Alibaba's Qwen models have surpassed the 3 billion download mark: a new stage in the open AI race.
The open neural network ecosystem from Chinese giant Alibaba has reached an impressive milestone: over the past six months, the total number of downloads of the Qwen model family has exceeded 3 billion. This is not just a number, but a marker of a tectonic shift in the global artificial intelligence market, where open weights are becoming the main battlefield.
The Hangzhou-based corporation has made more than 460 models publicly available, based on which the global developer community has created around 300,000 derivative solutions. Such depth of penetration indicates that Qwen has become fundamental infrastructure for thousands of startups and enterprises worldwide.
Hugging Face Data: An Objective Picture or the Tip of the Iceberg?
Analytics from the leading platform Hugging Face, published the day before, sheds light on the dynamics of competition. According to their semi-annual report, over seven months of the current year, Qwen was downloaded 2.05 billion times, and the number of derivative repositories reached 151,448. For comparison, Google's figure is only 82,506. It is important to understand: Hugging Face statistics only account for activity within its own ecosystem, excluding API requests, private deployments, and corporate channels. Thus, the real scale of Qwen usage is likely even higher.
Notably, the growth rate of Qwen derivative repositories is 180–210 per day. Of the 28,531 GGUF conversions (a format for local execution), only 54 were created by Alibaba itself—the rest are generated by the community. This is direct evidence that the models have become the de facto standard for fine-tuning and customization.
Why Qwen Outpaces Competitors: Three Pillars of Success
Qwen's superiority is explained by three strategic factors. First, a regular update cycle that prevents the ecosystem from becoming obsolete. Second, unprecedented scale coverage: from compact versions with sub-billion parameters to the flagship Qwen3.8-Max with 2.4 trillion parameters. It is the breadth of the lineup that has become the decisive argument.
According to Hugging Face data, 83% of all downloads in the platform's history come from models with fewer than 1 billion parameters, while giants over 100 billion account for only 1%. This explains the lag of laboratories focused on large LLMs. For example, Moonshot AI, which releases almost no models smaller than 70 billion, has gathered only 37 million downloads over the year—about 55 times less than Qwen.
Alibaba also dominates the local deployment segment: its GGUF builds are downloaded 39.6 million times per month, ahead of Gemma (20.8 million) and Llama (7.5 million). This is critically important for businesses that require privacy and control over data.
Geopolitics of Open Source: China vs. the USA
The report revealed a fundamental shift in the balance of power. Almost every month of 2026, the largest open model from China surpassed all American releases combined in size. The ceiling for Chinese models ranged from 754 billion to 2.78 trillion parameters, while for US competitors, in five out of seven months, it did not exceed 130 billion. This is not just a quantitative leap, but a change in the technological paradigm.
The key difference lies in licensing policy. In China, 59% of models with more than 20 billion parameters are released under the liberal Apache 2.0 license, 22% under MIT, and none of them have restrictions on commercial use. For American developers, the picture is the opposite: 29% under Apache/MIT, 41% under proprietary terms, and 30% have no specified license at all. This policy by China stimulates global adoption of their technologies.
Notably, in the USA, the leaders in the number of new open models were not AI labs, but chip manufacturers: AMD and Nvidia each released more than 200 repositories, leaving Google and Meta far behind.
My analysis. The growth of Qwen is not just the success of one company, but a signal that China is winning the battle for standards in open AI. The vast developer community accustomed to working with these models creates a lock-in effect: the more derivative solutions, the harder it is for competitors to win over users. However, one should not discount US control over infrastructure—from chips to cloud services—which could become a decisive factor in the long term. The war for AI is just beginning, and open weights are only the first front.