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Which artificial intelligence model is the most popular these days? Ask anyone in America or Europe, and you\u2019ll probably hear about the respective merits of OpenAI\u2019s ChatGPT, Anthropic\u2019s Claude, or Google\u2019s Gemini. All wrong. Over the past two weeks, the most widely used AI in the world was one that few Westerners had ever heard of: Kimi K2.6, an open-source Chinese model that topped the OpenRouter<\/a> leaderboard. <\/p>\n This ranking highlights how Western policymakers and CEOs could be fixating on the wrong race as they focus on semiconductor benchmarks and the question of which AI model is the most advanced. China, meanwhile, is quietly building something different: an ecosystem of open-source models that are both cheap and good enough for most use cases. By the time Western capitals notice, Chinese AI models may well have become global standards and prove hard to displace\u2014even by more advanced technology.<\/p>\n<\/p><\/div>\n Which artificial intelligence model is the most popular these days? Ask anyone in America or Europe, and you\u2019ll probably hear about the respective merits of OpenAI\u2019s ChatGPT, Anthropic\u2019s Claude, or Google\u2019s Gemini. All wrong. Over the past two weeks, the most widely used AI in the world was one that few Westerners had ever heard of: Kimi K2.6, an open-source Chinese model that topped the <\/span>OpenRouter<\/span><\/a><\/u><\/span> leaderboard. <\/span><\/p>\n This <\/span>ranking<\/span> highlights how Western policymakers and CEOs could be fixating on the wrong race as they focus on <\/span>semiconductor<\/span> benchmarks and the question of which AI model is the most advanced. China, meanwhile, is quietly building something different: an ecosystem of open-source models that are both cheap and good enough for most use cases. By the time Western capitals notice, Chinese AI models may well have become global standards and prove hard to displace\u2014even by more advanced technology.<\/span><\/p>\n The mainstream Western narrative is that U.S. export controls are preventing China from getting access to cutting-edge processor chips, slowing down the development of Chinese AI firms. This is true but eclipses a key part of the story: If Beijing\u2019s AI pitch centers on universal access and cost-effectiveness, then Chinese AI firms do not need the latest chips to win the global AI race.<\/p>\n Take Kimi K2.6, the open-source flagship model of Beijing-based Moonshot AI. On <\/span>industry benchmarks<\/span><\/a><\/u><\/span>, the model sits within <\/span>touching distance<\/span><\/a><\/u><\/span> of <\/span>cutting-edge <\/span>Western ones such as Anthropic\u2019s Claude Opus 4.7 and OpenAI\u2019s GPT-5.5. On price, it is in a different universe: Kimi costs <\/span>roughly $4<\/span><\/a><\/u><\/span> per million output tokens, about <\/span>six<\/span><\/a><\/u><\/span> to <\/span>eight<\/span><\/a><\/u><\/span> times cheaper than Opus 4.7 and GPT-5.5. (A token is a unit measuring the input and output of AI models.) For a casual user, this cost difference may not matter much. For a company running hundreds of AI agents, it can be decisive. <\/span><\/p>\n Kimi is not an outlier. While the Moonshot model tops the charts for usage through<\/span> an interface<\/span>, another Chinese model, Alibaba\u2019s Qwen, is fast becoming the default ecosystem for <\/span>self<\/span>-hosted AI models. Two data points help illustrate this point. First, by March, Qwen had captured <\/span>more than 50 percent<\/span><\/a><\/u><\/span> of global open-source model downloads, having <\/span>overtaken<\/span><\/a><\/u><\/span> its biggest Western competitor, Meta\u2019s Llama, in late 2025. Second, Qwen has been downloaded around <\/span>1 billion<\/span><\/a><\/u><\/span> times. Interest in Qwen is not confined to cost-minded firms. Last November, the Singaporean government <\/span>announced<\/span><\/a><\/u><\/span> that it would ditch Llama and build its sovereign AI model with Qwen instead.<\/span><\/p>\n With its open-source AI strategy, Beijing is revamping the logic of its Belt and Road Initiative (BRI)\u2014but with a twist. BRI entailed Chinese firms delivering fully financed infrastructure projects in a bid to lock third countries into China\u2019s orbit. The rollout of Chinese open-source AI models follows the same logic, but this time the infrastructure is both invisible and free. The marginal cost of AI diffusion is near zero (the main costs\u2014servers and the electricity to power them\u2014are borne by the hosting country), which makes the AI rollout a far cheaper investment for Beijing than building ports, railways, or power plants. What\u2019s more, BRI created highly visible, Chinese-owned infrastructure that sometimes proved unpopular. By contrast, AI dependency is invisible to both policymakers and the population, limiting pushback.