Alibaba's Qwen models have surpassed the 3 billion download mark: an analysis of the open AI phenomenon
Chinese tech giant Alibaba has reached an impressive milestone: over the past six months, the total number of downloads of their open-source AI models from the Qwen family has exceeded 3 billion. This event marks not just another record, but a fundamental shift in the global artificial intelligence landscape, where open-source developments from China are beginning to dominate over Western counterparts.
Numbers that speak for themselves
The scale of the Qwen ecosystem is staggering. The corporation has released more than 460 base neural networks into the public domain, on the basis of which the global developer community has created over 300,000 derivative models. Analyzing data from the Hugging Face platform, I see confirmation of this trend: over the seven months of the current year, Qwen models have been downloaded more than 2.05 billion times, with the number of forks reaching 151,448. For comparison, Google's figure stands at only 82,506.
The comparison with competitors is especially telling. In 2026, Google's model downloads amounted to 418 million, while Meta's were a modest 227 million. It is important to understand that Hugging Face statistics only account for activity within the platform's own ecosystem, excluding API requests and private corporate deployments. Nevertheless, even with these limitations taken into account, the gap is colossal and points to a qualitative change in developer preferences.
The secret to success: strategy, not chance
Why has Qwen managed to surpass recognized leaders? My analysis highlights three key factors. First, the regularity of updates — Alibaba releases new versions with enviable frequency. Second, unprecedented coverage across all model scales: from compact sub-billion versions to the giant Qwen3.8-Max with 2.4 trillion parameters. And third, the critically important Apache 2.0 license, which imposes no restrictions on modification or commercial use.
The breadth of the model lineup proved to be a decisive factor. According to Hugging Face data, versions with fewer than 1 billion parameters account for 83% of all historical platform downloads, while neural networks over 100 billion account for only 1%. This explains why laboratories focused exclusively on giant LLMs, such as Moonshot AI, are lagging behind: their models have gathered only 37 million downloads over the year — roughly 55 times fewer than Qwen. In the local deployment segment (GGUF builds), Qwen also leads with 39.6 million monthly downloads versus 20.8 million for Gemma and 7.5 million for Llama.
A geopolitical shift in the balance of power
The report demonstrates a fundamental shift in the balance. Almost every month of the current year, the largest open-source model from China surpassed all U.S. releases in size. The ceiling for Chinese models ranged from 754 billion to 2.78 trillion parameters, while for American competitors it did not exceed 130 billion in five out of seven months. Moreover, in China, 59% of models with more than 20 billion parameters are released under Apache 2.0, and none have restrictions on commercial use. In the U.S., the picture is the opposite: 41% are under proprietary terms.
Notably, within the United States itself, the leaders in the number of new open-source models were not AI laboratories but chip manufacturers: AMD and Nvidia each released more than 200 repositories, leaving Google and Meta far behind.
My expert assessment: Qwen's success is not just a victory for one company, but a signal that the open-source AI ecosystem is increasingly shifting toward China. Alibaba's strategy, betting on accessibility and flexibility, has proven more effective for the real-world development landscape than the closed or restricted approaches of Western giants. I believe that in the coming quarters, we will witness American companies being forced to reconsider their licensing policies in order not to lose their remaining market share.