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A new USCC report finds Chinese open-source models now dominate US AI startup stacks, with Qwen surpassing Llama in global downloads and Chinese models taking 41% of all Hugging Face downloads.
A community fine-tune distills Claude Opus 4.6 chain-of-thought reasoning into Qwen3.5-27B via LoRA, racking up 4,000+ downloads in days. No benchmarks yet - but the approach raises familiar questions.
Community fine-tune that distills Claude Opus 4.6 reasoning into Qwen3.5-27B via LoRA. 28B parameters, Apache 2.0, no published benchmarks.
Comparing the Claude Opus reasoning-distilled Qwen3.5-27B against the base model - what chain-of-thought distillation adds and what it costs in context, multimodal, and reliability.
Kimi K2.5 leads every coding benchmark, but Qwen3.5-35B-A3B delivers 87-93% of that performance at 3-4x lower cost and runs on a single consumer GPU. Here is the full breakdown.
Junyang Lin, the 32-year-old architect behind Alibaba's Qwen open-source AI models, announces his departure in a brief tweet - the fourth major exit from Tongyi Lab in two years.
China's National People's Congress opens this week with a 15th Five-Year Plan that puts $70 billion in semiconductor subsidies and AI-plus manufacturing at the center of its tech race with the West.
Alibaba completes the Qwen 3.5 lineup with four small models - 0.8B, 2B, 4B, and 9B - all natively multimodal, 262K context, Apache 2.0. The 9B outperforms last-gen Qwen3-30B and beats GPT-5-Nano on vision benchmarks.
Qwen3.5-0.8B is the smallest natively multimodal model in the Qwen 3.5 family - 0.8B parameters handling text, images, and video with 262K context. MathVista 62.2, OCRBench 74.5. Apache 2.0.
Qwen3.5-2B is a 2B dense multimodal model with 262K context, thinking mode, and native vision including video understanding. OCRBench 84.5, VideoMME 75.6. Apache 2.0 licensed.
Qwen3.5-4B is a 4B dense multimodal model that matches Qwen3-30B on MMLU-Pro and beats GPT-5-Nano on vision benchmarks. Runs on 8GB VRAM, Apache 2.0 licensed, 262K-1M context.
Qwen3.5-9B is a 9B dense model that outperforms Qwen3-30B on most benchmarks and beats GPT-5-Nano on vision tasks. Natively multimodal with 262K-1M context, Apache 2.0 licensed.
