Top model vendors compete in October with new releases
In the just-concluded October, the AI industry saw a surge of product and model launches. According to multiple industry media reports, leading companies including OpenAI, Google, and Anthropic all released significant updates in the month. OpenAI launched a new generation of its flagship model, achieving notable breakthroughs in long-context processing and instruction-following capabilities; Google released a multimodal optimized version of the Gemini series, enhancing video understanding and real-time interaction; Anthropic also conducted a mid-cycle upgrade of its Claude product line, further reducing latency. Meanwhile, domestic vendors such as Baidu and Alibaba also launched lightweight models targeting enterprise private deployment scenarios. Behind this wave of releases, a common trend clearly emerged: inference efficiency and cost control have become the core metrics of competition among companies.
Inference efficiency and cost control become the competitive focus
New models generally employ optimization techniques such as sparse attention and speculative decoding at the architectural level, significantly increasing the number of tokens that can be processed with the same computing resources. However, the enhanced model capabilities also drive greater demand for calls, and enterprise users are increasingly sensitive to inference costs. Multiple industry insiders point out that relying solely on models themselves to reduce costs is no longer sufficient for large-scale applications; supporting scheduling and procurement infrastructure is needed. It is against this backdrop that a new service model—token proxy—has begun to enter the mainstream. This model offers developers much lower unit prices than on-demand calls through batch purchasing, intelligent routing, and cache reuse, essentially shifting API calls from retail to wholesale.
AI API relay stations and token proxy models gain popularity
In this month's industry developments, multiple platforms announced the launch or upgrade of their AI API relay station services. These relay stations act as an intermediary layer between model providers and users, aggregating interfaces from multiple mainstream models and incorporating load balancing, failure retry, and cost optimization strategies. The most notable is the proliferation of token wholesale functionality: users can obtain inference compute at prices far below standard rates through prepayment or committed usage agreements, effectively gaining access to the 'cheapest token'. For example, a well-known AI hub platform launched a new token proxy module in early October, allowing enterprises to route requests to models from different vendors and automatically select the cheapest inference path based on real-time pricing. Meanwhile, some third-party AI channel agents have also begun integrating such services, helping small teams enjoy wholesale prices without building their own scheduling systems. The maturation of this ecosystem marks the evolution of AI infrastructure from simple API interfaces to intelligent cost optimization platforms.
How enterprises use the 'cheapest token' to reduce costs
For AI application developers, these changes in October directly reshaped cost structures. In the traditional model, enterprises had to interface with model vendors one by one and pay per token, with high price volatility and little bargaining power. Now, by accessing AI API relay stations and enabling token wholesale plans, enterprises can dynamically allocate inference resources based on business peaks and troughs. One case shows that an e-commerce company using token proxy services reduced its monthly inference spending for customer service chatbot models by over 40%, while also improving user experience due to latency optimization from intelligent routing. Additionally, some relay stations have introduced a 'model combination' feature, allowing developers to use low-cost small models for simple tasks and large models for complex tasks, with unified settlement via the token proxy, further lowering average costs. It is foreseeable that as the token wholesale model becomes widespread, the barrier to deploying AI applications will continue to drop, and more small and medium-sized enterprises are expected to achieve full-scenario AI adoption by 2027.