<\/p>\n China\u2019s AI bet is a long-term one with a clear end goal: ensuring that Chinese AI models become (and thus shape) global standards. The underlying logic is that a default AI tool could quickly become a de facto industry standard. Once developers and firms build on China-made architectures, they use Chinese technical assumptions and standards\u2014thus giving Beijing long-term influence. It\u2019s part of a larger strategy; in its <\/span>Standards 2035 blueprint<\/span><\/a><\/u><\/span>, Beijing sets itself the goal of seeing Chinese products become global defaults in a bid to shape standards for next-generation technologies. As a precedent, China has been handing over access to its <\/span>Logink<\/span><\/a><\/u><\/span> shipping software for free in recent years. The platform quickly became a household name in the shipping sector after being <\/span>deployed<\/span><\/a><\/u><\/span> in at least 86 ports across 24 countries.<\/span><\/p>\n The battle to set global AI standards will mostly be waged in the global south. Western economies are locked into U.S. models, while China runs on Chinese ones. The swing states are everywhere else, and three factors make Chinese models well-placed to make headway there. First, U.S.-made AI is <\/span>too expensive<\/span><\/a><\/u><\/span> for broad rollout in cost-sensitive developing economies. Second, U.S. models are typically trained with Western data, making them ill-suited to grasp local contexts in global south countries. By contrast, open-source Chinese models can be downloaded and fine-tuned with country-specific data. Exhibit A is <\/span>AfriqueQwen-14B<\/span><\/a><\/u><\/span>, a Qwen-based AI that was adapted to 20 African languages through training on African data. The flagship Western open-source model, <\/span>Llama<\/span><\/a><\/u><\/span>, offers only <\/span>patchy coverage<\/span><\/a><\/u><\/span> of global south languages.<\/span><\/p>\n Third, growing global resentment against the United States is reinforcing China\u2019s strategy. To make the most of this, Beijing does not even have to lift a finger. Washington <\/span>frames<\/span><\/a><\/u><\/span> global AI competition as a national security race to be won against China, with no offerings to third countries needing cost-effective, good-enough tools. <\/span><\/p>\n The outcomes of two major AI summits for global south economies illustrate this point. Last April in Kigali, Rwanda, the Global AI Summit on Africa and resulting Africa Declaration on AI called for AI <\/span>governance<\/span><\/a><\/u><\/span> built around ethics, sustainability, and responsibility\u2014something that would probably sound alien to Silicon Valley tech executives. <\/span><\/p>\n At the India AI Impact Summit in February, global south policymakers doubled down. New Delhi anchored the conference\u2019s <\/span>agenda<\/span><\/a><\/u><\/span> in a \u201cPeople, Planet, and Progress\u201d framework emphasizing social empowerment, development, and inclusion. As Amitabh Kant, India\u2019s former G-20 sherpa, <\/span>put it<\/span><\/a><\/u><\/span> on the sidelines of the conference: \u201cWe are providing more data to OpenAI than the U.S. Data from [the] global south is helping refine models. These will sell you high-cost products. So India needs to build models on its own data.\u201d<\/span><\/p>\n The obvious objection to this narrative\u2014that global south countries could reject Chinese AI on security grounds\u2014appears shaky. Global south governments did not reject Chinese ports, railways, or power plants on security grounds under BRI. With no cheap Western AI offerings on the horizon (U.S. AI firms are cash-strapped and thus unlikely to cut prices), there is even less reason to expect a different pattern this time. Besides, the Chinese AI offer is harder to refuse. It helps that open-source models have no single vendor; they are distributed quietly through <\/span>public websites<\/span><\/a><\/u><\/span>, not via state-to-state contracts that can grab headlines.<\/span><\/p>\n Skeptics could also flag that Beijing\u2019s models come with Chinese Communist Party censorship baked in. Even when it is self-hosted, China\u2019s DeepSeek sometimes <\/span>refuses<\/span><\/a><\/u><\/span> to answer sensitive questions. But such censorship has been squarely aimed at China-sensitive topics such as Taiwan, Tiananmen, Tibet, and Xinjiang, making it mostly irrelevant to local needs in the global south. Yes, Beijing could always threaten to pull the plug on its AI models, for instance by tightening access to updates in countries <\/span>pursuing <\/span>relations with Taiwan. Yet as Washington has become erratic and unpredictable under U.S. President Donald Trump, global south leaders have plenty of reason to think that relying on Chinese AI could be safer<\/span> in the long term <\/span>than dependency on U.S. technology.<\/span><\/p>\n The real AI competition may not be a hardware arms race won by access to the most cutting-edge chips. Instead, the global AI race may well be a contest to decide which models and standards become the default infrastructure in countries that remain up for grabs. China does not need to dominate the most advanced models to win the AI race. If Chinese models become the affordable, good-enough default across emerging markets, Beijing will have built durable influence for decades.<\/p>\n Instead of fixating on the AI horse race between ChatGPT, Claude, and Gemini, Western policymakers may want to ponder how they can make an AI offer of their own to the global south before the defaults are set. Meanwhile, developers across Asia, Africa, and Latin America will continue to fine-tune their Kimi, Qwen, and DeepSeek agents.<\/p>\n<\/p><\/div>\n