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Qwen3-Coder-Next Technical Report

Paper · 2026 · Ruisheng Cao, Mouxiang Chen, Jiawei Chen et al.

Summary

The report asks how much coding-agent capability a model with a small active parameter budget can reach if the training recipe is strong enough. Qwen3-Coder-Next is built on Qwen3-Next, which uses hybrid attention and a Mixture-of-Experts design. It has 80B total parameters, but only about 3B are used per token. The core idea is to scale agentic training on verifiable tasks that run inside executable environments, so the model learns from actual execution feedback.

Tasks come from two sources:

  • mining GitHub PRs and rebuilding them as Docker environments;
  • injecting bugs into repositories from existing executable datasets, such as SWE-Smith and SWE-Rebench.

A Kubernetes-based orchestrator called MegaFlow runs these tasks at scale. Training proceeds in stages: continued pretraining on code- and repository-heavy data, then supervised fine-tuning, then domain-expert models whose skills are distilled back into one model, with reinforcement learning also used.

The model is competitive on SWE-Bench and Terminal-Bench 2.0 with open models that use far more active compute. Both base and instruct weights are released, which makes it useful for building affordable, locally deployable coding agents.

Key points

  • Synthetic task scale: the bug-synthesis pipeline produced about 800K verifiable software-engineering tasks across more than nine programming languages. A bug is kept only if it breaks existing tests and reverting its patch fixes it.
  • Mid-training data:
    • programming-language coverage grew from 92 (Qwen2.5-Coder) to 370;
    • context length grew from 32,768 to 262,144 tokens;
    • repository-level data totals about 600B tokens.
  • Web-data rewriting: Qwen3-Coder-480B-A35B-Instruct was used to clean and restructure web documents. This raised EvalPlus from 54.38 to 63.09 and MultiPL-E from 36.02 to 48.35.
  • Code-editing and math results:
    • Aider-Polyglot score is 66.20, above Qwen3-Coder-480B-A35B (60.40) and Qwen3-Next (52.90);
    • on math, AIME25 rises from 69.64 for Qwen3-Next to 83.07;
    • general knowledge (MMLU-family) scores stay about the same as Qwen3-Next.
  • Stated limitations: the authors acknowledge a gap to Claude Opus 4.5 on large, complex software-engineering tasks. Other weak spots are frontend/UI work and needing more interaction turns on hard problems.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 worksComputer Science Applications · 102 works

Cite

Cite as 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 and Fan Zhou. 2026. Qwen3-Coder-Next Technical Report. arXiv:2603.00729.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2603.00729 · arXiv 2603.00729

@misc{cao2026qwen3,
  title = {{Qwen3-Coder-Next Technical Report}},
  author = {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 and Fan Zhou},
  year = {2026},
  doi = {10.48550/arxiv.2603.00729},
  eprint = {2603.00729},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2603.00729}
}

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