Best AI Models for Coding
Choosing a coding model takes more than a benchmark score. What matters is how it handles your repository, your language and framework, your review process, and your data requirements.
Claude (Anthropic)
Agentic codingAnthropic positions Claude Opus 5.5 for long-running agentic coding and knowledge work, and Sonnet 5.5 as the best combination of speed and intelligence. All current models list a 1M-token context window and 128K maximum output.
GPT (OpenAI)
Reasoning and codingOpenAI recommends GPT-6 Astra as its flagship for complex reasoning and coding, with a 1.05M-token context window and 128K maximum output listed for the GPT-6 line.
Gemini (Google)
Very long outputsGoogle describes Gemini 4 Argon as its flagship reasoning model, with a 1M-token context window and outputs up to 1M tokens, which suits tasks that generate very large files in a single pass.
Open-weights models
Self-hostedSeveral labs publish capable open-weights models you can run on your own infrastructure, including DeepSeek, Qwen, Kimi, GLM, and Mistral. Licenses differ, so confirm that yours permits your use before deploying.
What to test on your own repository
- A multi-file change that touches shared types or interfaces.
- A bug that needs reading several modules to diagnose.
- A refactor to a framework version your team actually uses.
- Following a strict instruction, such as “return the whole file” or “do not change the public API.”
Run each model several times and compare diffs, not impressions.