Cost to Review 100 Pull Requests with LLM APIs
Calculate the real cost of using LLM APIs for automated code review. Compare all models for reviewing 100 pull requests with verified per-million-token pricing.
⚡ Your Workload
📊 Cost Summary
Cost per pull requests across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | Cost for 100 pull requests |
|---|
How this calculator works
Each pull request review requires ~8,000 input tokens (the diff, surrounding code context, and review instructions) and ~1,000 output tokens (review comments, suggestions, and summary). Larger PRs will proportionally increase input tokens. Code review is input-heavy because the model must understand the full context of changes.
Formula: cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × quantity / 1,000,000
All prices are per million tokens, sourced directly from official provider pricing pages and verified by our automated scraper pipeline that runs 3× daily. No fabricated numbers — every price links to its source.
Frequently asked questions
How much does it cost to review 100 pull requests with an LLM?
Reviewing 100 PRs with an LLM costs $3-10 with budget models, $20-50 with mid-tier models, and $80-200+ with frontier models. Code review is input-token-heavy (reading diffs and context), so models with cheap input pricing are most cost-effective.
Which LLM is best for automated code review?
For cost-effective code review, DeepSeek V3 and Gemini Flash offer the lowest cost. For review quality (catching subtle bugs, suggesting architectural improvements), Claude Sonnet and GPT-4.1 are significantly better but cost 5-10x more per PR.
How are code review token costs calculated?
Each PR review uses ~8,000 input tokens (diff + context) and ~1,000 output tokens (review comments). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_PRs. Prices are per million tokens from official provider pricing.