800亿参数模型仅激活30亿，高效实现强编码能力。
Qwen3-Coder-Next Technical Report
- 通过激活仅30亿参数实现高效推理，兼顾性能与效率。
- 在SWE-Bench等基准上表现媲美更大规模模型。
- 适合研究和开发高效编程代理的开发者使用。
我们提出 Qwen3-Coder-Next，一个专为编程代理设计的开源权重语言模型。该模型拥有800亿参数，但在推理时仅激活其中30亿参数，实现了强大编码能力与高效推理的平衡。本文探索了优质训练方法在小参数规模下所能达到的能力极限。为此，我们采用大规模可验证代码任务与可执行环境结合的代理训练方式，通过中段训练和强化学习直接从环境反馈中学习。在以SWE-Bench和Terminal-Bench为代表的代理中心型基准测试中，Qwen3-Coder-Next的表现与其激活参数量相当。我们公开发布基础版和指令微调版的开源权重，以支持科研及实际编程代理开发。
原文摘要 · Abstract (English)
We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference. In this work, we explore how far strong training recipes can push the capability limits of models with small parameter footprints. To achieve this, we perform agentic training through large-scale synthesis of verifiable coding tasks paired with executable environments, allowing learning directly from environment feedback via mid-training and reinforcement learning. Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Coder-Next achieves competitive performance relative to its active parameter count. We release both base and instruction-tuned open-weight versions to support research and real-world coding agent development.
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