Chinese LLMs Move Beyond Token Sales: The Shift from Benchmark Competition to Real-World Deployment

The pace of Chinese large language model releases has accelerated noticeably in recent months, from efficient small and medium-sized parameter models to open-source weight versions that lead on performance. The speed of iteration on the technology supply side is plain to see. Yet a telling industry phenomenon is emerging: a handful of models account for the vast majority of API calls across the sector, but the revenue share they capture in the overall commercial value chain is far from proportional. This reflects the structural growing pains of an industry transitioning from a “technology arms race” to “commercial value realization.” This article attempts to strip away the surface-level metrics of benchmarks and call volumes to examine the core issues that Chinese LLMs must confront on the path to commercialization, as well as how developers and enterprise users can respond in this round of change.

1. The “Temperature Gap” Between Technical Prosperity and Commercial Monetization

The technical progress of Chinese LLMs is unmistakable. Whether it is the leap in multimodal understanding or the significant optimization of inference efficiency, both signal that the maturity of model foundations is approaching the international frontier. This rapid iteration stems partly from the accumulation of high-quality training data and innovations in algorithm architecture, and partly from the feedback loop provided by rich domestic application scenarios.

However, leadership in technical metrics does not directly translate into substantial commercial returns. There is a pronounced “temperature gap” in the current market—the underlying technology is running hot, while commercial monetization at the application layer often feels a chill.

1. Call Volume Is Not Revenue: The Imbalance in Value Chain Distribution

For many vendors providing foundation model services, even as API call volumes surge, the extremely low per-call pricing and frequent price wars driven by intense competition result in a prominent “revenue growth without profit growth” phenomenon. Behind this is a singular business model: if a large model is treated merely as a metered “token vending machine,” its value ceiling will be very obvious. Real commercial value tends to settle in solutions that solve specific business problems and are deeply embedded in workflows, rather than in raw compute consumption.

2. The Limits of the “Water Seller” Logic

In the early days, the industry often used the metaphor of “water sellers” to describe model providers—those who earn stable returns by supplying foundational tools. But in the LLM space, simply selling “water” (tokens) is not enough. The value of tokens is difficult for non-expert users to perceive intuitively. Enterprise customers need “a water treatment plant”—a complete system that directly produces business value. This requires model vendors to move downstream and engage with more complex tasks such as data governance, business logic mapping, and application integration.

2. Who Is Capturing the Commercial Value?

In the complex AI value chain, the bargaining power of the model layer, tool layer, and application layer differs markedly. Broadening the perspective, the commercial value differentiation in this round of generative AI can be attributed to three types of players:

1. Compute and Infrastructure Layer: As the physical foundation, these players benefit from overall demand growth and are relatively certain beneficiaries—but this is not purely a large model vendor capability.
2. Model and Algorithm Layer: This is the most competitive and homogenized tier. Without unique scenario binding, it is easy to fall into a pure price-and-parameter war of attrition—the awkward situation we currently observe of “capturing only 30% of the revenue.”
3. Application and Solution Layer: These players face customer pain points directly and hold pricing power. By deeply integrating large models with specific business processes and industry knowledge bases, they create irreplaceable value and typically capture the lion’s share of profits.

Therefore, the cry that “Chinese LLMs no longer want to just sell tokens” is essentially a desire for a “role upgrade”—from pure model outputters to solution architects and co-creators of business value.

3. The Paradigm Shift from “Parameter Competition” to “Outcome Delivery”

To escape the “selling tokens” dilemma, Chinese LLMs need to evolve deeply along the following dimensions:

1. Toward “Extreme Cost-Performance” and “Scenario Customization”

Future large models should fall into at least two categories: one is general-purpose, ultra-large parameter models with very high barriers, serving a small number of complex research or creative tasks; the other is vertical-domain, small and medium parameter models with low inference costs and excellent performance on specific tasks. Enterprise users do not need omniscience—they need high-precision output at controllable cost in specific domains (such as financial risk control or code generation).

Practical recommendation: When building applications, developers should abandon the惯性 of “one model for everything.” Adopt a “routing strategy” where simple tasks go to small models and complex logic goes to large models. Under this architecture, model vendors provide not just an interface but a cost-performance-optimized inference engine solution.

2. Emphasize “Context Engineering” and Knowledge Augmentation

Many application failures today are not due to insufficient model capability but rather the failure to effectively connect high-quality knowledge bases with model capabilities. LLM vendors need to focus more effort on building robust retrieval-augmented generation pipelines. This means not just providing a knowledge base tool, but offering a standardized set of services for data cleaning, chunking, vectorization, and retrieval ranking—ensuring that when the model answers professional questions, its supporting evidence is traceable and precise.

3. The Trade-off Between Private Deployment and Security Compliance

For heavily regulated industries such as government, finance, and healthcare, physical data isolation is a hard requirement. This demands that model vendors provide mature private deployment toolchains—including lightweight model distillation capabilities, efficient inference accelerator card adaptation, and comprehensive operations monitoring dashboards. Only by helping customers “bring the model home” while ensuring its runtime efficiency is no lower than the cloud version can vendors truly unlock the door to high-end markets.

Illustrative architecture (general pattern):

[Enterprise internal business systems]
   ↓ (invoke business APIs)
[Agent orchestration layer (task decomposition / tool selection)]
   ↓ (trigger)
[Mixture-of-experts model routing (small/large model switching)]
   ↓ (retrieve)
[Enterprise private knowledge base (vector database)] ← [Data cleaning & sync pipeline]

4. Rational Strategies for Developers and Enterprise Users

Faced with this paradigm shift, we as actual users should maintain a clear head and avoid being swept up by “parameter-only” thinking.

• Return to “Total Cost of Ownership”: When evaluating models, look not only at per-call pricing but also at the “total token consumption” and “human intervention cost” required to achieve business goals. A smart truncator is far more cost-effective than a cheap but indiscriminate component.
• Embrace “Agent-based” Development: Future applications are not just passive question-answering tools but intelligent agents that can proactively execute tasks. This means developers’ core skills will shift from “writing prompts” to “designing workflows, defining tool specifications, and orchestrating task state machines.”
• Beware the “Data Feeding” Trap: When calling external platforms, pay close attention to data permissions and privacy boundaries. Prioritize service providers that commit to not using data for model iteration and support private deployment—this is a key red line for protecting enterprise intangible assets.

5. Conclusion: Solve Real Problems to Create Real Value

The current situation of “capturing 70% of call volume but receiving only 30% of revenue” is somewhat bitter but carries profound warning. It reminds all industry participants: traffic and call volume alone are empty heat; only the ability to solve complex real-world problems is genuine value.

The next leg of the journey for Chinese LLMs is not about competing on leaderboards but about proving themselves on factory floors, in hospital examination rooms, in fields and farmlands, and in code repositories. Only when model capabilities are deeply dissolved into the business processes of thousands of industries and redefine the mode of human-machine collaboration will the “unallocated 70%” of commercial value return as ecosystem dividends to every practitioner who深耕 scenarios. This is not a simple business strategy adjustment—it is the coming-of-age ceremony for China’s entire AI industry as it matures from adolescence.