China will likely have its own Mythos-like model around February 2027

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China will likely have its own Mythos-like model around February 2027<br>A forecast built on chip counts, compute budgets, and Malaysian data centers.<br>Hamish Low<br>Jul 03, 2026

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Claude Mythos marked a step change in the cyber capabilities of AI models. A Mozilla executive called Mythos “as capable [as] the world’s best security researchers”. Epoch AI’s analysis of cyber benchmarks shows Mythos about seven months ahead of what prior trends would predict, and Mythos also has a clear lead on some of the UK AI Security Institute’s most challenging internal cyber evaluations:

See UK AISI’s evaluation of GPT-5.5-Cyber.<br>Given the dual-use nature of these cyber capabilities, when China will develop its own Mythos-like model is a critical national security question. Chinese firms have been making strong progress in cyber-capable models, with GLM 5.2 from Z.ai in particular posting impressive results on coding benchmarks such as FrontierSWE and PostTrainBench. A recent Wall Street Journal article claimed it had matched Mythos, but that was highly misleading: there is no evidence that GLM 5.2 can do the unstructured vulnerability discovery and exploit chaining that made Mythos a step change. On the Epoch Capabilities Index (ECI), GLM 5.2 falls between GPT-5 Pro (August 2025) and GPT-5.2 Pro (December 2025), placing it about seven months behind the frontier.<br>My central estimate for when a Chinese firm will have fully developed a Mythos-like model is around February 2027 (90% CI: October 2026 to September 2027), exactly a year after Mythos itself was ready for internal use in February 2026. This matches Elon Musk’s prediction of Q1 2027, with the caveat that Musk means a model with the “true usefulness” of Mythos, not simply one that matches it on certain cyber benchmarks. (The founder of Z.ai disagreed that it would take that long.)<br>I arrive at this date by building a model that uses compute input as the best proxy for Mythos’s capabilities. The model:<br>Estimates how much compute was used to train Mythos, based on Anthropic’s available training hardware and the likely timeline of its training

Adjusts this Mythos compute estimate for algorithmic progress over time (including distillation)

Matches it to an estimate of how much compute Chinese firms have, and, crucially, how much they are willing to spend on training a single model

Produces a date by which I’d expect a Chinese firm to pre-train a Mythos-like model, to which I add a period for post-training and reinforcement learning (RL) to reach the final prediction of a fully developed model

The key takeaways from building this model were:<br>The biggest uncertainty in Chinese firms’ frontier AI development is not simply access to compute but the willingness to commit large quantities of it to frontier model training. If Chinese hyperscalers were to seriously reallocate compute internally, or if the industry were to concentrate compute on model development, the model’s projected timeline could shorten a lot.

The largest sources of compute for Chinese firms remain overwhelmingly US chips, acquired through legal purchases, smuggling, and, most significantly, remote access in Southeast Asia.

A short explanation of the model is in the next section; a full account of its parameters is in the appendix.<br>Importantly, the model is based on how hyperscalers and AI start-ups in China have allocated their compute in the past. This could change soon. Chinese hyperscalers have generally only committed single-digit percentages of their compute towards large pre-training runs, with much directed towards non-LLM business (e.g., recommender algorithms), model inference, or renting out compute through their cloud services. This matches the behavior of US hyperscalers: inference and other non-LLM workloads are generally the most effective uses of their compute on the margin. But given enough pressure or incentive, this compute could be reallocated towards frontier AI development. Such a reallocation only becomes easier as Chinese hyperscalers build out huge clusters of advanced NVIDIA chips in Southeast Asia, offering greenfield capacity that could be dedicated to frontier training.<br>Apart from within-firm reallocations, there could also be market reallocations. For example, hyperscalers with more abundant compute could either buy out human-capital-rich start-ups or double down on more intensive, large-scale cloud service contracts with them, akin to OpenAI’s or Anthropic’s extensive partnerships with US hyperscalers. Alternatively, smaller Chinese AI start-ups could merge, or hyperscalers could step up their competition for top researchers.<br>Concentrating compute on frontier models in this way would materially pull forward the date at which, on compute alone, you would expect a Chinese firm to train a Mythos-like model.

In practice, far more goes into model development than hitting a...