Policy and investment dual drive: large model deployment enters acceleration phase
In October 2026, the AI large model industry reached a critical turning point. The national government issued a series of targeted policies for AI infrastructure, while multiple provinces and cities simultaneously introduced computing subsidy and data openness plans, explicitly encouraging enterprises to adopt large models for business innovation. At the same time, capital markets shifted their AI investment focus from model R&D to application deployment, with several top investment funds announcing the establishment of ten-billion-yuan industry funds dedicated to supporting large-model deployment in vertical sectors. Industry insiders point out that the high inference cost of large models has been the main bottleneck to adoption over the past year, and recent policy guidance along with capital injection are creating favorable conditions for cost optimization.
On the investment front, in Q3 2026 there were over 120 financing events related to AI large models, nearly half of which went to startups focused on model services and middleware platforms. These companies specialize in reducing API call costs, optimizing token consumption efficiency, and distributing model capabilities to small and medium developers through channel agent models. The industry widely believes that with policy dividends and increased capital, the large model industry has officially moved from the "model building" stage to the "model using" stage, and platform-level service innovation will become a key catalyst.
Platform announcement: AI hub channel agent plan officially launched
Recently, a leading domestic AI platform issued a public announcement, officially launching the "AI hub" channel agent plan. The plan aims to aggregate API capabilities from multiple mainstream large model providers, delivering services to enterprise customers through a unified interface and pricing system. According to the announcement, the platform will open AI large model agent permissions, allowing partners to directly access underlying model resources via the AI channel agent model without building their own computing infrastructure. Notably, the plan introduces a "token wholesale" mechanism for the first time: enterprise users can purchase a large quantity of tokens in bulk based on estimated usage, thereby enjoying lower prices than pay-as-you-go—the platform claims this is one of the cheapest token plans on the market.
To support the channel agent system, the platform simultaneously launched the AI relay station and token relay station features. The AI relay station handles routing and load balancing for model requests, intelligently selecting the lowest-cost or best-performing model based on task characteristics; the token relay station focuses on usage management and billing optimization, supporting the interchange of token quotas across multiple models to help enterprises control budgets. The platform spokesperson stated that this design allows small and medium enterprises to access top large models at extremely low cost, and it is expected to reduce monthly API spending for enterprises by more than 40%.
How the token wholesale model changes industry cost structure
For a long time, the per-token billing model for large model APIs deterred many small and medium developers—individual inferences seemed cheap, but cumulative costs from high-frequency calls were staggering. The newly introduced token wholesale model borrows from the reserved instance approach in cloud computing: users prepay for a certain amount of token quota, locking in call prices for months or a year ahead, and unused tokens can be shared across models within the platform. Combined with the intelligent scheduling of the AI API relay station, this model further reduces wasteful token consumption. For example, for the same prompt, the relay station automatically selects the best-value model version, avoiding hidden cost increases from model upgrades.
Industry analysts point out that token wholesale is not simply a price promotion but a fundamental restructuring of the AI service business model. Previously, model vendors relied on per-call commissions; now, through bulk pre-sales and channel agents, they shift revenue from transaction fees to subscription and ecosystem profit-sharing. This stabilizes the platform's cash flow while giving developers a more predictable cost curve. At the same time, the AI hub platform plans to channel a portion of token wholesale revenue back to channel agent partners, forming a virtuous cycle of transparent pricing, controlled usage, and shared returns.
Industry outlook: new opportunities for agents and multimodal applications
Cost structure optimization is giving rise to new application paradigms. At several industry technology summits held in October 2026, large model-based intelligent agents and multimodal interactions became the hottest topics. With the token wholesale model drastically lowering the marginal cost of each call, developers are beginning to build more complex multi-turn reasoning tasks, such as automated customer service, code review assistants, and real-time data summarization. These scenarios demand high inference counts and long context lengths, and were previously limited to small-scale pilots due to cost constraints. Now, with the elastic scheduling of the AI relay station and the price advantage of token wholesale, large-scale deployment is feasible.
Additionally, policy requirements for data security and model compliance are pushing platforms toward more transparent channel agent models. A recent AI application guideline from a local regulatory authority explicitly recommended that enterprises prioritize platforms offering token relay station services, to facilitate auditing and tracking of token flows. This policy signal has further accelerated the adoption of the AI channel agent model, and several large state-owned enterprises have begun purchasing token wholesale packages from the AI hub platform for intelligent upgrades to their internal office workflows. It is foreseeable that with the resonance of policy, capital, and business models, the fourth quarter of 2026 will become a watershed moment for large models, transitioning from a technology hot spot to an industry standard.