
Built on top of Qwen3-Next-80B-A3B-Base, which adopts a novel architecture with hybrid attention and MoE, Qwen3-Coder-Next has been agentically trained at scale on large-scale executable task synthesis, environment interaction, and reinforcement learning, obtaining strong coding and agentic capabilities with significantly lower inference costs.

Features
- Ultra-efficient inference: 80B total parameters, 3B active per token. Runs on consumer hardware with quantization.
- 256K native context: Full repository-scale understanding without chunking or retrieval hacks.
- Agentic training: Trained on 800K executable tasks with environment interaction and reinforcement learning—not just static code-text pairs.
- Tool calling: Works with coding agents like Claude Code, Qwen Code, Cline, and OpenCode out of the box.
- Non-thinking mode only: Fast responses without
<think></think> blocks.
Benchmarks
