China’s rise and the growing risks of the AI race
For the past three years, artificial intelligence has been dominated by a familiar collection of American companies. OpenAI, Anthropic, Google and Meta have attracted extraordinary investment, recruited some of the world’s best researchers and built increasingly powerful models. Their progress has been remarkable, although the cost of achieving it has been equally remarkable. Training a frontier model requires enormous computing resources, and operating one at scale creates another substantial bill.
China has approached the same problem from a different position. American restrictions on advanced semiconductor exports have made access to the most powerful AI chips more difficult, while fierce domestic competition has placed considerable pressure on prices. Chinese developers have consequently had strong incentives to find more efficient ways of training and running models. What began partly as a response to technological constraints has become a competitive advantage.
The arrival of DeepSeek R1 in January 2025 brought this into focus. Here was a Chinese company offering reasoning capabilities that challenged leading American systems while publishing its model weights and claiming substantially lower training costs. Some early comparisons were overstated, particularly those confusing the cost of an individual training run with the full expense of developing a frontier model. Nevertheless, DeepSeek demonstrated that sophisticated AI could be developed and distributed using a different economic model.
Alibaba, Moonshot AI, Z.ai and MiniMax have since helped establish a Chinese AI industry considerably broader than many Western observers appreciate.
How do Chinese and Western models compare?
Comparing AI models has become surprisingly difficult. Some excel at mathematical reasoning, others at writing software, analysing documents or operating tools autonomously. Performance also changes depending on how much computing power a model is allowed to spend answering a question.
The following comparison is deliberately approximate. Benchmark scores are indicative rather than definitive, and not every model has been evaluated under identical conditions. The pricing figures are similarly illustrative, reflecting reported API rates rather than a uniform, independently verified price survey.
| Region | Company | Model | Principal strengths | Score* | API input / output** |
| US | OpenAI | GPT-6 Astra | Reasoning, coding, agents | 53 | $10 / $50 |
| US | Anthropic | Claude Opus 5.5 | Reasoning, coding, autonomous tasks | 58 | $4 / $20 |
| US | Gemini 4 Argon | Multimodal reasoning, long context | 53 | $2 / $10 | |
| US | Meta | Muse Spark 1.3 | General reasoning, efficient inference | 48 | $1.25 / $4.25 |
| France | Mistral | Mistral Large 4 | Coding, enterprise deployment | N/V | $0.68 / $2.09 |
| China | Xiaomi | MiMo-V2.6-Pro | Reasoning, coding | 46 | N/V |
| China | Alibaba | Qwen 3.8 Max | Coding, multimodal applications | 45 | $2 / $6 |
| China | Z.ai | GLM 5.3 | Coding, agents | 45 | Varies |
| China | DeepSeek | V4 Pro | Reasoning, coding | 36 | $0.66–1.74 / $1.98–3.48 |
| China | Moonshot AI | Kimi K3 | Long-context reasoning, agents | 44 | $0.50–3 / $12–15 |
| China | MiniMax | M3 | Coding, multimodal tasks | N/V | N/V |
Approximate benchmark index points, not percentages or a uniform ranking. Indicative USD per million input/output tokens, October 2026. Prices vary by provider and usage conditions; some entries have not been independently verified. N/V means not verified.
What stands out is the number of Chinese developers competing across essentially the same technical territory as their American counterparts. Chinese models are increasingly designed for sophisticated reasoning, software development and autonomous agents, rather than simply providing inexpensive alternatives for routine tasks.
American proprietary models remain strong at the frontier, but the gap varies considerably by task. There is also an important distinction between performing well on a benchmark and operating reliably in a business environment. I’ve explored that problem in an earlier essay comparing leading AI models, where apparently small differences in performance can be much less meaningful than they first appear.
Pricing introduces another dimension. Several Chinese developers offer capable models at substantially lower prices than leading Western frontier systems, although Western companies also provide inexpensive smaller models. OpenAI’s GPT-6 Luna, for example, has been offered at $0.10 per million input tokens and $0.50 per million output tokens. Comparing prices without considering capability is rather like comparing the cost of two employees without asking what work they can actually do.
The effect of Chinese competition has therefore been to increase pressure across the price-performance spectrum. This becomes particularly important for AI agents that may make hundreds or thousands of model calls while completing a task. The wider economic consequences connect with my earlier essay, What is a company for when intelligence is cheap?
Another distinction concerns distribution. Several Chinese developers publish open-weight models that organisations can download and operate themselves, rather than accessing them exclusively through a commercial API. Meta and Mistral also offer open-weight models, but Chinese companies have made this approach particularly important to their competitive positioning. It offers flexibility and infrastructure independence, although it also makes subsequent control over model deployment considerably harder.
China’s industrial advantage
China’s progress extends beyond a collection of talented research laboratories. Artificial intelligence forms part of a broader industrial strategy intended to reduce dependence on foreign technology and strengthen domestic manufacturing and infrastructure.
