CHINA WATCHChinese Models Are on Track to Win the Agentic AI Price War
If Western companies don’t want to be left behind by their cheaper Chinese counterparts, they must find a balance between intelligence and affordability.
Chinese AI labs have stunned the world, again. In the space of about a month, Chinese AI start-up labs Z.ai and Moonshot have each launched a model that is nearly as intelligent as competitors from OpenAI and Anthropic but far cheaper. Silicon Valley start-ups are already using Z.ai’s GLM-5.2, and Moonshot’s Kimi-K3 likely isn’t far behind in that market.
The success of lower-cost Chinese AI models highlights how high prices are likely to put Western models at a major disadvantage in the coming era of agentic AI, which is more expensive to use than large language models (LLMs) by several orders of magnitude. A future dominated by AI agents is one where companies value the cheapest intelligence possible. Right now, it’s advantage China.
AI prices are calculated per token – a computing-resources unit that Nvidia CEO Jensen Huang has called ‘the new commodity’ due to its growing importance in AI economics. AI companies use tokens to meter AI usage, roughly measured by the number of words that users put into an AI model and the number of words it then generates. While ordinary internet users pay for a monthly subscription, developers, who are using agentic models to build the future of AI, are charged per token. Buying 1 million tokens for, say, ChatGPT, buys a developer access to that model for a certain number of tasks. The price of 1 million tokens, some for users’ input and others for the AI output, varies between models.
GLM-5.2 tokens are cheap: Z.ai charges US$1.92 (A$2.74) per million output tokens for GLM-5.2, whereas Anthropic charges $25 per million for its Opus 4.8.
AI agents dramatically compound these cost differences. These models don’t just generate content but can also perform tasks online. The number of tokens needed to use agentic AI is exponentially higher than for conventional chatbot-style LLMs. Recent estimates from a team from MIT, Stanford and Google DeepMind suggest that agentic tasks use up to 3,500 times as many tokens as a simple reasoning task. A single prompt and response for information on a normal LLM usually requires a couple of thousand tokens. A single agentic coding or research task often runs into the millions, with some agent users at the extreme end already using tens or hundreds of millions of tokens per day for tasks that take days to complete.
