So Long, GPT-5. Hello, Qwen
The landscape of artificial intelligence is undergoing a profound transformation, and the era dominated by proprietary, benchmark-topping models from Western tech giants might be slowly receding into the rearview mirror. A new contender, Qwen, an open-weight large language model developed by Chinese e-commerce behemoth Alibaba, is rapidly gaining ground, not just as a competent alternative but as a preferred choice for innovation and practical application across the globe. This shift signals a pivotal moment, challenging the established narrative and heralding a future where openness, adaptability, and real-world utility might trump raw computational power and secretive development.
My journey into this evolving reality began on a typically drizzly and windswept afternoon this past summer, during a visit to the headquarters of Rokid in Hangzhou, China. Rokid, a dynamic startup at the forefront of smart glasses technology, presented a compelling demonstration of AI’s immediate, tangible impact. As I engaged in conversation with their engineers, the Mandarin spoken effortlessly by my hosts was instantaneously translated into English and then transcribed onto a tiny, translucent screen hovering just above my right eye. This remarkable feat was achieved by one of Rokid’s cutting-edge prototype devices, powered by Qwen. The experience was seamless, almost futuristic, and served as a powerful testament to the practical prowess of this rising AI model.
Qwen, formally known as 通义千问 or Tōngyì Qiānwèn in Chinese, might not always steal the spotlight in the competitive arena of AI benchmarks. Models from OpenAI, such as the much-anticipated GPT-5, Google’s advanced Gemini 3, and Anthropic’s sophisticated Claude, frequently boast higher scores on tests designed to quantify various facets of machine intelligence. Nor was Qwen the pioneering force behind truly cutting-edge open-weight models; that distinction largely belongs to Meta’s Llama series, which first democratized access to powerful AI models when it was released by the social media giant in 2023. Yet, despite not always leading the pack in raw benchmark figures, Qwen is carving out an increasingly significant niche, driven by its exceptional blend of capability and unparalleled ease of customization.
The burgeoning popularity of Qwen, alongside other formidable Chinese models emerging from DeepSeek, Moonshot AI, Z.ai, and MiniMax, is a phenomenon that demands attention. Their ascendancy can be attributed to a powerful combination: they are not only "very good" in terms of performance but also "very easy to tinker with." This accessibility and flexibility have resonated deeply within the developer community. Evidence of this growing preference is robust: according to HuggingFace, a leading platform for accessing AI models and code, downloads of open Chinese models on its platform dramatically surpassed those of US models in July of this year. DeepSeek, another Chinese innovator, further underscored this trend by releasing a cutting-edge large language model that achieved impressive performance with significantly less computational power than its US counterparts. OpenRouter, a platform that intelligently routes queries to various AI models, further corroborates Qwen’s meteoric rise, reporting that it has rapidly ascended throughout the year to become the second-most-popular open model globally.
Qwen’s versatility means it can perform nearly any task one would expect from an advanced AI model, making it an invaluable tool for a wide array of applications. For Rokid’s users, this could encompass identifying products captured by a built-in camera, providing real-time navigation directions, drafting messages, executing web searches, and a myriad of other functions that enhance daily life and productivity. The inherent advantage of Qwen being easily downloadable and modifiable allows companies like Rokid to host their own version of the model, meticulously fine-tuned to precisely suit their specific operational requirements and user needs. Furthermore, the capacity to run a compact, "teensy" version of Qwen on smartphones or other edge devices ensures that crucial AI functionalities remain accessible even in the absence of an internet connection, a critical feature for reliability and ubiquitous utility.
Before embarking on my trip to China, I personally experienced this practical advantage by installing a smaller version of Qwen on my MacBook Air. I used it to practice basic Mandarin, finding it to be an incredibly effective and responsive language tutor. This personal experience reinforced a crucial insight: for a vast number of practical purposes, modestly sized open-source models like Qwen are proving to be just as effective and efficient as the colossal, proprietary AI behemoths that reside within massive, centralized data centers. This democratizes AI power, making advanced capabilities accessible without requiring immense computational resources or reliance on external, often expensive, cloud services.
