Qwen3-Coder – A Code Generation Model Released by Alibaba Tongyi Qianwen

What is Qwen3-Coder

Qwen3-Coder is a powerful code generation model launched by Alibaba’s Tongyi Qianwen team. It features 480 billion parameters with 35 billion activated parameters and natively supports a 256K-token context window, extendable up to 1 million tokens. The model excels at tasks such as Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use, reaching the top performance levels among open-source models. Qwen3-Coder enhances its capabilities through large-scale reinforcement learning and long-horizon interaction training. It provides a command-line tool called Qwen Code and API interfaces, making it convenient for developers to use. Qwen3-Coder aims to assist software development by improving efficiency and reducing the human effort needed for complex tasks.

Main Features of Qwen3-Coder

Code Generation and Optimization: Generates high-quality code based on user’s natural language descriptions. Supports multiple programming languages including, but not limited to, Python, JavaScript, and Java. It can produce complex code logic such as functions, classes, and modules.
Agentic Coding: Autonomously plans and executes multi-step tasks, such as automatically invoking tools and running code tests during development. Supports interaction with external tools like browsers and APIs to accomplish complex tasks.
Long-Horizon Interaction: Uses multi-turn interactions to solve problems in real-world software engineering tasks, demonstrating excellent performance in benchmarks like SWE-Bench.
Context Extension: Natively supports a 256K-token context length, extended to 1 million tokens using YaRN technology, suitable for repository-level and dynamic data handling such as Pull Requests.
Multi-Tool Integration: Supports integration with various tools including Qwen Code, Claude Code, Cline, and others.

Technical Principles of Qwen3-Coder

Mixture-of-Experts (MoE) Model: Qwen3-Coder is a 480-billion-parameter mixture-of-experts model with 35 billion activated parameters. This design enables efficient computation on large-scale data while maintaining strong expressive power.
Large-Scale Pre-Training: Trained on 7.5 trillion tokens, with 70% being code data. The model learns rich programming patterns and language structures. Supports 256K-token context natively and is extended to 1 million tokens with YaRN technology to better handle repository-level and dynamic data.
Synthetic Data Enhancement: Cleans and rewrites low-quality data based on Qwen2.5-Coder outputs, significantly improving overall data quality and enhancing training effectiveness.
Reinforcement Learning (RL): In the post-training phase, large-scale reinforcement learning is applied by automatically expanding test samples and constructing high-quality training instances to significantly improve code execution success rates. Incorporates Long-Horizon RL to encourage multi-turn interactions, boosting performance in real software engineering tasks.

Project Links for Qwen3-Coder

Official Website: https://qwenlm.github.io/blog/qwen3-coder/
GitHub Repository: https://github.com/QwenLM/Qwen3-Coder
HuggingFace Model Hub: https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct

Application Scenarios for Qwen3-Coder

Code Generation and Automated Development: Quickly generate code prototypes supporting multiple languages to save development time and increase efficiency.
Agentic Coding: Autonomously plan and execute multi-step tasks, interacting with external tools to complete complex workflows.
Software Engineering Tasks: Assist in code review, optimization, test generation, and documentation writing to improve code quality and development process efficiency.
Education and Learning: Provide code examples and teaching support for beginners, helping them quickly grasp programming concepts and skills.
Enterprise Development: Rapidly develop internal tools and automation scripts to boost team productivity and accelerate project launch.