Why is China's AI (Artificial Intelligence) big model capability so excellent, yet still so cheap? ”Recently, there has been a high level of discussion about the Chinese big model on overseas social media platforms. Many overseas developers have reported that using Chinese large models to handle complex development tasks for a whole day incurs significantly lower overall costs compared to similar overseas products; A startup team has stated that after fully switching to the Chinese AI big model, technology operation expenses have significantly decreased, and many AI projects that were previously put on hold due to cost issues have been able to be implemented and promoted. From North American technology circles to entrepreneurial teams in Southeast Asia, from independent developers in Europe to technology enthusiasts in Asia, Chinese AI big models are becoming increasingly recognized by global users for their ease of use and accessibility.

The selection criteria for users have always been straightforward: firstly, whether the model capability is strong, whether the response is efficient, and whether it can truly solve practical problems; The second is whether the usage cost is reasonable and whether it can be used stably for a long time. The essence of token call volume is the actual usage volume, which reflects the penetration depth and application breadth of AI models in production and life scenarios. The frequent selection of Chinese AI models by global users is the best response to this selection criterion.

High cost-effectiveness accelerates global fan base

In recent years, the competitive landscape of the global AI market has been quietly changing. According to publicly available operational data from OpenRouter, a global AI model aggregation platform, since 2026, China's large model weekly tuning volume has consistently ranked among the top in the world, with multiple top models occupying the top spot in tuning volume rankings for a long time. In a statistical week in late July 2026, the top five platforms in terms of call volume were all Chinese enterprise models.

The performance of the open source community is equally impressive. On Hugging Face, one of the world's largest AI open source communities, China has become the country with the highest monthly download volume of models on the platform. The spring 2026 report of this community shows that the download volume of open source models developed in China has accounted for 41% of the global total, with a cumulative download volume exceeding 10 billion times, deeply integrated into the global open source innovation system. In the past year, multiple Chinese open source models have consistently ranked among the top in platform downloads, growing from technology catch-up players to important suppliers and drivers of the global open source ecosystem.

The attitude of the overseas technology industry is the most indicative of the problem. NVIDIA CEO Huang Renxun has repeatedly publicly praised China's open-source AI models as "very excellent"; Tesla CEO Musk also praised China's artificial intelligence. The global recognition of Chinese AI technology continues to increase.

From the perspective of product implementation, Chinese big models have already gone abroad and integrated into various industries around the world. Singaporean engineers have trained a large model based on China's open-source model to meet local needs and serve the development of the local digital economy; American creators use Chinese video generation models to produce visual content that meets professional standards, significantly reducing the threshold for creative production. From government services in Southeast Asia to the energy industry in the Middle East, from start-ups in Europe and America to e-commerce platforms in Latin America, China's big models are winning the trust of global users with their tangible capabilities.

Architecture Innovation Runs Out of 'China Efficiency'

The secret to the dual advantages of performance and cost that China's large-scale models can achieve lies in the innovative breakthroughs in technology routes, among which the large-scale application of mixed expert (MoE) sparse architecture is a key link.

Unlike traditional dense models that activate all parameters for each inference, hybrid expert architectures call a small number of "expert" subnetworks on demand through a "gate controlled network", with most parameters in a dormant state. This significantly reduces computational power consumption while ensuring output quality. Many leading large model teams in China have already laid out this technology route, optimizing common engineering problems such as uneven routing load and related communication bottlenecks in the industry, continuously improving the efficiency of computing power utilization, and striving to achieve the same output quality with less computing power.

On this basis, the domestic team continues to deepen their efforts in optimizing the entire chain engineering. Native low precision quantization technology significantly reduces model memory usage; KV caching (key value caching) and multi-level caching technology significantly reduce duplicate calculations; System level communication and pipeline scheduling optimization further unleash the potential of computing power clusters. The domestic large-scale model gradually forms a positive cycle of "technological iteration cost reduction application explosion", relying on algorithm innovation and engineering optimization to continuously reduce inference costs and build outstanding cost-effectiveness advantages.

