On a drizzly and windswept afternoon this summer, I visited the headquarters of Rokid, a startup developing smart glasses in Hangzhou, China. As I chatted with engineers, their words were swiftly translated from Mandarin to English, and then transcribed onto a tiny translucent screen just above my right eye using one of the company’s new prototype devices.

Rokid’s high-tech spectacles use Qwen, an open-weight large language model developed by the Chinese ecommerce giant Alibaba.

Qwen—full name 通义千问 or Tōngyì Qiānwèn in Chinese—is not the best AI model around. OpenAI’s GPT-5, Google’s Gemini 3, and Anthropic’s Claude often score higher on benchmarks designed to gauge different dimensions of machine cleverness. Nor is Qwen the first truly cutting-edge open-weight model, that being Meta’s Llama, which was released by the social media giant in 2023.

Yet Qwen, and other Chinese models—from DeepSeek, Moonshot AI, Z.ai, and MiniMax—are increasingly popular because they are both very good and very easy to tinker with. According to HuggingFace, a company that provides access to AI models and code, downloads of open Chinese models on its platform surpassed downloads for US ones in July of this year. DeepSeek shook the world by releasing a cutting-edge large language model with much less compute than US rivals, but OpenRouter, a platform that routes queries to different AI models, says Qwen has rapidly risen in popularity through the year to become the second-most-popular open model in the world.

Qwen can do most things you’d want from an advanced AI model. For Rokid’s users, this might include identifying products snapped by a built-in camera, getting directions from a map, drafting messages, searching the web, and so on. Since Qwen can easily be downloaded and modified, Rokid hosts a version of the model, fine-tuned to suit its purposes. It is also possible to run a teensy version of Qwen on smartphones or other devices just in case the internet connection goes down.

Before going to China I installed a small version of Qwen on my MacBook Air and used it to practice some basic Mandarin. For many purposes, modestly sized open source models like Qwen are just as good as the behemoths that live inside big data centers.

The rise of Qwen and other Chinese open-weight models has coincided with stumbles for some famous American AI models in the last 12 months. When Meta unveiled Llama 4 in April 2025, the model’s performance was a disappointment, failing to reach the heights of popular benchmarks like LM Arena. The slip left many developers looking for other open models to play with.

When OpenAI unveiled its latest model, GPT-5, in August it also underwhelmed. Some users complained of an oddly cold demeanor while others spotted surprising simple errors. OpenAI released a less powerful open model called gpt-oss the same month, but Qwen and other Chinese models remain more popular because more work is put into building and updating them, and because details of their engineering are often published widely.

Hundreds of academic papers presented at NeurIPS, the premier AI conference, used Qwen. “A lot of scientists are using Qwen because it's the best open-weight model,” says Andy Konwinski, cofounder of the Laude Institute, a nonprofit established to advocate for open US models.

The openness adopted by Chinese AI companies, which sees them routinely publishing papers detailing new engineering and training tricks, stands in stark contrast to the increasingly closed ethos of big US companies, which seem afraid of giving away their intellectual property, Kowinski says. A paper from the Qwen team, detailing a way to enhance the intelligence of models during training, was named as one of the best papers at NeurIPS this year.

Other big Chinese companies are using Qwen to prototype and build. A few days before visiting Rokid, I saw how BYD, China’s leading EV maker, has integrated the model into a new dashboard assistant. US firms are adopting Qwen too. Airbnb, Perplexity, and Nvidia are all using Qwen. Even Meta, once the pioneer of open models, is now said to be using Qwen to help build a new model.

Kowinski says US AI companies have become too focused on gaining a marginal edge on narrow benchmarks measuring things like mathematical or coding skills at the expense of ensuring that their models have a big impact. “When benchmarks are not representative of real usage or problems being solved in the world, you end up in this tired, misaligned mode,” he says.

The rising prominence of Qwen and similar models does seem to suggest that a key measure for any AI model, beyond how clever it is, should be how widely it is used to build other stuff. By that benchmark, Qwen and other open Chinese models are ascendant.