Why China’s Qwen3 AI Now Leads Global AI Downloads

As of November 2025, Chinese open-source AI models make up about 17% of total open-model downloads, slightly ahead of US-built models at about 15.8% in second place.It is a significant shift, even though the gap may seem small. It means that when programmers look for a baseline model to start from, they are increasingly choosing engines such as Qwen and DeepSeek rather than US engines as the default option.

The Constraint: When You Can't Buy Enough GPUs

US export controls that block China from using Nvidia’s top accelerators led to large-scale training by Alibaba and ByteDance taking place in data centers owned by foreign companies in other countries, such as Singapore and Malaysia, to gain access to GPUs that are more available there. However, using foreign compute resources comes with high costs and high latencies. The ongoing scarcity of GPUs made cost per token the main optimization target rather than pure benchmark scores.

This shift clearly appears in DeepSeek R1, which relies heavily on reasoning tasks and was trained much more cheaply than Western frontier models, but still reaches competitive performance levels.When the numbers from DeepSeek surprised financial markets in January 2025, Nvidia’s market value fell by close to 17% in a single trading day, sending a strong signal that the GPU moat was being challenged by efficient Chinese models.

Qwen3 is Alibaba's answer to the same issue, but as a complete platform, not simply another model.

The Engine: Qwen3's Dual-Mode

In April 2025, Alibaba announced the Qwen3 family, designed to be useful, very flexible, and inexpensive to run, and immediately released it under an Apache 2.0 license so that it could be adopted commercially by any person or company.

Specifications at a Glance:

The family includes dense and Mixture-of-Experts models with up to 235B parameters, but only about 22B active parameters per token.

It was pre-trained with about 36 trillion tokens in 119 languages and dialects, with a strong focus on math and code.

The novelty:

Dual-mode thinking design:

Non-Thinking Mode when rapid queries are required.

Thinking Mode when step-by-step reasoning is needed in situations that require in-depth analysis.

In other words, the two-mode system above is actually a cost-control mechanism. Most tasks—summarizing pages, translating menus, responding to simple queries—stay on the inexpensive path. Only complex tasks such as planning and analysis trigger the expensive path. For example, for a firm that licenses and buys offshore GPU hours, this approach lets the company turn the intelligence in its model into something it can actively budget and control, rather than just promote.

Openness serves as the distribution leverage. The model by AI Singapore, SEA-LION v4, designed to handle Southeast Asian languages such as Indonesian, Thai, or Vietnamese, is built using Qwen3-32B, with over 100 billion new regional tokens added on top of it.Although Qwen is China’s native model family, it has already started to become the backbone of AI in another region.

The Interface Play: From Apps to Glasses

An efficient engine, however, still requires traffic. Alibaba's next step has been to get Qwen into the places where user intent appears first.

On the software side, the Qwen App was marketed as a tool for getting things done, instead of just chatting, and apparently reached 10 million downloads in under a week.

A revamped Quark AI browser enabled Qwen to act as the brain that powers search, summarization, and operations on local files without needing any plug-in, embedding Qwen as a “system engine” on desktop. In both cases, Qwen effectively became the layer between the typical user and the web.

Whereas with hardware, Alibaba went further with the launch of Quark AI glasses in two versions at prices starting from 1,899 yuan, equipped with cameras, microphones, dual batteries, and direct links to Taobao and Alipay. Qwen powers live translations, product identification, navigation, and meeting recording; these glasses can route simple glances and queries through the non-thinking mode and push more complex requests through full reasoning.

But the hardware would not make sense if the engine were not designed to think in terms of tiers. Otherwise, always-on glasses connected to the cloud would either be too slow or too expensive, or both.

Proof That the Strategy Is Working

There are two signals that show how the above plan is being implemented outside of the Alibaba ecosystem.

First, capital markets have already reacted. The efficient R1 model from DeepSeek triggered the nearly 17% decline in Nvidia stock in a single day, evidence that Wall Street already sees such efficiency in the Chinese market as highly important for the GPU industry.

Second, governments and regional initiatives are ready to bet their future on Qwen's stack. By choosing Qwen3-32B as the base model in SEA-LION v4, AI Singapore effectively treated the Chinese open model as an "Android layer" for Southeast Asian large language models—tailored to the region but rooted in the Alibaba engine.

Why This Matters For Everyone Else

For those designing AI-powered banking, healthcare, logistics, or media systems, the Qwen3 story cannot end with what happens in China, here’s why. Think of the level of reasoning as something that can be turned like a dial. Use open weights and friendly licenses to become the default engine in other people’s products. Integrate your model with strong interfaces like browsers, apps, and wearables so that you control where questions are asked and where purchases happen.

The race to build the most intelligent closed model might still have the US in the lead, but Qwen3 shows how a different race to lead in the open layer is unfolding underneath.