs per month, that is $1.75–$3.50 saved per month — small individually, but meaningful at scale.

Predicting Your Monthly Review Costs

To estimate your monthly AI code review costs, you need three numbers: PRs per month by size category, average tokens per review by size, and your model selection. A team merging 200 PRs per month with a typical size distribution (60% small, 30% medium, 10% large) using Sonnet for all reviews would spend roughly:

• 120 small PRs × $0.15 average = $18
• 60 medium PRs × $0.50 average = $30
• 20 large PRs × $2.00 average = $40
• Total: ~$88/month

With tiered model routing (Haiku for small, Sonnet for medium, Opus for large) and prompt caching for the system prompt, that same team could reduce costs to roughly $35–$45/month — a 50–60% reduction. Use the AI Cost Estimator to model your specific review workflow and find the optimal cost-quality tradeoff.