Qwen3-Coder-Next Technical Report

Ruisheng Cao, Mouxiang Chen, Jiawei Chen, Zeyu Cui, Feng YunLong, Binyuan Hui, Yuheng Jing, Kaixin Li, Mingze Li, Junyang Lin, Zeyao Ma, KaShun SHUM, Xuwu Wang, Jinxi Wei, Jiaxi Yang, zjj, Lei Zhang, Zongmeng Zhang, Wenting Zhao, Fan Zhou

Qwen3-Coder-Next Technical Report: 66 upvotes on Hugging Face Daily Papers, #5 of 43 papers on 2026-03-04. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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