Today, we're announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we're excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters which supports the context length of 256K tokens natively and 1M tokens with extrapolation methods, offering exceptional performance in both coding and agentic tasks. Qwen3-Coder-480B-A35B-Instruct sets new state-of-the-art results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use, comparable to Claude Sonnet 4.
Alongside the model, we're also open-sourcing a command-line tool for agentic coding: Qwen Code. Forked from Gemini Code, Qwen Code has been adapted with customized prompts and function calling protocols to fully unleash the capabilities of Qwen3-Coder on agentic coding tasks. Qwen3-Coder works seamlessly with the community’s best developer tools. As a foundation model, we hope it can be used anywhere across the digital world — Agentic Coding in the World!
There’s still room to scale in pretraining—and with Qwen3-Coder, we’re advancing along multiple dimensions to strengthen the model’s core capabilities:
Scaling Tokens: 7.5T tokens (70% code ratio), excelling in coding while preserving general and math abilities.
Scaling Context: Natively supports 256K context and can be extended up to 1M with YaRN, optimized for repo-scale and dynamic data (e.g., Pull Requests) to empower Agentic Coding.
Scaling Synthetic Data: Leveraged Qwen2.5-Coder to clean and rewrite noisy data, significantly improving overall data quality.
In real-world software engineering tasks like SWE-Bench, Qwen3-Coder must engage in multi-turn interaction with the environment, involving planning, using tools, receiving feedback, and making decisions. In the post-training phase of Qwen3-Coder, we introduced long-horizon RL (Agent RL) to encourage the model to solve real-world tasks through multi-turn interactions using tools. The key challenge of Agent RL lies in environment scaling. To address this, we built a scalable system capable of running 20,000 independent environments in parallel, leveraging Alibaba Cloud's infrastructure. The infrastructure provides the necessary feedback for large-scale reinforcement learning and supports evaluation at scale. As a result, Qwen3-Coder achieves state-of-the-art performance among open-source models on SWE-Bench Verified without test-time scaling.
Qwen Code is a research-purpose CLI tool adapted from Gemini CLI, with enhanced parser and tool support for Qwen-Coder models.
Qwen Code supports the OpenAI SDK when calling LLMs, and you can export the following environment variables or simply put them under the .envfile .
Now enjoy your vibe coding with Qwen-Code and Qwen, by simply typing: qwen!
In addition to Qwen Code, you can now use Qwen3‑Coder with Claude Code. Simply request an API key on Alibaba Cloud Model Studio platform and install Claude Code to start coding.
We have provided two entrypoints for seamlessly experiencing coding with Qwen3-Coder.
通义千问 这条官方动态围绕「Qwen 发布 Qwen3-Coder 编程模型」展开，英文标题为 “Qwen3-Coder: Agentic Coding in the World”。正文重点落在智能体工作流、工具调用和任务执行稳定性，需要结合官方发布内容理解它对模型使用和开发者接入的影响。
对用户来说，这类信息最有价值的部分是判断新能力是否已经可用、适合哪些任务，以及调用时可能受到哪些版本、地区、权限或产品形态限制。
放到 AI API 中转站评测场景，重点要看服务商是否真实支持相关模型或能力，模型名称、返回行为、延迟、错误信息、上下文限制和价格说明是否能相互印证。
后续自测时可以围绕「Qwen 发布 Qwen3-Coder 编程模型」设计更具体的探针任务：复杂提示词、连续对话、工具调用、多模态输入或代码任务都能帮助区分真实能力和只写在页面上的模型列表。