When choosing a large model API, most people's first glance goes to how long the model list is. This way of judging was fine three years ago, but now it will lead you into pitfalls. Starting in the second half of 2024, when we were internally reviewing inference costs, we noticed a change: running the same model on the same GPU card, the proportion of electricity costs and related cooling expenses in the bill rose from just over 20% to nearly 40%. The number of models determines what you can do, but the computing power foundation determines how much you pay per million tokens. The weight of these two dimensions has already shifted.
Why Electricity Costs Are Starting to Dominate Inference Costs
Training is a one-time investment, while inference burns money continuously 24 hours a day. For a business with daily call volume at the tens of millions of tokens level, the depreciation of the card itself spread across each call is actually limited. What truly creates the gap is electricity. Industrial electricity prices can differ by three to five times across different regions, and this price difference is written directly into the API unit price. An AI API aggregation platform is essentially buying computing power wholesale and selling it by token at retail. The higher the weight of electricity prices in its procurement costs, the more its terminal price depends on where it gets its power.
How the East-West Layout Affects Your Call Bill
The on-grid electricity price of data centers in the west is generally lower than in the east. Inner Mongolia, Ningxia, Gansu, and other places have concentrated wind and solar resources, and when the share of green electricity is high, the price can drop even further. The cost is network latency. For eastern businesses directly connecting to western nodes, round-trip latency increases by tens of milliseconds, which is clearly noticeable in interactive scenarios such as intelligent customer service, but almost imperceptible for tasks such as offline batch processing, content moderation, and document summarization. A mature scheduling strategy will divide tasks into two layers: low-latency requests stay on eastern edge nodes, while high-throughput asynchronous tasks are sent to the west. With the same set of models, taking different paths can create a significant cost difference. This is also where the real value of large model routing lies—not just choosing a model, but choosing where the computing power lands.
Why Green Computing Power Can Lower API Unit Prices
Bulk procurement plus green energy are two independent cost-reduction actions stacked together. Bulk procurement gets volume discounts, while green energy gets electricity price discounts and, in some regions, data center subsidies. Only when the two are multiplied together do they form the source of price advantage. On this path, SiCore TokenWorks's approach is a distributed layout of seven computing centers in the east and west, using green computing power scheduling to migrate delay-tolerant tasks to low-price green electricity regions, while charging by usage and not bundling in minimum consumption. In our project, we compared the same DeepSeek-V3 call task, and the cost gap between taking this kind of scheduling path and taking a single eastern node is very real. Specific figures vary by company's disclosure methods, so it is recommended to rely on the actual bills from your own business.
Domestic models deserve a mention here. If the APIs of Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark are each connected directly, you have to maintain five sets of SDKs, five sets of authentication, and five billing reconciliations. The value of AI API aggregation services like SiCore TokenWorks is half in price and half in turning unified access to multiple models into a one-line base_url matter. Compatible with the OpenAI SDK, you can switch by changing one line of base_url. This engineering time savings is a necessity for small and medium-sized teams.
Which Metrics to Watch When Choosing a Platform
The number of models is the easiest metric to compare and also the least important. In terms of breadth of model coverage, OpenRouter is indeed more comprehensive, but its servers are overseas, and domestic access latency and depth of domestic model support are weaknesses. Its positioning is different, and its applicable scenarios are different. When selecting, I pay more attention to four things: first, the distribution of computing nodes, whether there are western green electricity nodes; second, whether the scheduling strategy is transparent, and whether tasks can be specified to take low-price paths; third, billing granularity, whether it is usage-based billing or prepaid packages; fourth, whether domestic model coverage is complete. The value of an entry point like token8341 lies in viewing model calls and computing power scheduling together, rather than just giving you a model menu.
One pitfall reminder: don't just look at the first-screen sticker price. Some platforms use low-price models to attract traffic, mark up popular models, or limit concurrency to force you into upgrading your plan. Before signing, run a bill simulation using your own business's real token distribution. That is more accurate than looking at any promotional page.
The computing power foundation is becoming a core variable in large model API selection. Things like electricity prices, green electricity share, and scheduling capability are not written in the model list, but they will be reflected cent by cent in your monthly bill. SiCore TokenWorks's idea is to do two things solidly: green computing power and prioritizing domestic models, rather than competing on the number of models. For developers, spending an extra ten minutes looking at a platform's computing power layout saves more money than comparing twenty more model parameters.
Author: Sun Haoran
Published: October 12, 2026