Token wholesale: New engine for cost reduction
In October, several leading AI platforms announced Token wholesale plans, allowing enterprises to purchase API call quotas in bulk via prepayment, with unit prices 30% to 50% lower than pay-as-you-go pricing. This model directly addresses enterprises' urgent demand for the "cheapest token" — previously, high per-inference costs were a core bottleneck hindering large model deployment. For example, a well-known domestic platform now offers Token wholesale options in standard, advanced, and enterprise-customized tiers, with the lowest tier monthly cost already under 1,000 yuan, equating to just a few cents per inference request. Industry analysts point out that Token wholesale essentially front-loads computing costs, and with scheduling optimization, marginal costs approach hardware limits.
Meanwhile, a group of third-party service providers focused on API cost optimization have emerged. They set up "AI 中转站" or "token transfer stations" to centrally purchase Token quotas from leading platforms and then resell them in smaller portions to small and medium developers. This model further reduces access costs for long-tail users, with some "AI API transfer stations" pricing at only 60% of official rates. Although some platforms remain cautious about third-party resale, the market-driven distribution network is already significant, pushing the entire ecosystem toward more flexible pricing structures.
Channel agency model reshapes the ecosystem
Alongside the scaling of Token wholesale, the channel agency model became a buzzword in the AI industry this October. Multiple platforms opened "AI channel agent" or "AI large model agent" interfaces, allowing partners to purchase API resources at deeper discounts and flexibly combine multiple model services. For instance, a cloud computing vendor teamed up with vertical solution providers to launch the "AI hub" program, where agents can access dozens of open-source and closed-source large models under one roof, with unified billing and maintenance to reduce enterprise selection costs. Under this model, "token agents" offer not only price advantages but also value-added services such as model evaluation, performance monitoring, and failover switching, making AI capabilities as plug-and-play as electricity or water.
Notably, the rise of channel agents also brings regulatory challenges. Some local data bureaus now require agent service providers to register operational qualifications and explicitly prohibit unauthorized resale. However, overall, policy tends to encourage "compliant agents" to accelerate AI democratization. Industry estimates suggest that by the end of 2026, API calls completed through channel agents will account for over 40% of the entire industry, becoming the mainstream path for enterprises to access AI.
Policy support: Efficient allocation of computing resources
In mid-October, the National Development and Reform Commission, together with the Ministry of Industry and Information Technology, issued the "Guiding Opinions on Further Optimizing the Supply of AI Computing Resources (Draft for Comments)", explicitly proposing pilot "computing vouchers" for large model training and inference, offering subsidies of up to 30% for enterprises using public computing platforms. This policy directly benefits Token wholesale and channel agent models, as subsidies can cover part of the procurement costs, further lowering the actual price paid by enterprises. At the same time, the document urges local governments to strengthen coordinated planning of computing centers, avoid disorderly expansion, and promote cross-regional sharing of existing computing resources through "computing scheduling platforms".
At the local level, cities such as Shanghai, Shenzhen, and Hangzhou have rolled out complementary measures. For example, Shenzhen supports building an "AI large model cost optimization demonstration base" in the Hetao Cooperation Zone, encouraging enterprises to use idle nighttime computing capacity for batch Token production; Hangzhou is piloting a "Token wholesale plus data sandbox" integration scheme, allowing sensitive industries like finance and healthcare to use large model capabilities at low cost in compliant environments. These policies, combined with commercial innovations, significantly reduce the cost of trial and error from prototype validation to large-scale deployment.
Future outlook: From cost games to value deep dive
As Token wholesale and channel agent models become widespread, the AI industry is moving beyond the "parameter-only" phase and entering a new cycle centered on cost efficiency. In the fourth quarter of 2026, more platforms are expected to introduce long-term contracts based on usage commitments, as well as scenario-specific Token packages (e.g., for customer service, code generation, document processing). Meanwhile, there is still room for reducing the inference cost of long contexts in multimodal models; some labs have demonstrated that sparse attention and quantization compression techniques can compress Token consumption by another 40%.
For developers, choosing the "cheapest token" is no longer a simple price comparison but requires comprehensive consideration of model quality, latency, availability, and agent service stability. In the coming six months, we can expect third-party evaluation platforms, price comparison sites, and insurance mechanisms centered on Token wholesale to emerge one after another, forming a more mature market system. When the marginal cost of AI approaches zero, real value will be created by those who skillfully apply AI to solve specific problems and build products and business models.