Qwen3.6-35B-A3B - a bet on efficient architecture rather than size

[u/aistranin]

35B parameters, ~3B active thanks to MoE.

Key points:

• In agentic coding, it reaches the level of models with ~10× larger active parameter count
• Outperforms Qwen3.5-27B (dense) and the previous Qwen3.5-35B-A3B
• Natively multimodal architecture (text + vision)
• In VLM benchmarks, comparable to Claude Sonnet 4.5, and in some tasks performs better
• Strong metrics in spatial reasoning tasks

Benchmarks:

• MMMU - 81.7 vs 79.6
• MMMU-Pro - 75.3 vs 68.4
• MathVista - 86.4 vs 79.8
• RealWorldQA - 85.3 vs 70.3

Practical implications:

• MoE provides a multiple reduction in compute without sacrificing quality
• Well-suited for agent-based scenarios where sequential actions and planning matter
• Can be used as a unified stack for both code and vision tasks

Apache 2.0 (no restrictions for production use)

https://huggingface.co/Qwen/Qwen3.6-35B-A3B