sophnet/Qwen3-Coder

Qwen3-Coder is Alibaba Tongyi’s flagship code model, primarily positioned as an “open-source programming agent with strong agent capabilities.” It leverages the MoE architecture to achieve efficient code generation and handle complex tasks, delivering performance on par with top closed-source models and well-suited for professional development and agent-based programming scenarios.

• Advantages of the MoE Architecture: The flagship version, Qwen3-Coder-480B-A35B-Instruct, boasts a total parameter count of 480 billion and an active parameter count of 35 billion, striking a balance between large-scale parameter performance and computational efficiency.
• Ultra-long Context Support: Natively supports 256K tokens, expandable up to 1M via YaRN, making it ideal for handling long texts such as repository-level code and PR dynamic data.
• Industry-Leading Task Performance: Achieves open-source SOTA results in tasks including Agentic Coding (SWE-bench Verified 69.6%), Browser-Use (WebArena 49.9%), and Tool-Use (BFCL-v3 68.7%), rivaling Claude Sonnet4.
• Specialized Training Optimization: Trained on 7.5T pre-training data (with code accounting for 70%), followed by post-training using “hard-to-solve, easy-to-verify” Code RL, automatically expanding test cases to improve code execution rates.
• Rich Tool Ecosystem: Offers the open-source Qwen Code CLI tool (compatible with Node.js 20+), supporting integration with Claude Code, Cline, and others, as well as compatibility with OpenAI SDK and Alibaba Cloud DashScope API.

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Core Capabilities

💻 Advanced Code Generation: Supports multi-language programming, full-stack development, and code repair, generating runnable code and boosting basic task efficiency by up to 10 times (e.g., generating a brand website in just 5 minutes).

🤖 Agent-Based Programming: Independently plans multi-step tasks, calls command-line tools, browsers, and other utilities, handling complex development workflows such as cross-file refactoring and CI feedback debugging.

📚 Long-Form Code Understanding: Parses codebases up to 1M tokens, comprehends cross-file dependencies, and is well-suited for large-scale project development and maintenance.

🌍 Low-Barrier Programming: Supports “atmosphere programming,” enabling ordinary users to generate code for complex scenarios such as 3D physical simulations through natural language.

🔧 Flexible Integration and Deployment: Provides API call examples, supports local deployment (requiring high GPU memory) and IDE integration, and is compatible with multiple development toolchains.