China's three major telecom operators accelerate token commercialization, prices drop from 5 yuan to 1 yuan per million tokens

As artificial intelligence permeates every industry, the commercialization of the token economy is drawing increasing attention.

Token Factories Enter Pilot Phase, but Core Challenges Shift

The newly released China Token Factory Development White Paper (2026) (hereafter referred to as the White Paper) indicates that token factories have moved beyond the concept incubation stage and entered a multi-track experimental phase encompassing project construction, capability procurement, and platform operations. However, from an industry perspective, the proportion of projects that have actually entered production and operation remains low. The core challenge is also evolving: previously, the focus was on "whether computing power exists"; now, the question is "whether stable, continuous production of cost-competitive effective tokens can be achieved."

This shift is driven by the rapid growth in AI application scale. According to data from the National Data Administration (NDA), China's average daily token calls were approximately 100 billion at the beginning of 2024, surged to 100 trillion by the end of 2025, and exceeded 140 trillion as of March 2026. NDA Director Liu Liehong previously stated that tokens are measurable, priceable, and tradable, serving as the "settlement unit" connecting technology supply with commercial demand. Meanwhile, data from the Ministry of Industry and Information Technology (MIIT) shows that as of the end of June 2026, China's intelligent computing power scale had reached 2,185 EFLOPS, with an overall rack utilization rate of 71.4% for national computing infrastructure.

From Selling "Resources" to Selling "Services"

According to the White Paper, a "token factory" is defined as an industrialized production system that "uses electricity as the energy input, GPU hours as the production process, tokens as the product, and unit effective task cost as the core metric." Notably, the emphasis is not on simply increasing token quantity, but on "effective tokens."

Under this definition, only output tokens that simultaneously meet service-level requirements such as latency, availability, and accuracy—and are ultimately seen and used by end users—are considered effective tokens and enter commercial measurement.

Industry observers believe the concept of "token factories" reflects a shift in the evaluation system for AI infrastructure. In the past, after a data center or intelligent computing center was built, investors primarily focused on server, GPU, and facility scale. But in the inference stage, what truly determines commercial value is whether these hardware assets can be utilized continuously, stably, and efficiently. This means AI infrastructure is gradually transitioning from selling "resources" to selling "services."

This trend aligns with the development stage of the computing industry itself. MIIT data shows that as of June 2026, the national computing facility rack utilization rate was 71.4%, with intelligent computing power reaching 2,185 EFLOPS. Faced with growing computing supply, simply expanding hardware scale no longer automatically translates into economic value. Improving computing utilization, reducing unit inference costs, and converting computing power into stable AI services are becoming key priorities for the next phase of industry development.

At the inaugural Token Cloud Service Conference, the China Academy of Information and Communications Technology (CAICT) proposed that tokens are gradually becoming an important service unit connecting computing supply, large model capabilities, and industry applications. It called for further improvements in standards for service architecture, resource scheduling, metering and billing, quality assurance, and security and trustworthiness. The national standard Artificial Intelligence Token Metering Framework and Testing Methods has also been initiated, with participation from organizations including the China Electronics Standardization Institute.

Wang Qinglin, Manager of the Ruihengda Industrial Research Institute, believes the impact of token factories may not be limited to adding a new type of computing project. Instead, it is driving the industry to use multiple effective metrics to measure the return on investment (ROI) of AI infrastructure. For local governments, computing investors, and cloud service providers, this also means the previous approach of "build first, find demand later" is facing adjustment. Whether a project can generate sustained orders will increasingly determine investment returns.

Operators Accelerate Efforts to Capture the Token Gateway

While token factories remain in the pilot phase, telecom operators—also actively exploring token operations—have entered a period of rapid product and platform iteration.

According to a recent research report by Omdia China Research Analyst Xia Maosen, when the three major operators first launched token packages in May, prices generally ranged from 3 to 5 yuan per million tokens. By July, core package prices had dropped to approximately 1 yuan per million tokens, gradually approaching the levels of leading domestic cloud service providers. This round of price adjustments indicates that operators are quickly abandoning the strategy of profiting solely from token price margins, instead seeking comprehensive value from networks, computing power, models, channels, and industry customer resources.

Strategically, the three operators have adopted different focuses:

• China Mobile emphasizes a platform-based approach. In June, it established a group-level Token Office responsible for integrating the entire process of "creating, delivering, and applying tokens." Simultaneously, China Mobile began combining tokens with traditional services like 5G packages and offering multi-model capabilities to individual and enterprise users through an AI distribution platform. Xia Maosen believes the significance of this move lies in unifying previously fragmented token business pilots at the group level, reducing coordination costs between different business units.

