For Chinese businesses, it’s not about which AI is the smartest

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This report is from this week’s CNBC’s The China Connection newsletter, which brings you insights and analysis on what’s driving the world’s second-largest economy. You can subscribe here.

The big story

Will the U.S. or China win the artificial intelligence race? That’s the big question for investors wondering where they should put their money.

But frequently, I’ve found that many companies in China are asking a different question: Which AI tools can help me survive in a tough economy?

All this means that investors who choose an AI “winner based on smarts alone likely overlook a bigger story.

First, there’s the cost factor.

While OpenAI’s gpt-oss open-weight model and DeepSeek are the cheapest AI models to operate – 30 cents per 1 million tokens – Google’s Gemini 3 Pro charges $4.50, while Anthropic’s top-tier Claude Opus 4.5 costs $10, according to Artificial Analysis, which benchmarks AI models.

That’s a pay-per-use structure that can quickly balloon in price — whether businesses are using a “closed” AI model from OpenAI or remotely accessing an open-source model from DeepSeek or Alibaba.

Instead, many companies in China prefer to control costs by choosing a third option — downloading open-source models, James Tong, CEO of Suzhou-based Movitech, told me. His enterprise software company’s clients include Starbucks and Unilever.

Another key difference with the U.S. is how Chinese businesses are using AI.

Companies in the U.S. tend to use AI more for coding, while in China, they use the technology more for specific situations, according to Tong. For example, since the second half of 2025, Movitech has used AI agents to help manufacturers improve their production processes and assist state-owned enterprises with compliance.

Locally run open-source models again fit better because they don’t require users to upload information to third-party cloud servers.

It’s a bonus productivity tool when domestic demand has been sluggish.

Tong said he’s had to lower product…

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