Stable energy supply and comprehensive computing infrastructure provide guarantees for the formation of cost advantages in China's large-scale model. China's intelligent computing power ranks among the top in the world, and its green power supply capacity continues to increase. The construction of large-scale data centers relies on economies of scale to help reduce unit computing power costs. By continuously adapting and mass producing domestic computing chips, the domestic AI industry is gradually building a more resilient and competitive industrial system, laying a solid foundation for providing inclusive AI services to global users.

Open source ecosystem gathers global iterative forces

The rapid progress of China's large-scale models cannot be separated from the development concept of open source and openness. Unlike some top overseas companies that adhere to a closed source approach, mainstream big model teams in China generally choose an open weight or partially open source strategy, actively integrating into the global open source innovation ecosystem.

The most direct effect brought by open source is to accelerate iteration. After the opening of model weights, millions of developers around the world can troubleshoot defects and contribute optimization solutions, and a massive amount of feedback from real-world scenarios continues to flow back to the model team, driving rapid evolution of capabilities. The speed of this global crowdsourcing iteration is far faster than the pace of closed source teams polishing behind closed doors. It is widely believed in the industry that open source is not a one-way technological output, but the most efficient way of innovation. Developers from all over the world participate in the construction, and ultimately promote the sustained prosperity of the entire industry ecosystem.

The prosperity of the open source ecosystem has in turn amplified the global influence of Chinese models. International mainstream AI development platforms have been integrating Chinese big models as soon as possible, and many overseas inference platforms regard the launch of new Chinese models as an important development in the industry. More and more overseas developers are conducting secondary development based on Chinese models, adapting to local languages, industry scenarios, and cultural needs, giving rise to a number of new technologies and applications, further enriching the ecological map of Chinese models, and forming a virtuous two-way cycle.

More importantly, open source has lowered the threshold for the use of AI technology. For developing countries and small and medium-sized enterprises, there is no need to invest huge amounts of money to train models from scratch, and they can quickly build their own AI applications relying on mature Chinese open source models. This also confirms an industry trend: China's big models are moving from a single product going global to a technology base going global, empowering the development of the global digital economy.

Scene based training is the "practicality oriented" hardcore skill

Overseas users find the Chinese big model "user-friendly" not only in terms of laboratory performance, but also in its ability to solve practical problems. And this practical application capability cannot be separated from the continuous polishing of China's rich application scenarios.

China has the largest Internet user group in the world and a complete industrial application ecosystem. E-commerce customer service, document processing, code development, content creation, industrial quality inspection, government services... A massive number of real-world application scenarios continue to demand large models, driving rapid evolution of models in terms of practicality. Taking long text processing as an example, domestic enterprises often need to process hundreds of pages of contracts and complete code repositories, which drives the widespread use of large contextual windows in domestic models. This is also a prominent pain point faced by overseas enterprise users. Many overseas users have reported that the actual performance of Chinese models often exceeds expectations when dealing with long documents and complex tasks.

The breakthroughs in cutting-edge capabilities such as intelligent agents and code generation also stem from the refinement of real-world scenarios. The demand for digital transformation of the domestic Internet and manufacturing industry promotes the continuous iteration of the domestic big model in the dimensions of tool invocation, task planning, code generation, etc. Many overseas developers have tested and shown that the Chinese model performs stably in complex work scenarios such as terminal task execution, code debugging, and multi tool collaboration, effectively improving development efficiency. At the same time, the comprehensive cost of use is highly attractive. In fact, there is already a difference in the adaptability between laboratory scores and real-world scenarios. Many models score well in standard evaluations, but when faced with vague requirements and abnormal situations in real-world scenarios, they are often prone to adaptation bias. The Chinese model has been polished by a large number of user practical scenarios, making it more convenient to handle various real-life tasks such as production and life. This characteristic of being close to actual needs is also an important reason for gaining recognition from global users.