• China Telecom focuses more on productization. From its "From Bit to Token" value proposition to its Tianyi Cloud Token product and the Xingchen Token Hub, China Telecom aims to package tokens directly as standardized products for individuals, families, and enterprises. It has already launched a personal package at 9.9 yuan/month for 10 million tokens and an enterprise package at 299.9 yuan/month for 150 million tokens. Its Token Hub offers over 100 model options. This approach closely resembles the package-based, product-oriented operations of traditional telecom services.

• China Unicom emphasizes industry and scenario applications. After a July organizational restructuring, China Unicom incorporated "strengthening token operations" into the responsibilities of its Product Innovation and Marketing Department. At its recent partner conference, it further proposed enhancing the full-chain capability of token "creation, storage, transfer, cleaning, and application," and fully opened its "Agent + Token + AI Cloud" operating system.

China Unicom Chairman Dong Xin publicly stated that the company aims to integrate computing supply, model calls, and scenario returns into a "single account," driving the transition from "transporting traffic" to "supplying tokens," from "transmitting bits" to "empowering intelligence," and from "connectivity" to "productivity." China Unicom also launched Unicom Cloud 8.0, a trusted token infrastructure, and Unicom Yuanjing MaaS, seeking to upgrade tokens from a mere billing unit to a foundational service for industry clients.

More notably, China Unicom is addressing the long-standing "metering black box" in token commercialization. The company has introduced the "Yuanzhu Metering and Billing System" to standardize metering across different models with inconsistent token standards and difficult-to-trace usage. Unicom Cloud 8.0 further proposes "six-coordination" involving computing-electricity, computing-network, computing-chip, computing-data, computing-model, and computing-security, targeting key scenarios such as government services, healthcare, education, finance, and state-owned enterprises.

The strategies of the three operators indicate that tokens are no longer just a new package for them. They are redefining the relationship between operators, the AI industry, and end users.

Omdia's assessment aligns with this logic: token services themselves are rapidly commoditizing. In the long run, the real opportunity for operators to build differentiated advantages lies not in simply "selling tokens," but in leveraging their existing capabilities—national networks, computing infrastructure, enterprise customers, billing systems, and security—to become the AI infrastructure and delivery layer between models and end users. In other words, tokens may be just the gateway; the real prize is the AI service orchestration and industry customer service rights behind the tokens.

Three Hurdles Remain for Mature Commercialization

The boom in AI development has provided a clear demand foundation for the token economy. More importantly, token consumption is extending from C-end chat and Q&A to more complex industry scenarios such as enterprise office work, software development, and industrial production.

The MIIT has explicitly called for deepening "AI + Manufacturing," promoting the integration of large models into R&D, production, marketing, and operations management. This suggests that as enterprise AI applications gradually enter production systems, token demand is expected to shift from sporadic calls to sustained, stable industrial loads.

However, significant challenges remain for the token industry to move from rapid growth to mature commercialization:

1. Metering Standards: Although tokens inherently possess metering attributes, the computational cost and ultimate value of a token vary across different models, tasks, and context lengths. Wei Kai, Director of the CAICT AI Research Institute, previously noted that the industry should not focus solely on token unit prices. Standards for measuring "high-quality tokens" are needed. This aligns closely with the White Paper's emphasis on "effective tokens": future price competition will likely shift from "cost per million tokens" to "how many tokens and how much computing power are needed to complete a specific task, and what is the final outcome."

1. Cost and Profitability: Token factories are capital-intensive, with significant upfront hardware and infrastructure investments but relatively low marginal costs. Consequently, if utilization is insufficient, the unit cost of effective tokens can rise rapidly. The White Paper specifically notes that the impact of token factory utilization on unit costs and project profitability is even greater than hardware procurement prices. The same hardware can show significant differences in profitability when carrying different business loads. Future computing projects will need to compete not just on "who builds bigger," but on "who can keep computing power fully utilized over the long term."

1. Value Realization: For enterprise clients, purchasing tokens is not the end goal. The true business outcome is reducing R&D costs, improving customer service efficiency, optimizing production processes, or enabling agents to complete tasks. Therefore, token operations may eventually evolve from "pay-per-call" to "pay-per-task, pay-per-outcome, or pay-per-service-level."

Huang Wei, Deputy Director of CAICT, publicly noted that tokens have given AI services a metering foundation comparable to electricity or data traffic for the first time. However, moving from "meterability" to "tradability" and "scalable operations" still requires improved standards, service quality, and business rules.

In the near future, the token industry is likely to remain in a phase of "rapid growth" coexisting with "model experimentation." Token factories will address large-scale production; operators and cloud service providers will handle supply, scheduling, and delivery; and model and agent companies will continue to extend along the application value chain upstream and downstream. As various segments of the industry chain gradually form unified metering standards, tokens may truly evolve from a mere number on a large model API bill into a fundamental commercial unit connecting computing power, data, models, and the physical economy.