Cheapest AI model for code review
Code review on PRs needs to fit the diff + surrounding context (1,500-5,000 input tokens) and generate detailed suggestions (300-1,000 output tokens). We ranked every model on 3,000 input + 600 output, a realistic mid-sized PR review.
Ranked cheapest first
| # | Model | Input $/M | Output $/M | Per 1M calls |
|---|---|---|---|---|
| #1 | GPT-5 Nano OpenAI |
$0.05 | $0.40 | $390 |
| #2 | GPT-4.1 Nano OpenAI |
$0.10 | $0.40 | $540 |
| #3 | Gemini 2.5 Flash-Lite |
$0.10 | $0.40 | $540 |
| #4 | Llama 3.1 8B Meta |
$0.18 | $0.18 | $648 |
| #5 | GPT-4o mini OpenAI |
$0.15 | $0.60 | $810 |
| #6 | GPT-5.4 Nano OpenAI |
$0.20 | $1.25 | $1,350 |
| #7 | DeepSeek V3 DeepSeek |
$0.27 | $1.10 | $1,470 |
| #8 | Gemini 3.1 Flash-Lite |
$0.25 | $1.50 | $1,650 |
| #9 | GPT-5 Mini OpenAI |
$0.25 | $2.00 | $1,950 |
| #10 | GPT-4.1 Mini OpenAI |
$0.40 | $1.60 | $2,160 |
| #11 | Llama 3.1 70B Meta |
$0.59 | $0.79 | $2,244 |
| #12 | Gemini 2.5 Flash |
$0.30 | $2.50 | $2,400 |
| #13 | DeepSeek V3.1 DeepSeek |
$0.60 | $1.70 | $2,820 |
| #14 | Qwen 2.5 Coder 32B Alibaba |
$0.80 | $0.80 | $2,880 |
| #15 | Llama 3.3 70B Meta |
$0.88 | $0.88 | $3,168 |
| #16 | Qwen 2.5 72B Alibaba |
$0.90 | $0.90 | $3,240 |
| #17 | Gemini 3 Flash |
$0.50 | $3.00 | $3,300 |
| #18 | GPT-5.4 Mini OpenAI |
$0.75 | $4.50 | $4,950 |
| #19 | o3-mini OpenAI |
$1.10 | $4.40 | $5,940 |
| #20 | o4-mini OpenAI |
$1.10 | $4.40 | $5,940 |
| #21 | Claude Haiku 4.5 Anthropic |
$1.00 | $5.00 | $6,000 |
| #22 | GLM-5.1 zhipu |
$1.40 | $4.40 | $6,840 |
| #23 | Qwen3 Coder 480B Alibaba |
$2.00 | $2.00 | $7,200 |
| #24 | Mistral Large Mistral |
$2.00 | $6.00 | $9,600 |
| #25 | GPT-5.1 OpenAI |
$1.25 | $10.00 | $9,750 |
| #26 | GPT-5 OpenAI |
$1.25 | $10.00 | $9,750 |
| #27 | Gemini 2.5 Pro |
$1.25 | $10.00 | $9,750 |
| #28 | GPT-4.1 OpenAI |
$2.00 | $8.00 | $10,800 |
| #29 | o3 OpenAI |
$2.00 | $8.00 | $10,800 |
| #30 | Llama 3.1 405B Meta |
$3.50 | $3.50 | $12,600 |
| #31 | Gemini 3.1 Pro |
$2.00 | $12.00 | $13,200 |
| #32 | DeepSeek R1 DeepSeek |
$3.00 | $7.00 | $13,200 |
| #33 | GPT-4o OpenAI |
$2.50 | $10.00 | $13,500 |
| #34 | GPT-5.3 OpenAI |
$1.75 | $14.00 | $13,650 |
| #35 | GPT-5.2 OpenAI |
$1.75 | $14.00 | $13,650 |
| #36 | GPT-5.4 OpenAI |
$2.50 | $15.00 | $16,500 |
| #37 | Claude Sonnet 4.6 Anthropic |
$3.00 | $15.00 | $18,000 |
| #38 | Claude Opus 4.8 Anthropic |
$5.00 | $25.00 | $30,000 |
| #39 | GPT-5.5 OpenAI |
$5.00 | $30.00 | $33,000 |
| #40 | GPT-4 Turbo OpenAI |
$10.00 | $30.00 | $48,000 |
| #41 | Claude Opus 4.8 (Fast Mode) Anthropic |
$10.00 | $50.00 | $60,000 |
| #42 | o3-pro OpenAI |
$20.00 | $80.00 | $108,000 |
| #43 | GPT-5 Pro OpenAI |
$15.00 | $120.00 | $117,000 |
| #44 | GPT-5.2 Pro OpenAI |
$21.00 | $168.00 | $163,800 |
| #45 | GPT-5.5 Pro OpenAI |
$30.00 | $180.00 | $198,000 |
| #46 | GPT-5.4 Pro OpenAI |
$30.00 | $180.00 | $198,000 |
Workload assumption: 3,000 input tokens + 600 output tokens per call, scaled to 1M calls. Pricing as of 2026-05-31.
How we computed this
The 3,000-input figure covers a 200-line diff plus the surrounding function context a reviewer needs to judge it, and 600 output covers 3-6 substantive review comments with code suggestions. Team-scale math matters here: a 20-engineer team merging 15 PRs a day runs about 450 reviews a month, so even the priciest model in this table costs single-digit dollars monthly at that volume. This is the rare workload where the cost table almost does not matter below a few thousand PRs a day, which is why the quality caveats below should outweigh the ranking for most teams.
The math, worked through
One call at this workload costs GPT-5 Nano $0: 3,000 input tokens at $0.05 per million is $0, plus 600 output tokens at $0.40 per million is $0. At 10,000 calls a day that is $117 a month. The third-place model, Gemini 2.5 Flash-Lite, runs 1.4x that. The most expensive model in the table, GPT-5.4 Pro, costs 508x the winner at the same workload: the spread between top and bottom of this ranking is not a rounding error, it is the difference between a tool budget and a headcount budget.
About the winner
Code review is a reasoning-heavy task wearing a cheap-workload costume. The models at the top of this table will catch syntax errors and obvious bugs but miss race conditions, subtle API misuse, and security issues, which are the bugs a review bot exists to catch. Most teams should pick from the frontier tier here and treat the table as a floor, not a recommendation.
When not to pick the cheapest
Avoid the cheapest tier if reviews gate merges (false confidence on a bad approval is expensive) or if your codebase uses less-common languages where budget models have thinner training coverage. Also cap output length in your prompt: review bots that ramble produce 2,000-token comments nobody reads, quadrupling output cost for negative value.
How to use this ranking
The winner is mathematically cheapest at the listed workload shape — that's not the same as "best for the use case." Cheaper models often have lower reasoning depth, smaller context windows, or worse instruction-following. Use this as the cost baseline, then test the top 2-3 candidates on your real prompts via the live counter.
Pricing snapshots come from each provider's published rate cards and are tracked in the full pricing changelog. Tokenizer accuracy per model is documented in the methodology.