The government’s AI Plus initiative encourages adoption across established industries, including manufacturing, transport and robotics. China already has enormous industrial capacity, sophisticated supply chains and companies operating at scale in electric vehicles, telecommunications and automation. These provide opportunities to apply AI to physical processes rather than concentrating primarily on software and information work.
An AI system that improves factory scheduling, reduces defects or enables an industrial robot to perform a new task has an economic value that can be measured relatively directly. It may be less glamorous than demonstrating that a model can solve an obscure mathematical problem, but the potential productivity gains are substantial.
There are limitations. Semiconductor restrictions remain a constraint, and government support can sustain investments that would struggle under ordinary commercial scrutiny. Nevertheless, the combination of domestic competition, industrial demand and strategic investment gives Chinese developers considerable opportunities to improve their products.
It also creates pressure to demonstrate progress, which introduces a less comfortable aspect of the competition.
The safety problem
The incentives driving AI development are not necessarily aligned with those required for safety. Releasing a more capable model ahead of a competitor can bring considerable commercial rewards. Delaying that release for additional evaluation may produce little visible benefit, particularly if a rival proceeds anyway. The company exercising caution risks losing customers and investment, while the benefits of its restraint are shared across the industry.
This is not exclusively a Chinese problem. American companies face similar pressures, although their approaches to testing, disclosure and regulation differ.
The concern becomes more serious as models acquire the ability to operate autonomously. A conventional chatbot produces an answer that someone can examine before acting upon it. An AI agent may write and execute code, access information systems or perform sequences of actions with limited human supervision. Errors that were previously confined to a conversation can become operational incidents. This connects with a question I explored in Who signs their name to the answer?, concerning responsibility when organisations delegate consequential decisions to AI.
Our ability to evaluate these risks remains immature. Developers can test models against known categories of harmful behaviour, but cannot comprehensively anticipate every interaction between a model, its tools and an unfamiliar environment.
A Reuters report published on 9 October 2026 examined research into safety disclosures by major Chinese AI developers. The study found that only 31 of 857 model releases, approximately 3.6%, had publicly disclosed model-specific safety evaluations. Just nine had published those evaluations at or before launch.
These figures do not establish that the remaining models were released without testing, nor did the research provide an equivalent comparison with American developers. They do, however, illustrate how little independent evidence may accompany the release of increasingly capable systems.
China is not ignoring AI safety. Its government has introduced regulations and safety initiatives addressing generative AI services and emerging risks. However, regulatory priorities differ between countries. China’s approach places considerable emphasis on information control, social stability and compliance with national requirements, while Western frameworks have tended to give greater prominence to privacy, discrimination, transparency and systemic risks. There is overlap, but reaching agreement on what constitutes an acceptably safe model is difficult.
Open-weight models introduce another complication. Once distributed, their developers cannot reliably withdraw them, enforce safety updates or prevent others from modifying safeguards. There are substantial benefits to openness, including independent research and scrutiny, but these need to be considered alongside the difficulty of controlling subsequent deployment.
When competition becomes a race
There is a familiar pattern in technological competition. As one organisation announces a breakthrough, rivals accelerate their own development programmes. Investors expect progress, customers anticipate frequent improvements and researchers become reluctant to spend months evaluating systems that may soon be superseded.
In most industries, this competition benefits customers. Products improve, prices fall and innovation accelerates. AI introduces an uncomfortable complication because the capabilities being developed may eventually exceed our ability to understand their behaviour or reliably constrain their actions.
China’s emergence as a credible frontier competitor makes the problem harder to manage. Even if American laboratories agreed to more demanding safety evaluations, they would have limited influence over Chinese developers operating under different regulatory arrangements. The reverse is equally true. Neither side needs to be indifferent to safety for a dangerous dynamic to develop. Both merely need to believe that the other is moving faster.
Governments have reinforced this tension by treating AI leadership as a matter of national economic and strategic importance. Technological sovereignty and national competitiveness encourage investment, but they also make restraint politically difficult.
There is a strong case for international agreement on minimum evaluation standards, independent testing of particularly capable systems and greater transparency about serious incidents. Such cooperation would not require countries to abandon competition or disclose valuable intellectual property. Aviation, nuclear engineering and pharmaceuticals demonstrate that rigorous safety processes can coexist with commercial rivalry, although establishing them requires considerable institutional effort.
AI has not yet developed an equivalent international framework, and the technology is evolving considerably faster than the institutions attempting to govern it.
China’s progress deserves to be understood on its own merits. Its developers are producing sophisticated models, challenging assumptions about the cost of AI and creating credible alternatives to American technology. The competition is likely to accelerate innovation and make advanced systems accessible to many more organisations.
What remains uncertain is whether that same competition will leave sufficient room for the slower, less visible work of understanding how these systems behave, how they might fail and when they should not be deployed. Both China and the West may recognise the risks while neither feels able to slow down. That is a rather more difficult problem than deciding which country has the best AI model, and one for which another impressive benchmark result offers little reassurance.
