183 models ranked for code generation. Scored with heavy bonuses for large output (complete files), reasoning (correct logic), large context (project awareness), streaming, JSON mode, and function calling.
| # | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.7 (Fast)Anthropic | 95 |
| 2 | Claude Opus 4.7Anthropic | 95 |
| 3 | GPT-5.5OpenAI | 93 |
| 4 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 5 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 6 | GPT-5.4 ProOpenAI | 92 |
| 7 | GPT-5.4OpenAI | 92 |
| 8 | GPT-5.5 ProOpenAI | 91 |
| 9 | GPT-5.2 ProOpenAI | 91 |
| 10 | Claude Opus 4.6 (Fast)Anthropic | 90 |
| 11 | Claude Opus 4.6Anthropic | 90 |
| 12 | GPT-5.2-CodexOpenAI | 90 |
| 13 | GPT-5.2OpenAI | 90 |
| 14 | GPT-5.3-CodexOpenAI | 89 |
| 15 | GPT-5 ProOpenAI | 89 |
| 16 | Gemini 3 Flash PreviewGoogle | 88 |
| 17 | GPT-5.1-Codex-MaxOpenAI | 88 |
| 18 | GPT-5 CodexOpenAI | 88 |
| 19 | GPT-5OpenAI | 88 |
| 20 | GPT-5.1OpenAI | 87 |
| 21 | GPT-5.1-CodexOpenAI | 87 |
| 22 | GPT-5.1-Codex-MiniOpenAI | 87 |
| 23 | DeepSeek V4 ProDeepSeek | 87 |
| 24 | o3 Deep ResearchOpenAI | 87 |
| 25 | o3 ProOpenAI | 87 |
| 26 | o3OpenAI | 87 |
| 27 | Claude Sonnet 4.6Anthropic | 85 |
| 28 | Claude Opus 4.5Anthropic | 85 |
| 29 | Gemini 2.5 ProGoogle | 84 |
| 30 | Gemini 2.5 Pro Preview 06-05Google | 84 |
Describe what you need in plain language and get production-ready code. Large output models generate complete classes with methods, types, and documentation.
Generate entire project structures including routes, models, controllers, and configuration. Large context understands your existing codebase for consistent patterns.
Generate code in Python, TypeScript, Go, Rust, Java, and 20+ languages. Reasoning models understand language-specific idioms and best practices.
Complete partial functions, fill in TODO comments, and extend existing patterns. Streaming provides real-time code suggestions as you type.
Models scoring highest on coding benchmarks (SWE-bench, HumanEval) generate the most reliable code. Look for models with large output tokens (16K+) for complete implementations and reasoning capability for architecturally sound solutions.
Top models generate complete files, multi-file projects, and full-stack applications. Models with 16K+ output tokens produce entire components without truncation. For large projects, use models with big context windows to maintain consistency across files.
Python, JavaScript/TypeScript, and Go have the richest training data and produce the best results. Rust, Swift, and Kotlin are well-supported but may need more specific prompting. Niche languages (Haskell, Elixir) work best with the largest models.
Yes, with guardrails. Use AI for initial implementation and boilerplate, then review with tests and linting. Models with function calling integrate into IDE workflows and CI pipelines. The best results come from iterative prompting with test feedback.