Qwen Ecosystem: 3 Billion Downloads and a New Balance of Power in Open AI
Chinese tech giant Alibaba has reached an impressive milestone: the cumulative number of downloads of their open-source AI models from the Qwen family has exceeded 3 billion over the past six months. This figure, which I had not previously reported in my reviews, confirms a tectonic shift in the open artificial intelligence industry.
To date, the corporation has publicly released more than 460 neural networks, which have become the foundation for the creation of 300,000 derivative models by developers worldwide.
Ecosystem analysis through the lens of Hugging Face
Data from the Hugging Face platform, published in its semi-annual report, paints an even more detailed picture. Over seven months of the current year, Qwen models were downloaded 2.05 billion times, and the number of derivative repositories reached 151,448. For comparison, Google's figure stands at only 82,506. It is important to understand that Hugging Face statistics only account for activity within its own ecosystem, excluding API requests and private corporate deployments, which makes the real scale of Qwen's distribution even more significant.
The growth dynamics are impressive: between 180 and 210 new derivative repositories based on Qwen appear daily. Notably, out of 28,531 GGUF conversions of these models on the platform, only 54 were created by Alibaba itself—the rest are the merit of an active developer community.
"Qwen has become the standard workflow for developers deciding which model to fine-tune and deploy," note Hugging Face analysts, and I fully agree with this.
Success factors and the balance of power
Qwen's leadership is explained by three key factors: regular updates to the model lineup, coverage of all scales—from sub-billion versions to the flagship Qwen3.8-Max with 2.4 trillion parameters—and the Apache 2.0 license, which does not restrict commercial use. The breadth of the lineup proved decisive: models with fewer than 1 billion parameters account for 83% of all historical downloads on the platform, while neural networks over 100 billion account for only 1%.
In the local deployment segment, Qwen also dominates: GGUF builds are downloaded 39.6 million times per month, nearly double the figures for Gemma (20.8 million) and five times more than Llama (7.5 million). Chinese developers are increasingly releasing models under open licenses: 59% of models with more than 20 billion parameters use Apache 2.0, whereas among their American counterparts, 41% fall under proprietary terms.
My analytical conclusion: China is strategically winning not only in quantity but also in the accessibility of technology, building itself an army of loyal developers worldwide. The United States, in turn, maintains control over infrastructure and chips, making the race more complex than a simple comparison of download numbers. In the coming years, it will be ecosystems, not individual models, that determine the balance of power in global AI.