Qwen3-Coder-Next Technical Report

• URL: http://arxiv.org/abs/2603.00729v1
• Date: Sat, 28 Feb 2026 16:25:04 GMT
• Title: Qwen3-Coder-Next Technical Report
• Authors: Ruisheng Cao, Mouxiang Chen, Jiawei Chen, Zeyu Cui, Yunlong Feng, Binyuan Hui, Yuheng Jing, Kaixin Li, Mingze Li, Junyang Lin, Zeyao Ma, Kashun Shum, Xuwu Wang, Jinxi Wei, Jiaxi Yang, Jiajun Zhang, Lei Zhang, Zongmeng Zhang, Wenting Zhao, Fan Zhou,
• Abstract summary: We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents.<n>Qwen3-Coder-Next activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference.
• Score: 67.90974638938285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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