Inference cost becomes new focus: large model competition enters 'cost-performance' phase
In October 2026, the large model industry quietly shifted direction. The arms race around model parameter scale and multimodal capabilities in the past two years is gradually being replaced by the more pragmatic metric of 'per-token cost'. Recent enterprise application reports from several leading AI companies show that inference costs have surpassed training costs, becoming the primary barrier to enterprise AI deployment. To address this challenge, a number of aggregation platforms represented by AI hub have begun integrating high-quality model resources and offering flexible token wholesale plans, allowing small and medium-sized enterprises to access top large models at near-cost unit prices. Insiders point out that this shift marks the transition of large models from 'luxury goods' to 'daily necessities'.
Rise of token wholesale model: from pay-per-use to bulk wholesale
Traditional large model APIs mostly charge per call or per token usage, with volatile pricing and hidden costs. In October 2026, multiple platforms simultaneously launched a 'token wholesale' model — enterprises can prepay a certain amount to purchase token packages at prices far below retail rates, and even support cross-model and cross-time usage. This model essentially borrows the concept of reserved instances from cloud computing. Meanwhile, AI relay stations serve as key infrastructure, helping enterprises achieve intelligent routing between multiple models: when a model is overloaded or its price fluctuates, the system automatically switches to a more economical alternative, ensuring enterprises always use the cheapest token combination. According to industry observers, the emergence of such AI API relay stations allows enterprises to avoid being locked into a single model vendor, significantly reducing lock-in risk.
AI hub and AI channel agent ecosystem: dual transformation of aggregation and distribution
Behind the implementation of the token wholesale model is the rapid maturation of AI hub platforms. These platforms not only aggregate dozens of mainstream large models from domestic and international sources but also unify billing and scheduling interfaces through their own token relay stations. Enterprise developers need only integrate one AI hub SDK to simultaneously invoke models such as GPT series, Claude, 文心一言, 通义千问, and others. More notably, the AI channel agent and AI large model agent models are emerging — many small and medium-sized cloud service providers, ISVs, and even individual developers have become distribution nodes for the platforms, packaging and reselling token packages using their own industry resources and earning margins. This multi-tier distribution system accelerates market penetration but has also sparked industry discussion about pricing transparency. However, at present, this model effectively reduces customer acquisition costs and is particularly popular with enterprises in vertical sectors such as education, finance, and healthcare.
Policy and capital dual boost: subsidies for AI computing centers accelerate cost reduction
In October 2026, the Ministry of Industry and Information Technology, together with several other ministries, issued the 'High-Quality Development Action Plan for Artificial Intelligence Computing Infrastructure (2026-2028)', which explicitly grants subsidies of up to 30% for eligible enterprises using domestic AI computing center inference services. At the local level, cities such as Beijing, Shanghai, Shenzhen, and Hangzhou have introduced supporting 'computing vouchers' policies, focusing on supporting small, medium, and micro enterprises to adopt AI large models for innovation. Benefiting from this, multiple AI computing center operators have begun cooperating with platforms like AI hub, offering combined 'computing power wholesale + token wholesale' discounts to further lower enterprise usage barriers. In the capital market, investment and financing events this quarter have also clearly tilted toward the 'cost optimization' track — several startups focused on model inference scheduling and token agent services have received tens of millions of yuan in funding, with valuations generally up 20% to 40% from the beginning of the year.
Outlook: from 'usable' to 'great to use', inference cost optimization remains the main theme
Although the token wholesale model has achieved initial results, the industry still faces challenges such as uneven model quality and latency in multi-model switching. Industry developments in October 2026 indicate that the next phase of competition will focus on balancing 'cost, quality, and latency'. It is expected that by early 2027, more platforms will launch token agent markets based on real-time bidding mechanisms, allowing enterprises to bid for AI model calls just like bidding for cloud computing resources. Meanwhile, as domestic large models outperform foreign competitors in some scenarios, AI relay station services combined with local subsidy policies are expected to achieve the 'global lowest inference cost' first. For the vast number of AI application developers, 'doing more with less' is becoming a reality.