181 models ranked for code refactoring. Scored with heavy bonuses for reasoning (understanding code intent), large context (full codebase analysis), large output (complete rewrites), streaming, and JSON mode.
| # | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.7Anthropic | 95 |
| 2 | GPT-5.5OpenAI | 93 |
| 3 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 4 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 5 | GPT-5.4 ProOpenAI | 92 |
| 6 | GPT-5.4OpenAI | 92 |
| 7 | GPT-5.5 ProOpenAI | 91 |
| 8 | GPT-5.2 ProOpenAI | 91 |
| 9 | Claude Opus 4.6 (Fast)Anthropic | 90 |
| 10 | Claude Opus 4.6Anthropic | 90 |
| 11 | GPT-5.2-CodexOpenAI | 90 |
| 12 | GPT-5.2OpenAI | 90 |
| 13 | GPT-5.3-CodexOpenAI | 89 |
| 14 | GPT-5 ProOpenAI | 89 |
| 15 | Gemini 3 Flash PreviewGoogle | 88 |
| 16 | GPT-5.1-Codex-MaxOpenAI | 88 |
| 17 | GPT-5 CodexOpenAI | 88 |
| 18 | GPT-5OpenAI | 88 |
| 19 | GPT-5.1OpenAI | 87 |
| 20 | GPT-5.1-CodexOpenAI | 87 |
| 21 | GPT-5.1-Codex-MiniOpenAI | 87 |
| 22 | DeepSeek V4 ProDeepSeek | 87 |
| 23 | o3 Deep ResearchOpenAI | 87 |
| 24 | o3 ProOpenAI | 87 |
| 25 | o3OpenAI | 87 |
| 26 | Claude Sonnet 4.6Anthropic | 85 |
| 27 | Claude Opus 4.5Anthropic | 85 |
| 28 | Grok 4.20xAI | 89 |
| 29 | Gemini 2.5 ProGoogle | 84 |
| 30 | Gemini 2.5 Pro Preview 06-05Google | 84 |
Migrate code between frameworks, update deprecated APIs, and convert legacy patterns to modern idioms. Large context models understand full project structure for consistent refactoring.
Reasoning models identify anti-patterns and suggest proper design patterns. They understand SOLID principles, DRY, and architectural boundaries.
Identify bottlenecks, optimize algorithms, reduce memory allocations, and improve query performance. Chain-of-thought explains each optimization decision.
Add TypeScript types to JavaScript, improve error boundaries, and strengthen validation. Large output models produce complete refactored files in one response.
Reasoning models detect code smells (long methods, deep nesting, duplicated logic, God classes), calculate complexity metrics, and prioritize refactoring targets by impact. Large context windows analyze entire modules to identify cross-cutting concerns.
Models generate refactoring plans with step-by-step transformations, ensuring each step is independently testable. They produce both the refactored code and updated tests. Reasoning ensures behavioral equivalence between old and new implementations.
Extract Method, Move Method, Replace Conditional with Polymorphism, and Introduce Parameter Object are well-handled. AI also excels at naming improvements, simplifying boolean logic, and converting callback patterns to async/await.
Models with large context windows (128K+) process multi-file refactoring including renaming across files, extracting shared utilities, reorganizing module structures, and updating import paths. They generate migration scripts for breaking changes.