Alibaba's open-weight agentic coding model, tuned for multi-file reasoning, tool use, and long-horizon autonomous programming.
Qwen3-Coder is Alibaba's flagship open-weight model purpose-built for agentic coding. Trained with reinforcement learning on real repo tasks, the 480B-A35B mixture-of-experts variant matches or beats proprietary models on SWE-bench Verified and handles multi-file refactors, tool use, and long-context agent loops natively. The open weights mean you can run it locally, fine-tune it, or serve it inside your own infrastructure without per-token fees.
Who it's for: Teams building private code assistants, Researchers studying agentic coding, Engineers wanting to fine-tune on internal codebases, Anyone avoiding per-token AI costs
Scores competitive with Claude and GPT-5 on SWE-bench Verified, the gold-standard benchmark for engineering agents.
Sparse mixture-of-experts activates 35B parameters per token — high capability without dense-model cost.
Apache 2.0 license lets you self-host, fine-tune, or distill for your own use cases with no vendor lock-in.
Trained on agentic trajectories for shell commands, file edits, and web lookups as part of the reasoning loop.
Native support for long contexts, extensible to 1M with RoPE scaling for repo-scale reasoning.
Works out of the box with Claude Code, Cline, Aider, and Cursor's open protocols.
Qwen3-Coder is the open-weight answer to proprietary frontier coding models and the right default for teams that want to self-host or fine-tune. If you are building an internal code assistant, training on proprietary code, or just want to escape per-token pricing, Qwen3-Coder is the strongest foundation available in 2026.