The remarkable rise of Qwen and other Chinese open-weight models has coincided with a period of unexpected stumbles and disappointments for some of the most celebrated American AI models over the past 12 months. When Meta unveiled Llama 4 in April 2025, the model’s performance, regrettably, fell short of expectations, failing to achieve the high scores anticipated on popular benchmarks like LM Arena. This unexpected slip left many developers, who had previously championed Llama, searching for alternative open models to experiment with and build upon.
The narrative continued with OpenAI’s highly anticipated GPT-5, unveiled in August, which, to the surprise of many, also underwhelmed. Users voiced complaints ranging from an "oddly cold demeanor" in its interactions to the identification of "surprisingly simple errors" in its outputs, issues that seemed incongruous with a model of its presumed caliber. Although OpenAI did release a less powerful open model, gpt-oss, in the same month, it struggled to capture the widespread enthusiasm seen for Qwen and other Chinese models. The reasons for this disparity are multi-faceted: Chinese developers often dedicate more continuous effort to building, updating, and refining their models, and, crucially, they are significantly more transparent about the intricate details of their engineering and training methodologies.
This commitment to openness is a defining characteristic of Chinese AI companies, and it stands in stark contrast to the increasingly guarded and closed ethos adopted by many major US tech companies, which appear hesitant to divulge their intellectual property. Andy Konwinski, cofounder of the Laude Institute, a nonprofit dedicated to advocating for open US models, highlighted this difference, noting that "a lot of scientists are using Qwen because it’s the best open-weight model." The transparency practiced by Chinese firms manifests in their routine publication of academic papers that meticulously detail new engineering breakthroughs and innovative training techniques. A prime example is a paper from the Qwen team, which outlined a novel method for enhancing model intelligence during the training phase, an achievement that was recognized as one of the best papers at NeurIPS, the premier AI conference, this year. Such open sharing fosters a vibrant research ecosystem and accelerates global AI development.
The adoption of Qwen extends far beyond startups like Rokid, reaching into the core operations of major industries. Just days before my visit to Rokid, I witnessed how BYD, China’s preeminent electric vehicle (EV) manufacturer, has seamlessly integrated Qwen into a new generation of dashboard assistants, enhancing the in-car experience with intelligent, responsive AI. This cross-industry adoption is not limited to China; a growing number of influential US firms are also embracing Qwen. Companies such as Airbnb, known for its innovative platform, Perplexity, a rising star in AI-powered search, and even Nvidia, a powerhouse in AI hardware, are reportedly leveraging Qwen’s capabilities. Perhaps the most telling indicator of Qwen’s undeniable impact is the revelation that even Meta, once the undisputed pioneer of open models with its Llama series, is now said to be utilizing Qwen as a foundational component to help build its own new models. This signifies a remarkable full-circle moment, underscoring Qwen’s strategic importance in the global AI landscape.
Konwinski’s critique of US AI companies rings particularly true in this evolving context. He argues that they have become overly preoccupied with securing a "marginal edge on narrow benchmarks," meticulously measuring specific skills like mathematical proficiency or coding ability. This intense focus, he contends, has come at the expense of ensuring their models achieve a broader, more significant impact in real-world applications. "When benchmarks are not representative of real usage or problems being solved in the world, you end up in this tired, misaligned mode," Konwinski states, pinpointing a fundamental disconnect.
The undeniable rise and widespread prominence of Qwen and other similar open-weight models strongly suggest that the true measure of any AI model’s value, extending far beyond its inherent cleverness or benchmark scores, should ultimately be defined by how extensively it is utilized to build and innovate other applications and technologies. By this crucial benchmark—the metric of practical utility, adaptability, and widespread adoption—Qwen and its fellow open Chinese models are not merely ascendant; they are leading a profound paradigm shift. The era of closed, proprietary models from Western giants may not be over, but the message is clear: the future of AI is increasingly open, collaborative, and globally distributed, with models like Qwen at the forefront of this exciting new chapter. So long, GPT-5. Hello, Qwen.
