Qwen3.6-35B-A3B Release Notes

Qwen has open-sourced Qwen3.6-35B-A3B, a sparse mixture-of-experts (MoE) model designed for high-efficiency agentic coding and multimodal reasoning. The model features 35 billion total parameters but only 3 billion active parameters, allowing it to rival the performance of much larger dense models like Qwen3.5-27B and Gemma4-31B while maintaining a significantly smaller active compute footprint.

Agentic Coding and Reasoning Performance

Qwen3.6-35B-A3B outperforms its predecessor, Qwen3.5-35B-A3B, and competes with larger dense models across several coding and agentic benchmarks. With only 3B active parameters, it achieves superior results in agentic coding tasks compared to the dense 27B-parameter Qwen3.5-27B.

Coding Agent Benchmarks

  • SWE-bench Verified: 73.4 (compared to 75.0 for Qwen3.5-27B and 70.0 for Qwen3.5-35B-A3B).
  • Terminal-Bench 2.0: 51.5 (surpassing Qwen3.5-27B's 41.6).
  • Claw-Eval Avg: 68.7 (surpassing Qwen3.5-27B's 64.3).
  • NL2Repo: 29.4 (surpassing Qwen3.5-27B's 27.3).
  • QwenWebBench: 1397 (surpassing Qwen3.5-27B's 1068).

General Agent and Knowledge Benchmarks

  • MCPMark: 37.0 (compared to 36.3 for Qwen3.5-27B).
  • AIME26: 92.7 (compared to 92.6 for Qwen3.5-27B).
  • GPQA: 86.0 (compared to 85.5 for Qwen3.5-27B).

Multimodal Capabilities

Qwen3.6-35B-A3B is natively multimodal, delivering perception and reasoning capabilities that match or exceed Claude Sonnet 4.5 on various vision-language benchmarks. Its performance is particularly strong in spatial intelligence.

Vision-Language Benchmarks

  • Spatial Intelligence: Achieves 92.0 on RefCOCO and 50.8 on ODInW13.
  • STEM and Puzzle: 81.7 on MMMU and 86.4 on Mathvista(mini).
  • General VQA: 85.3 on RealWorldQA and 92.8 on MMBench EN-DEV-v1.1.
  • Document Understanding: 89.9 on OmniDocBench1.5 and 81.9 on CC-OCR.
  • Video Understanding: 83.7 on VideoMMMU and 86.2 on MLVU.

Integration and Deployment

Qwen3.6-35B-A3B is available as open weights on Hugging Face and ModelScope, and via the Alibaba Cloud Model Studio API (as qwen3.6-flash).

API Features and Compatibility

  • Thinking Preservation: The API supports the preserve_thinking feature, which keeps thinking content from preceding turns in messages, a feature recommended for agentic tasks.
  • Protocol Support: Alibaba Cloud Model Studio supports OpenAI-compatible chat completions and responses APIs, as well as an API interface compatible with Anthropic.

Third-Party Coding Assistant Integration

  • OpenClaw: Compatible with the self-hosted open-source AI coding agent OpenClaw.
  • Qwen Code: Deeply optimized for the Qwen series and available as a terminal-based AI agent.
  • Claude Code: Supported via the Anthropic API protocol compatibility of Qwen APIs.

Summary of Model Specifications

Feature Specification
Total Parameters 35 Billion
Active Parameters 3 Billion
Architecture Sparse Mixture-of-Experts (MoE)
Availability Open Weights (Hugging Face, ModelScope) / API (qwen3.6-flash)

Sources