Aparna A Nair Aparna A Nair is a postgraduate student at the Department of Political Science, Madras Christian College, Chennai. She is currently a Research Assistant at NIAS.

Open-weight AI models are machine learning systems available for download and allow users to run them on their own infrastructure. They come with trained parameters - numerical ‘weights’ that encode what the model has learned. These weights determine how the model processes a prompt and generates answers. 

Closed models are models available to companies via a controlled application programming interface. In such a system, users send prompts to the provider's service and cannot inspect or modify the underlying system. They withhold training code, datasets, and full replication instructions. Unlike them, open-weight models offer lower operating costs, better privacy, and modification scope. Users are expected to comply with licensing restrictions and provide computing infrastructure, as it is easier to generate malicious code and harmful content if the safeguards are removed.

What are the major Chinese companies working on AI?
First, Alibaba’s Qwen series. Qwen 3.8 and related variants are among the most optimised open-weight models. They develop multilingual and multimodal models for text, coding, reasoning, images, and enterprise applications. The platform Hugging Face notes that Qwen recorded 2 billion downloads in 2026 so far, with over 150,000 derivative models crossing US milestones.

Second, DeepSeek’s V4 Flash. It is known for low-cost, high-performance inference in coding and agentic tasks. It is a benchmark setter for what open models can achieve on limited budgets.

Third, Z.ai’s GLM models. The company is viewed as the most capable model for agentic coding and cyber defence.

Fourth, Moonshot AI’s Kimi K3. It quickly rose to frontier status and is most capable for coding, reasoning, and agentic tasks. It rivals top U.S. closed systems on key tasks.

Why are Chinese companies betting on it?
With open-weight AI models, Chinese labs reduce training and inference costs with architectural efficiencies, distillation and large-scale engineering. Open models function as an entry point to Chinese infrastructure. Once users build applications around Qwen or DeepSeek, they become part of a larger ecosystem including software libraries, cloud services, technical standards, and hardware. Developers create derivatives, extensions, and specialised versions and upload them to platforms like Hugging Face, further improving the ecosystem and attracting more users.  Companies benefit by monetising APIs, enterprise services, and cloud. They focus on high performance at low cost.

AI is considered a strategic priority by the Chinese government. State support and financial repression allow Chinese labs to absorb the research and development costs. It also allows them to treat open releases as strategic investments rather than pure profit centres. Chinese companies face restrictions and political suspicion in Western markets. Open-weight distribution helps reach developers through global repositories and third-party cloud providers. This makes Chinese AI harder to exclude from international experimentation. This also helps them circumvent market sanctions. China also seeks a prominent role in AI governance. By reaching developers from East Asia to the US, Beijing may influence the language infrastructure and norms of AI systems.

What does it mean for the global AI race?
Firstly, a change in the meaning of competition. It is no longer only about producing the most capable closed model. It is about models that are affordable, customisable and easy to deploy. The market now prefers high-performance, low-cost models. It also changed market expectations. It has pressured competitors to either lower the price and open up more or risk losing volume in the market.

Secondly, open weights increase diffusion. Earlier, advanced models were available only through expensive contracts or depended entirely on US-based providers. Now, with more models or different uses available in the market, even countries seeking technological sovereignty or localised systems in their own language can achieve this more easily. As the refined or modified versions are uploaded back, it creates a diffusion-innovation loop which, in turn, helps Chinese labs get close to frontier AI performance.

Thirdly, a possible divide in the AI ecosystem. With the current geopolitical tensions and market restrictions, the division will be between Western firms and Chinese firms. One sphere could revolve around American firms, Western cloud providers and controlled access to frontier models. The latter could centre on Chinese models, domestic chips, and alternative cloud infrastructure. A divide is evidently visible in how the government views AI. China promotes ‘open, inclusive AI ', while the US enforces an ‘AI Iron Curtain.’

Fourthly, constraints on China’s AI growth. Access to the most advanced semiconductors is behind market barriers. Open-weight models lack sufficient computing power, high-quality data, research talent, or global trust. Concerns exist around data security, censorship, intellectual property, etc.

References

“State of Open Models: Summer 2026 Observations,” Hugging Face, 14 August 2026

“Why Is China Giving Away Its AI Models? Five Possible Explanations for China’s Open-Weight Strategy,” Takshashila Institution, 07 August 2026

“Two Loops: How China’s Open AI Strategy Reinforces Its Industrial Dominance,” US-China Economic and Security Review Commission, 23 March 2026

“Beyond DeepSeek: China's Diverse Open-Weight AI Ecosystem and Its Policy Implications,” Stanford University, 16 December 2025

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