Qwen3-Coder - Ali Tongyi Qianqian open source code generation model

What is Qwen3-Coder

Qwen3-Coder is an advanced code generation model introduced by Ali Tongyi Qianqian team. The model has 480B parameters and 35B activation parameters, and supports native 256K token The model is based on a hybrid expert architecture, optimized for large-scale pre-training and reinforcement learning, and generates high-quality code based on natural language descriptions, supporting a variety of programming languages, such as Python, JavaScript, and Java, etc. Qwen3-Coder has agent-based programming capabilities, and is able to autonomously plan multi-step tasks and interact with external tools. Qwen3-Coder provides command line tools and API interfaces for code generation, automated development, software engineering tasks, and education and learning scenarios, significantly improving development efficiency and reducing the labor burden of complex tasks.

Main Features of Qwen3-Coder

• Code Generation and Optimization: Quickly generate high-quality code based on user-supplied natural language descriptions. The model supports a variety of mainstream programming languages, such as Python, JavaScript and Java, etc. It supports the generation of simple code snippets, and is capable of generating complex code logic, such as functions, classes and modules.
• Proxy Programming: Have the ability to autonomously plan and execute multi-step tasks, such as automatically invoking tools and executing code tests during the development process. Support interaction with external tools (e.g., browsers, APIs, etc.) to accomplish complex tasks.
• long time series interaction: In real-world software engineering tasks, Qwen3-Coder supports multiple rounds of interactive problem solving, and excels in tasks such as SWE-Bench, for example, which is suitable for dealing with complex problems requiring multi-step solutions.
• context extension (computing): Native support for context lengths of 256K tokens, scaled to 1M tokens with YaRN technology, suitable for processing repository-level and dynamic data (e.g., Pull Requests).
• Multi-tool integration: Supports integration with a variety of tools (e.g., Qwen Code, Claude Code, Cline, etc.), making it easy for developers to use it in different development environments.

Qwen3-Coder's official website address

• Project website:: https://qwenlm.github.io/blog/qwen3-coder/
• GitHub repository:: https://github.com/QwenLM/Qwen3-Coder
• HuggingFace Model Library:: https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct

How to use Qwen3-Coder

• Command Line Tool Qwen Code Usage::
  • Installing Node.js(Ensure version 20 and above):

curl -qL https://www.npmjs.com/install.sh | sh

• • Installation of Qwen Code::

npm install -g @qwen-code/qwen-code

• • or install from source::

git clone https://github.com/QwenLM/qwen-code.git
cd qwen-code && npm install && npm install -g .

• • Configuring Environment Variables(Call LLM using the OpenAI SDK):

export OPENAI_API_KEY="your_api_key_here"
export OPENAI_BASE_URL="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
export OPENAI_MODEL="qwen3-coder-plus"

• • Using Qwen Code::

qwen "生成一个简单的 Python 函数，计算两个数的和。"

• Claude Code Usage::
  • Installation of Claude Code::

npm install -g @anthropic-ai/claude-code

• • Configuring Environment Variables(using the proxy API provided by DashScope):

export ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/api/v2/apps/claude-code-proxy
export ANTHROPIC_AUTH_TOKEN=your-dashscope-apikey

• • Using Claude Code::

claude "生成一个简单的 Python 函数，计算两个数的和。"

• Cline Usage::
  • Select "OpenAI Compatible" mode.
  • In the OpenAI Compatible API tokens Enter the key obtained from Dashscope.
  • Check "Use custom base URL" and enter:https://dashscope-intl.aliyuncs.com/compatible-mode/v1The
  • Enter the model name:qwen3-coder-plusThe

• API Calls: If you need to integrate Qwen3-Coder in your program, call it via the API.

import os
from openai import OpenAI

# 配置 API 客户端
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

# 发送请求
prompt = "生成一个简单的 Python 函数，计算两个数的和。"
completion = client.chat.completions.create(
    model="qwen3-coder-plus",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ],
)

# 输出结果
print(completion.choices[0].message.content.strip())

Qwen3-Coder's Core Advantages

• Powerful code generation capabilities: Quickly generate high-quality, runnable code based on natural language descriptions, support multiple programming languages, and reduce manual coding effort.
• Excellent agent-based programming performance: Ability to autonomously plan and execute multi-step tasks and interact with external tools (e.g., browsers, APIs, etc.) to accomplish complex tasks.
• Large-scale context supportNative support for 256K token contexts, and up to 1M tokens based on scaling technology, suitable for processing warehouse-level and dynamic data.
• Enhanced learning-driven optimization: Improving code execution success through large-scale reinforcement learning to optimize code quality and task completion results.
• Flexible tool integrationThe command line tools, API interfaces, and support for seamless integration with existing development tools (e.g., Claude Code, Cline, etc.) make it easy to integrate into the development process.
• Efficient data processing capabilities: Extending with synthetic data and pre-training with high-quality data to improve the model's understanding and generation of different programming patterns.
• Multi-language and multi-platform support: Supports multiple programming languages and cross-platform development, adapting to different development environments and project needs.
• Improve development efficiency: Based on automated code generation, test case generation, and documentation generation, it significantly reduces development time and improves the overall efficiency of the team.

People for whom Qwen3-Coder is suitable

• Professional Software Developer: Rapidly generate code prototypes, optimize code quality, improve development efficiency, support multiple languages and platforms.
• Beginners and programming enthusiasts: Provide code samples and learning aids to lower the programming learning threshold and help with hands-on projects.
• Enterprise Development Team: Rapidly develop in-house tools to generate automation scripts to improve the overall efficiency of the team.
• Researchers and algorithm developers: Rapid implementation and testing of new algorithms to fuel innovative projects and technical research.