‘Open weight models are closing the capability gap with frontier models at a fraction of the cost’: Cheap Chinese AI models could spell trouble for US big tech

The rising quality of cheaper Chinese AI models could wreak havoc on the US tech industry, according to Juniper Research.

Analysis from the consultancy suggests leading US tech giants could find themselves losing out to Chinese rivals offering alternatives in a bid to break the industry’s global dominance.

Juniper Research pointed to figures from OpenRouter, which show that just 30% of the work on that platform was through AI models offered by leading providers such as OpenAI, Google, or Anthropic. That marks a steep fall from 70% last year.

The report noted that Chinese models typically run 60% to 90% cheaper than rivals from Anthropic and OpenAI. The pricing challenge from China was first faced last year when DeepSeek hit the market, tanking tech stocks briefly by offering high-level performance at a much lower cost.

This year, Alibaba's Qwen models had overtaken Meta's Llama as the most downloaded open model system, the report noted.

February marked the "real crossover into cheaper inference", Juniper added, with Chinese models processing 4.12 trillion tokens on OpenRouter vs 2.94 trillion for American models.

That's all down to lower prices for capable-enough open models, the consultancy noted.

“Open weight models are closing the capability gap with frontier models at a fraction of the cost," said Juniper Senior Analyst Jawad Jahan.

The trend is also being aided by more efficient designs, such as Mixture of Experts (MoE) models, which only use necessary parameters for a given query.

"This cuts compute-per-query without shrinking the model’s capability," the report noted. "DeepSeek is the cleanest example of this. Its V3 models carry 671 billion parameters in total, but only 37 billion of them fire for any given token. This led to a cut of V3.2’s price by more than half, as a result of lowering the cost of long-context inference."

Cutting costs with Chinese AI models

According to Juniper, DeepSeek's V4 Flash cost $0.14 per million input tokens against $5.00 for OpenAI’s GPT-5.5, while Claude Opus 4.8 was at the time charging about $5 for input and $25 for output per million tokens.

"This pricing gap has existed ever since the release of the first Chinese open-sourced model that was able to rival frontier organizations in the West," the report noted.

Shifting demand has altered the picture, however, with programming rising from 11% to more than half of OpenRouter's token volume. Agentic workloads also account for the majority of output, Juniper said.

"Given the large call frequency for agentic workloads, per-token pricing starts to become a real budget consideration and constraint," the report noted.

Funding the AI roll-out

That raises a problem, one that Juniper refers to as an "existential risk": can we afford all the borrowed billions being spent on data centers via "complex financing agreements" if customers stop paying for the very best models and instead choose cheaper Chinese options?

The firm argued that if the price of running AI falls too low, companies will lose the ability to make revenue from it to pay for all this investment.

That concern isn't new, either. Investors have been questioning the massive spending on data centers, with Juniper noting that the four major hyperscalers are expected to spend $725 billion on capital expenditure this year, up by 77% compared to the year prior.

Beyond the raw figures, Juniper pointed to the convoluted ways American companies are gathering the funds to pay for such projects, largely spurred by the AI infrastructure spending outpacing available cashflow and revenue.

The firm noted that this year the top five hyperscalers will spend more than a trillion dollars on infrastructure for AI.

"This figure exceeds their combined earnings and free cashflow," the report said.

Juniper claimed this has driven a rise in "off balance sheet financing" — in which companies don't borrow directly but build standalone legal entities to carry debt, later leasing the infrastructure back to the hyperscaler.

Circular financing is also a concern, which Juniper defines as a supplier investing in a company with the understanding the recipient will spend those funds buying the supplier's products.

The consultancy pointed to OpenAI's deals with Oracle and Nvidia as prime examples.

Chinese challenge to funding structures

If revenue from AI starts to shift to China, these spending levels and funding structures could prove problematic, the analyst firm noted.

"If this trend continues, the inference revenue underwriting the Western datacentre build-out weakens, and, correspondingly, the financing structures resting on that revenue,” Jahan commented.

That's exacerbated by the fact Chinese companies don't face the same issues, with funding either so far provided by external financial businesses, particularly in the case of DeepSeek.

Similarly, they aren’t beholden to a business model that sees cloud providers like Alibaba and Baidu effectively give the model away, then sell the compute to make use of it.

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