More than 150,000 Qwen derivatives as China’s open models gain ground

The number of publicly released models listed on Hugging Face Hub grew from 2.43 million to 2.96 million. An analysis by Hugging Face tracking activity from January through July 2026 found that Chinese labs and companies are strengthening their presence both in model size and in reach across the community.

China’s players took the lead in scale. According to Hugging Face, the largest model released by a Chinese lab was bigger than the largest model from a U.S. lab in almost every month of the period examined. The monthly ceiling for Chinese players ranged from 754 billion to 2.78 trillion parameters. By contrast, U.S. players remained below 130 billion parameters in five of the seven months. Moonshot AI, MiniMax, Xiaomi and Z.ai have largely avoided the conventional strategy of sequentially expanding models from 1 billion to tens of billions of parameters, releasing few models below 70 billion parameters.

Tencent and Alibaba’s “Qwen” present a contrast. Qwen offers a broad range, from models with fewer than 1 billion parameters to large models, aiming to provide a family of models that developers can use as a common foundation. Qwen derivatives on Hugging Face Hub reached 151,448, 2.6 times the total for Meta and 4.7 times the number associated with “Llama” alone. From January through July 2026, derivative models grew at a pace of 180 to 210 per day.

The combination of regular releases, a range of size options and the accessible Apache 2.0 license supported this expansion. Of 178 models released in 2026 from China with more than 20 billion parameters, 59% used Apache 2.0 and 22% used MIT. However, examples such as Kimi K3 and Qwen 3.8 also include noncommercial restrictions or revenue-sharing requirements. Hugging Face analyzed the revenue sources for these model releases as coming not from licensing fees, but from APIs, cloud services, hardware and platforms.

The practical change is that developers can more easily modify foundation models such as Qwen and create derivative models tailored to their own needs. At the same time, download counts and the number of derivative models are not in themselves measures of quality, commercial adoption or market share. In practice, 83% of cumulative downloads went to models with fewer than 1 billion parameters, while those with more than 100 billion accounted for just 1%. The competition to release large models attracts attention, but downloads are dominated by smaller models with fewer than 1 billion parameters.