Qwen3-Coder-Next Technical Report
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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Ruisheng Cao
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Mouxiang Chen
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Jiawei Chen
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Zeyu Cui
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Yunlong Feng
claimed_verified
2026-03-09T10:38:14.089Z
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Feng YunLong
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ylfeng
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Binyuan Hui
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Yuheng Jing
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Kaixin Li
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Mingze Li
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Junyang Lin
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Zeyao Ma
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Kashun Shum
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Xuwu Wang
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Jinxi Wei
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Jiaxi Yang
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Jiajun Zhang
claimed_verified
2026-03-05T09:59:39.783Z
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Lei Zhang
claimed_verified
2026-03-23T11:00:33.604Z
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Lei Zhang
Hambaobao
user
Hambaobao
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Zongmeng Zhang
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Wenting Zhao
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Fan Zhou
Qwen3-Coder-Next Technical Report
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.
Qwen3-Coder-Next is an 80-billion-parameter language model that activates only 3 billion parameters during inference, achieving strong coding capabilities through agentic training with verifiable task synthesis and reinforcement learning.
language model parameter-efficient fine-tuning agentic training verifiable coding tasks executable environments mid-training reinforcement learning SWE-Bench Terminal-Bench