The latest Chinese AI models may indeed work for enterprises, but only in a handful of specific applications

Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. 

But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask if these models are worth using, even in a limited fashion.

Former Walmart risk official Steven Eric Fisher, now an independent cybersecurity and risk advisor, thinks they should at least take another look.

“Enterprises should take these models seriously, but neither adopt nor reject them solely because they are Chinese,” he said. “They should be assessed like any other critical technology dependency: jurisdiction, ownership, training and software provenance, licensing, data handling, hosting, security, reliability, and the ability to independently test their behavior. Geopolitical exposure is a legitimate risk factor, but it should be incorporated into technical and supply-chain diligence rather than used as a substitute for it.”

Choose applications with care

He added, “Chinese models may be especially valuable for coding, multilingual processing, high-volume document analysis, research, synthetic-data generation, and privately operated security or forensic workflows, but they should be subject to task-specific testing rather than broad benchmark claims.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, agreed that the Chinese models can work well if they are only used in carefully chosen applications. 

“Although Moonshot’s K3 still trails Claude’s Fable 5 and GPT 5.6 Sol on performance and user experience, good companies that have governance and prompt guardrails will not face the instability and improvisation of [the Chinese] models,” he said. “These models will win in usage. US frontier models are leading as the best models, but Chinese models will be sufficient for high-volume, low-drama tasks that cost less for non-critical transactions.”

On the flipside, Bellamkonda suggested a variety of areas where enterprises should avoid Chinese AI models, including “customer-facing work without a human in the loop, regulated or sensitive data, and anything where a hallucinated answer creates legal or safety exposure. That is where the reliability gap and the political-radioactivity concern both bite, and where the closed American models still earn their premium.”

Bellamkonda said he didn’t see the differences in data reliability, mostly involving hallucination rates, as meaningful for enterprise AI strategy decisions.

“Every open-weight model in this class can get facts wrong or make things up. That is fixable with the right setup, so it is not a reason to avoid these models,” he said. “For high-volume tasks with clear limits, you feed the model your own trusted documents to answer from, and you keep a person checking the output. That combination is safe for production. The model on its own is not.”

Too early for enterprises to consider

However, not everyone agrees that the latest Chinese models have earned their place as enterprise AI decision options. 

Cybersecurity consultant Brian Levine, executive director of FormerGov, focused on Chinese technology concerns when he worked for the US Justice Department as its representative in the US law enforcement Joint Liaison Group (JLG) with China. 

“It is way too early for US enterprises to seriously consider these models,” he said. “Until proven otherwise, enterprises should assume that if they use these models, they may be granting China complete access to everything they do through the models, and potentially access to their networks and employees more broadly. At this point, any pros of using such models are strongly outweighed by the potential security, confidentiality, and reliability concerns.”

Tom Findling, CEO of Conifers.ai, was equally emphatic that enterprise CIOs need to steer clear of these newer Chinese models. 

“Using them inhouse? Absolutely not. You simply don’t know what is planted inside of it and you don’t know what training data is put into them,” Findling said. 

Mike Wilkes, enterprise CISO at Aikido Security, added that the very attractive pricing for these Chinese models may be appealing, but suggested that, despite the low cost, they’re ultimately too risky.

“Enterprises should take these models seriously, but not romantically. Parameter count is horsepower measured in a showroom, not braking distance in the rain,” he said. “The real tests are reliability on your data, the cost of a wrong answer, and whether the model behaves predictably under pressure.”

He noted that the benchmarks on the latest open-weights models are impressive, and very close to those of the frontier lab models, which makes the cost ”incredibly seductive, especially when a team does not want to risk their data being used to train those frontier models.”

But the Chinese models can still work in specific circumstances. “The strongest value will be in bounded, reversible and inspectable work: coding inside a sandbox, multilingual translation, document triage, data extraction and other high-volume tasks where outputs can be verified,” he said. “Cheap intelligence is valuable, but only when it is not mistaken for trustworthy judgment.”

Wilkes added that the regulatory issues surrounding Chinese models can be especially problematic. Texas, for example, has banned their usage.  

A rational choice for some workloads

However, Yuri Goryunov, CIO of consulting firm Acceligence, argued that CIOs should seriously consider these models. 

“Counterintuitively, the biggest benefit of Kimi and models like it is the lack of guardrails,” Goryunov said. “Think of it as stick shift cars in the era of automatics. If you want ease and comfort, stay with the frontiers because they have cruise control, shift the gears for you and they decide when. If you want performance and control, expand your horizons. But a stick shift assumes you know how to drive one: you bring your own governance, your own evals, your own safety layer. That’s a cost and specialized talent, which is super rare, and for the right organization it’s also the whole point.”

Goryunov’s bottom line: “For internal, high-volume, well-harnessed workloads, [the Chinese models] have moved from ‘watch list’ to ‘rational choice.’”