Top picks for Bug Fixing (2026)
Diagnosing root cause and producing a working patch. Ranked from 340 live models on the OpenRouter catalog, weighted for reasoning quality, tool calling, context window.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 194 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 193 | $5.00 | $25.00 | 1,000,000 | Details → |
| 3 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 192 | $5.00 | $25.00 | 1,000,000 | Details → |
| 4 | OpenAI: GPT-5openai/gpt-5 | 191 | $1.25 | $10.00 | 400,000 | Details → |
| 5 | OpenAI: o3openai/o3 | 174 | $2.00 | $8.00 | 200,000 | Details → |
| 6 | DeepSeek: DeepSeek V3deepseek/deepseek-chat | 158 | $0.20 | $0.80 | 131,072 | Details → |
| 7 | OpenAI: GPT-4.1openai/gpt-4.1 | 155 | $2.00 | $8.00 | 1,047,576 | Details → |
| 8 | Google: Gemini 2.5 Progoogle/gemini-2.5-pro | 151 | $1.25 | $10.00 | 1,048,576 | Details → |
| 9 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 145 | $0.30 | $2.50 | 1,048,576 | Details → |
| 10 | Anthropic: Claude Sonnet 4anthropic/claude-sonnet-4 | 143 | $3.00 | $15.00 | 1,000,000 | Details → |
| 11 | OpenAI: o4 Mini Highopenai/o4-mini-high | 141 | $1.10 | $4.40 | 200,000 | Details → |
| 12 | OpenAI: o3 Proopenai/o3-pro | 141 | $20.00 | $80.00 | 200,000 | Details → |
| 13 | OpenAI: o3 Mini Highopenai/o3-mini-high | 138 | $1.10 | $4.40 | 200,000 | Details → |
| 14 | OpenAI: o3 Miniopenai/o3-mini | 137 | $1.10 | $4.40 | 200,000 | Details → |
| 15 | Meta: Llama 4 Maverickmeta-llama/llama-4-maverick | 137 | $0.15 | $0.60 | 1,048,576 | Details → |
How we ranked these
For Bug Fixing, we weight models on reasoning quality, tool calling, context window. 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 Bug Fixing
Bug fixing is the process of identifying the root cause of a software defect and writing a patch that resolves it without introducing new failures. You need this when code is broken in production, tests are failing, or behavior doesn't match specification. A strong model traces execution flow, cross-references error messages with code context, and proposes minimal, testable changes. Weak models generate speculative fixes that don't address the actual problem or miss side effects. The main tradeoff: models that request full codebase context are more accurate but slower and more expensive than those working from stack traces and isolated snippets.
When to use: Use this when your code is crashing, returning wrong results, or failing tests, and you need an AI to analyze logs and source code to find and fix the problem quickly.
Common questions
What is the difference between a model good at bug fixing versus one that just rewrites code?
A model good at bug fixing traces the actual execution path, connects error messages to their causes in the code, and makes surgical repairs. One that just rewrites code may reformat working sections or "fix" something that wasn't broken. Claude and GPT-4 excel at this because they maintain context across large files and reason about side effects; cheaper models often miss the actual failure point.
How much faster is AI bug fixing compared to manual debugging?
For straightforward bugs with clear error messages, AI can propose a fix in seconds versus 10-30 minutes of manual trace work. Complex bugs involving state corruption or race conditions still require human verification and may take longer overall. Speed improves most when you provide complete logs, stack traces, and the relevant code section upfront.