The investment bank's new three-dimensional analysis finds Chinese AI models achieving near parity with US rivals at a fraction of the cost, with massive revenue growth projected through 2030.
Goldman Sachs dropped a comprehensive report on Friday dissecting the competitive positioning of China’s AI large language model industry, and the takeaway is pretty clear: Chinese AI companies are building models that rival their American counterparts while charging a fraction of the price.
The report, led by analyst Ronald Keung and titled “Who Will Be the Long-Term Winner in China’s AI Large Model Industry?,” introduces a novel three-dimensional framework evaluating companies across pricing power, cost advantages, and financial strength.
The competitive landscape, mapped #
Goldman’s framework identifies two clear leaders in different domains. Zhipu and DeepSeek currently dominate in foundational text models, while ByteDance has pulled ahead as the frontrunner in multimodal and video generation capabilities.
Here’s the thing about pricing. Chinese high-end models are running at roughly $1 per million tokens, compared to $4-8 for their US equivalents. That’s 10-25% of what American companies charge. At the low end, Chinese models go as cheap as $0.06-0.20 per million tokens. Chinese AI companies are achieving this through smaller, more efficient architectures, often ranging from 2-10% of the parameter sizes of US models, and leveraging techniques like Mixture-of-Experts (MoE) architectures that route queries to specialized sub-networks rather than activating the entire model.
Goldman initiated coverage on Zhipu with a Neutral rating, pegging the company’s valuation at $110 billion. DeepSeek and ByteDance remain unlisted. The bank maintains Buy ratings on MiniMax and Kuaishou.
A 25x consumption surge by 2030 #
Goldman estimates Chinese AI model API and subscription revenue will grow from roughly 35 billion RMB in 2026 to 879 billion RMB by 2030. That’s approximately a 25-fold increase in daily token consumption over four years.
The drivers behind this growth are twofold. First, open-weight strategies, where companies release model weights publicly, are accelerating developer adoption and creating ecosystem lock-in. Second, enterprise adoption is ramping as businesses realize they can deploy AI solutions at a fraction of what US providers charge.
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