Qwen 3.5 - Small models match or beat larger open competitors

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Alibaba released the Qwen3.5 Small model series, consisting of four AI models ranging from 0.8 billion to 9 billion parameters that run on standard laptops and mobile devices. 

The largest, Qwen3.5-9B, achieves a score of 81.7 on the GPQA Diamond graduate-level reasoning benchmark, surpassing OpenAI’s gpt-oss-120B (80.1) despite being 13.5 times smaller, and leads in multimodal tasks with 70.1 on MMMU-Pro visual reasoning versus Gemini 2.5 Flash-Lite’s 59.7. (Although Google has released an update to Gemini Flash-Lite: version 3.1.) 

Qwen’s small models use a hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts and native multimodal training through early fusion, enabling the 4B and 9B versions to handle video analysis, document parsing, and UI navigation tasks previously requiring models ten times larger. 

All weights are available under Apache 2.0 licenses on Hugging Face and ModelScope, allowing unrestricted commercial use and customization. 

The efficiency gains shift which model sizes developers can deploy for production agentic workflows — tasks like automated coding, visual workflow automation, and real-time edge analysis now run locally without cloud API costs or latency.

Ref: Hugging Face