1. October trend: AI platforms compete to offer 'cheapest tokens'
In October 2026, a fresh wave of price wars erupted in China's AI large model sector. Unlike the model capability contests earlier this year, the focus this time is on inference costs. Several mainstream AI platforms successively announced upgrades to their token wholesale modules, introducing on-demand procurement plans for enterprises. One leading platform announced early in the month that its token wholesale service would allow enterprise customers to access inference resources at unit prices significantly below the industry average, promising 'no inventory threshold.' Hot on its heels, another platform touted the 'cheapest tokens' slogan, directly targeting the pain point of high call costs that have long constrained small and medium-sized enterprises. Behind this flurry of moves lies an inevitable industry shift from model arms races to commercial deployment—when foundational capabilities become commoditized, the player that can save customers real money will secure its niche in the ecosystem.
2. Token wholesale model: Cost-reduction logic from pay-as-you-go to bulk purchasing
Traditional large model APIs typically charge per token in real time, exposing enterprise users to price fluctuations and uncontrollable costs for long-text inference. Since the third quarter of 2026, multiple platforms have introduced token wholesale models, allowing enterprises to purchase token quotas in bulk via prepayment, with per-token prices cut by 30% to 50%. More critically, this model is deeply tied to the AI channel agent ecosystem. Through aggregation platforms like AI hub, enterprises can dispatch inference requests across different models via an AI large model agent for unified scheduling, further spreading costs. As one CTO of a mid-sized AI company revealed at an industry forum: 'By connecting three models simultaneously through an AI channel agent and locking in six months of usage via token wholesale, our inference costs dropped by 60%.' Notably, some platforms have also introduced token agent services, allowing enterprises to resell idle quotas to other users, creating a secondary market that further energizes token exchange.
3. AI relay stations: Infrastructure for optimizing inference costs
As the token wholesale model gains traction, a technical architecture known as the 'AI relay station' has quietly become popular. Essentially an intelligent routing layer between users and models, it dynamically selects the most suitable model interface and allocates tokens based on request type, latency requirements, and cost budget. Several cloud vendors announced in October that they had launched their own AI API relay stations, claiming they could reduce inference costs by an additional 15% to 20% without sacrificing performance. For example, for simple text classification tasks, the AI relay station automatically routes requests to cheaper lightweight models, while complex reasoning tasks are handled by flagship models. This token relay station design also supports multi-channel agent access, helping enterprises implement a 'hybrid inference' strategy. Industry analysts note that the AI relay station has become an indispensable middleware for large model deployment, its value comparable to the optimization that CDNs once brought to content delivery.
4. Policy push: Standardization of large model services and the token exchange market
In mid-October 2026, the Ministry of Industry and Information Technology, together with multiple departments, released the 'Draft Guidelines for Service Capability Grading and Transaction Standards of AI Large Models (for Public Comment),' which for the first time incorporates commercial practices such as token wholesale and token agents into standardized management. The document requires platforms to disclose token pricing mechanisms, cache ratios, and refund rules, and encourages the establishment of open and transparent token exchange markets. The policy direction has directly accelerated industry consolidation—several small and medium-sized model providers have begun jointly launching AI channel agent networks, leveraging shared token pools to reduce individual platform operating costs. According to one policy consultancy, once the guidelines take effect, they could give rise to a hundred-billion-yuan token trading market, similar to the reserved instance bidding market in cloud computing years ago. For enterprise users, this means the 'cheapest tokens' are no longer just short-term promotions but a stable, predictable long-term cost structure.
5. Outlook: Cost efficiency competition will reshape the AI ecosystem
From the October developments, it is clear that the AI large model industry is undergoing a paradigm shift from 'capability first' to 'cost is king.' The maturation of infrastructure such as token wholesale, AI relay stations, and channel agents allows enterprises to access intelligent services as flexibly as they procure cloud computing resources. In the next six months, we expect to see more third-party service platforms centered around token agents—platforms that do not directly provide models but act as intermediaries to optimize supply-demand matching. For AI platforms, building their own AI hub and integrating token relay station capabilities will be key to staying competitive. As one industry report put it: 'Whoever masters the cheapest tokens holds the key to AI adoption.' This trend, already accelerating since October, will profoundly reshape the entire industry landscape by 2027.