The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of overall performance, this model is second only to Qwen3.5-397B-A17B. Its text capabilities significantly outperform those of Qwen3-235B-2507, and its visual capabilities surpass those of Qwen3-VL-235B.
Why it stands out
What to watch
Release timeline
release
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of overall performance, this model is second only to Qwen3.5-397B-A17B. Its text capabilities significantly outperform those of Qwen3-235B-2507, and its visual capabilities surpass those of Qwen3-VL-235B.
View sourceNearby alternatives
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms
The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a spars
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sp
Qwen launched Qwen: Qwen3.5-122B-A10B
Compare
vs Google: Nano Banana 2 (Gemini 3.1 Flash Image Preview)
Side-by-side pricing, context, and capabilities
vs MoonshotAI: Kimi K2.5
Side-by-side pricing, context, and capabilities
vs OpenAI: GPT Audio Mini
Side-by-side pricing, context, and capabilities
vs Google: Gemini 3 Flash Preview
Side-by-side pricing, context, and capabilities
vs Mistral: Devstral 2 2512
Side-by-side pricing, context, and capabilities
vs Qwen: Qwen3.5-35B-A3B
Side-by-side pricing, context, and capabilities