Top picks for Code Review (2026)
Spotting bugs, security issues, and style problems in pull requests. Ranked from 334 live models on the OpenRouter catalog, weighted for reasoning quality, context window, structured output.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 184 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 184 | $5.00 | $25.00 | 1,000,000 | Details → |
| 3 | OpenAI: GPT-5.4openai/gpt-5.4 | 175 | $2.50 | $15.00 | 1,050,000 | Details → |
| 4 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 173 | $5.00 | $25.00 | 1,000,000 | Details → |
| 5 | Z.ai: GLM 5.2z-ai/glm-5.2 | 173 | $1.00 | $4.00 | 1,048,576 | Details → |
| 6 | DeepSeek: DeepSeek V4 Prodeepseek/deepseek-v4-pro | 171 | $0.43 | $0.87 | 1,048,576 | Details → |
| 7 | Google: Gemini 3.1 Pro Previewgoogle/gemini-3.1-pro-preview | 170 | $2.00 | $12.00 | 1,048,576 | Details → |
| 8 | OpenAI: GPT-5.5openai/gpt-5.5 | 170 | $5.00 | $30.00 | 1,050,000 | Details → |
| 9 | DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flash | 168 | $0.09 | $0.18 | 1,048,576 | Details → |
| 10 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 164 | $1.50 | $9.00 | 1,048,576 | Details → |
| 11 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 164 | $0.66 | $3.50 | 262,144 | Details → |
| 12 | MiniMax: MiniMax M3minimax/minimax-m3 | 162 | $0.30 | $1.20 | 1,048,576 | Details → |
| 13 | Xiaomi: MiMo-V2.5-Proxiaomi/mimo-v2.5-pro | 160 | $0.43 | $0.87 | 1,048,576 | Details → |
| 14 | Qwen: Qwen3.7 Maxqwen/qwen3.7-max | 160 | $1.25 | $3.75 | 1,000,000 | Details → |
| 15 | OpenAI: GPT-5.4 Miniopenai/gpt-5.4-mini | 159 | $0.75 | $4.50 | 400,000 | Details → |
How we ranked these
For Code Review, we weight models on reasoning quality, context window, structured output. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Code Review
Code review is the process of using an AI model to identify bugs, security vulnerabilities, style violations, and logic errors in source code before it ships. You need this task when pull requests arrive faster than your team can manually inspect them, or when you want consistent enforcement of security and style standards across a codebase. Good models at this task catch off-by-one errors, SQL injection vectors, and missing null checks while ignoring stylistic preferences your team doesn't care about. Bad models either flag false positives relentlessly (wasting reviewer time) or miss context-dependent bugs that require understanding the broader application flow. Speed matters here: a model that takes 90 seconds to review a 500-line PR will create bottlenecks in fast-moving teams, while one that responds in under 10 seconds stays integrated into CI/CD workflows.
When to use: Use this when your team receives more pull requests than developers can manually review in a reasonable timeframe, or when you want automated detection of common security flaws and coding mistakes before human review.
Common questions
Which AI models perform best at catching security bugs in code?
Claude 3.5 Sonnet and GPT-4 currently lead for security-focused code review because they understand context across multiple files and recognize subtle privilege escalation or injection patterns. For pure speed on simpler PRs, models like Llama 2 70B work adequately but miss more nuanced vulnerabilities.
How much does it cost to run code review on every pull request in a large repository?
Using Claude via API costs roughly $0.003-0.01 per standard PR depending on code size and model choice. For high-volume teams reviewing 50+ PRs daily, expect $5-25/month in model costs, which is negligible compared to preventing a single production security bug.