A policy brief jointly released in late 2025 by Stanford's Institute for Human-Centered Artificial Intelligence (HAI) and the DigiChina project provides an in-depth analysis of China's rising open-weight AI models—a topic widely debated in Silicon Valley but rarely systematically examined. Titled "Beyond DeepSeek: China’s Diverse Open-Weight AI Ecosystem and Its Policy Implications," the December-published report by researchers including Caroline Meinhardt, Sabina Nong, and Graham Webster clarifies that when DeepSeek stunned global investors in January with its reasoning model—erasing nearly $100 billion from Nvidia’s market cap in a single day—the Hangzhou startup was far from China’s only AI contender. It merely represented the tip of a larger, more diverse ecosystem.

From Follower to Leader Multiple data points cited in the report indicate China has transitioned from follower to leader in open-source large language models (LLMs). Open-weight models allow parameter weights to be downloaded, used, and modified, enabling developers to run them independently of official APIs. Hugging Face data shows Alibaba’s Qwen (Tongyi Qianwen) overtook Meta’s Llama as the platform’s most-downloaded LLM family in September 2025, with 385 million cumulative downloads by mid-December versus Llama’s 346 million. Between August 2024–2025, Chinese developers accounted for 17.1% of Hugging Face downloads, surpassing the U.S. (15.8%) for the first time, per MIT-Hugging Face tracking analyzed by ATOM.

Derivative models tell a sharper story: Since January 2025, uploads based on Qwen and DeepSeek surged, with Chinese model variants comprising 63% of Hugging Face’s new derivatives by September—signaling unprecedented global developer engagement.

Four Model Families The report highlights four representative Chinese models: - **Qwen**: Developed by Alibaba Cloud, supports 119 languages under Apache 2.0. - **DeepSeek-R1**: Excels in reasoning and math, offers distilled versions for resource-constrained users. - **Moonshot AI’s Kimi K2**: Focused on code generation and agent tasks. - **Z.ai’s GLM-4.5**: Balances reasoning, programming, and vision via multi-expert training.

Efficiency Under Chip Constraints Most models adopt Mixture of Experts (MoE) architecture, optimizing performance under compute limits—a necessity amid U.S. AI chip export controls since 2022. DeepSeek-V3, for instance, activates only 37B of its 671B parameters per inference, cutting costs. Licensing also liberalized: Qwen3 (Apache 2.0) and DeepSeek R1 (MIT License) dropped earlier commercial restrictions, reflecting efforts to attract global developers and build academic credibility. Even Baidu, once a staunch proponent of closed-source models, open-sourced its flagship ERNIE 4.5 in June 2025.

Policy and Business Models China’s 2017 AI development plan prioritized "open-source," with 2023–2025 policies framing it as a tool for global influence. However, DeepSeek’s rise—originating from quant fund幻方—shows market forces, not state planning, drove much of this growth. Local governments now fund open-source projects, while academia rewards contributions in performance reviews.

Monetization remains experimental: Alibaba positions Qwen as an "AI OS" to boost cloud adoption (clients include HP and AstraZeneca), while DeepSeek and Z.ai adopt asset-light strategies via localized deployments. Like Western peers, most rely on funneling open-model users to paid services.

Geopolitical Echoes The report reiterates U.S. concerns: Chinese models may "inherit content moderation logic," risk data transfer to China, or face higher vulnerability (CAISI claims DeepSeek is 12x more breach-prone than U.S. counterparts). DeepSeek R1’s release shifted U.S. policy—prompting OpenAI’s first open-weight model in six years, with Sam Altman citing Chinese competition as key.

Ultimately, the report complicates narratives of "overtaking" by framing Sino-U.S. AI competition as a multidimensional systems battle—spanning ecosystems, engineering, cost, and compliance. For observers reducing China’s AI landscape to DeepSeek alone, this is a necessary recalibration.