Qwen3.6-35B-A3B - Alibaba Tongyi's Open-Source Mixture-of-Experts MoE Model

What is Qwen3.6-35B-A3B

Qwen3.6-35B-A3B is an open-source Mixture-of-Experts (MoE) model released by the Alibaba Tongyi Qianwen team, with a total of 35 billion parameters and 3 billion activated parameters. The model focuses on extreme inference efficiency and agentic coding capabilities, surpassing the larger dense model Qwen3.5-27B on multiple coding benchmarks, and significantly outperforming Google's latest releasedGemma 4series models. As the first open-source version of the Qwen3.6 series, it natively supports multimodal perception and reasoning, and its visual-language capabilities are already on par withClaude Sonnet 4.5or even partially surpassing it, making it one of the most versatile open-source models currently available.

Main Features of Qwen3.6-35B-A3B

• Agentic Coding: The model has excellent Agentic Coding capabilities and performs well on coding benchmarks such as SWE-bench and Terminal-Bench. It can be seamlessly integrated intoOpenClaw、Claude Code, Qwen Code, and other third-party coding assistants.
• Multimodal Perception and Reasoning: Natively supports visual-language understanding, performs outstandingly on visual question-answering benchmarks such as MMMU and RealWorldQA, and has significant advantages in spatial intelligence (RefCOCO 92.0, ODInW13 50.8).
• Dual-Mode Reasoning: Supports flexible switching between thinking mode (complex reasoning) and non-thinking mode (fast response), adapting to the needs of different task scenarios.
• Efficient Inference: Adopts a sparse MoE architecture and can achieve performance comparable to dense models several times its size while activating only 3 billion parameters, greatly reducing inference costs.
• Tool Calling and MCP Support: Supports function calling, code interpreter, and MCP (Model Context Protocol), and can connect to external tools and APIs to complete complex tasks.
• Long-Context Processing: Supports a 200K context window and can handle tasks such as long-document understanding and long-range code generation.

How to Use Qwen3.6-35B-A3B

• Online Experience:Visit Qwen Studio official website (https://chat.qwen.ai/），无需注册可直接开始对话，支持文本与图片输入，即时体验模型的编程与多模态能力。
• API Calling (Production Deployment)
  • Alibaba Cloud Bailian Platform: Log in to the Alibaba Cloud Bailian console, select the model qwen3.6-flash and create an API Key. Supports the standard OpenAI protocol (Chat Completions/Responses API) and the Anthropic protocol, and can seamlessly replace existing GPT/Claude interfaces. When calling, it is recommended to enable the preserve_thinking parameter to preserve the chain of thought and optimize agentic task performance.
  • Local Deployment: Download the open-source weights from Hugging Face and load them using vLLM, Ollama, or Transformers.
• Third-Party Tool Integration
  • OpenClaw: In the configuration file, set the Base URL to the Alibaba Cloud Bailian endpoint, fill in the API Key, and select the model name qwen3.6-flash, then you can use this model in the terminal to replace the default coding assistant.
  • Qwen Code: An open-source terminal AI agent specially optimized for the Qwen series. On first run, enter /auth to complete Alibaba Cloud Bailian authentication, and the system will automatically recognize and call Qwen3.6-35B-A3B for code generation and tool calling.
  • Claude Code: Because the API is compatible with the Anthropic protocol, you can directly replace the endpoint address and model name in the Claude Code configuration to obtain a coding experience with visual capabilities.

Key Information and Usage Requirements for Qwen3.6-35B-A3B

• Open-Source License: The model weights are fully open source, support local deployment and commercial use, and can be downloaded on the Hugging Face and ModelScope platforms.
• Online Experience: ViaQwen StudioYou can interact and converse directly, and experience the full functionality without deployment.
• API Call: Support for the Alibaba Cloud Bailian API is coming soon. The call name isqwen3.6-flash, compatible with the OpenAI specification's Chat Completions and Responses API, as well as the Anthropic API protocol.
• Hardware Requirements: Compared with dense models, it greatly lowers the threshold for local deployment. It only requires VRAM sufficient to support 3 billion activated parameters to run, making it suitable for individual developers to deploy locally.
• Third-Party Integration: It has been adapted for mainstream AI programming assistants such as OpenClaw (Moltbot), Qwen Code, and Claude Code, and can be directly integrated into existing development workflows.
• Special Features: The API supports thepreserve_thinkingparameter, which can retain the thinking content of previous turns in messages and is recommended for complex agent tasks.

Core Advantages of Qwen3.6-35B-A3B

• Extreme Parameter Efficiency: 3B activated parameters can surpass 27B dense models on programming and reasoning tasks, achieving the inference economy of "small size, big power."
• Top-Tier Agentic Coding Capabilities: It leads models of the same scale and even larger scale on authoritative coding benchmarks such as SWE-bench Verified (73.4) and Terminal-Bench 2.0 (51.5).
• Visual Capabilities on Par with Closed-Source Models: Its multimodal performance is on par with Claude Sonnet 4.5, with particularly outstanding spatial intelligence (RefCOCO 92.0) and document understanding capabilities.
• Full-Scenario Ecosystem Compatibility: It supports both the OpenAI and Anthropic API protocols, and can seamlessly replace models in existing workflows, reducing migration costs.

Project Address of Qwen3.6-35B-A3B

• HuggingFace Model Repository：https://huggingface.co/Qwen/Qwen3.6-35B-A3B

Comparison of Qwen3.6-35B-A3B with Similar Competing Products

Application Scenarios of Qwen3.6-35B-A3B

• AI-Assisted Programming: Automated code generation, bug fixing, code refactoring, and code review, which can be integrated into an IDE for use as an intelligent programming assistant.
• Agent Development: Build autonomous agents capable of calling tools, browsing the web, and executing code, for workflows such as automated operations and maintenance and data analysis.
• Multimodal Content Understanding: Handle tasks such as document analysis with mixed text and images, chart interpretation, visual question answering, and video content understanding.
• On-Device and Edge Deployment: Due to its few activation parameters and high inference efficiency, it is suitable for deployment on resource-constrained edge devices or edge servers to provide localized AI services.
• Education and Research: Used as an open-source foundation model for academic research, algorithm teaching, or domain-specific fine-tuning training.