Code generation — which AI model writes the best code?

Coding workloads have specific demands: long contexts (whole repos), stable tool-use loops for agents, low hallucination on API signatures, and enough output tokens to return entire files. Reasoning modes help with multi-step refactors but are too slow for inline completion.

Is this your use case?

·You're building a coding agent (Cursor Composer, Claude Code, autonomous PR generation).
·You need refactoring across multiple files, not just snippets.
·Output needs to be whole files, not 50 lines.
·You want tool calls (read file, run tests) reliable every turn.

What to look for

→At least 128k context — 200k+ preferred for repo reading.
→At least 16k output tokens so full files come back.
→Stable tool calling with low argument hallucination.
→Recent knowledge cutoff (2025+) for current framework APIs.

Recommended models

Best-of list, ordered by fit — not alphabetically.

1
anthropic/claude-sonnet-4.6
Top pick
Anthropic's workhorse: top-tier coding and agentic, fair pricing.
Top SWE-bench score (77.2%), 64k output tokens, extremely stable tool loops. The default for coding agents.
Model details
2
anthropic/claude-opus-4.6
Anthropic's premium tier for the hardest reasoning and coding tasks.
Even better on the hardest tasks (SWE-bench 79.4%) but pricier. Reach for it on critical migrations or security audits.
Model details
3
openai/gpt-5.4
OpenAI's new flagship — agentic workloads and deep reasoning chains.
400k context for monorepos, very strong tool use. Pricier per token than Sonnet but newer knowledge cutoff.
Model details
4
openai/gpt-5.4-mini
Best budget
GPT-5.4's smaller sibling — cheaper, faster, built for volume.
Inline completion and linter hints — fraction of the cost, same 400k context. Not for deep refactoring.
Model details
5
deepseek/deepseek-v3
DeepSeek-V3 — MoE model, impressive coding quality for very little money.
The dark horse: cheapest serious-quality coder. Compliance concerns for US/EU enterprise.
Model details