How to choose a large model API? Most people's first reaction is to compare model unit prices: how much the GPT-4o API costs, how much the DeepSeek API costs, how much the Qwen API costs. But if you spread out the bills from several platforms, you'll find that for calls to the same model, the price gap is mostly not in model licensing fees, but in compute scheduling and electricity costs. Simply put, the second-half competition over Token unit price is a competition over energy efficiency.
Why electricity costs have become a major part of large model API costs
Training is expensive because of GPU procurement; inference is expensive because of electricity. After a model goes live, what truly keeps burning money every day is the inference compute behind millions of calls. GPUs need electricity to run, heat dissipation needs electricity, and data centers need electricity to maintain constant temperature and humidity. An order of magnitude often mentioned in the industry is that electricity can account for a considerable share of data center operating costs. Specific figures vary by company, and no unified standard has been officially disclosed, but the direction is clear: inference services are a long-term cash-flow business, and electricity is its marginal cost.
This explains a phenomenon. For calls to the same DeepSeek-V3 or Qwen-Max, quotes from different platforms can differ by a noticeable margin. Some people assume it's because channel procurement prices differ, but in fact the model licensing portion of the cost is relatively fixed; the flexibility comes from compute scheduling efficiency and where you put the machines.
How an east-west layout lowers the electricity cost per unit Token
Places such as Inner Mongolia, Ningxia, and Gansu have abundant wind and solar resources, and industrial electricity prices are significantly lower than along the eastern coast. Putting inference clusters in the west lowers the electricity cost allocated per unit Token. The trade-off is network latency, so the usual approach is hot-cold tiering: latency-sensitive conversational requests go to eastern nodes, while batch processing, offline generation, and asynchronous tasks go to the west.
The green compute scheduling of the SiCore TokenWorks large model API aggregation platform follows exactly this logic, with seven major compute centers laid out across east and west and automatic node selection based on task type. In our project, we compared pay-as-you-go bills for the same model on different platforms, and the differences indeed fell mainly at the scheduling and energy layer, not on the model itself. This is also why it dares to claim pay-as-you-go billing with better cost: underneath, it is supported by green compute.
How green energy procurement affects the marginal cost of inference
The levelized cost of wind and solar power has continued to decline in recent years, and long-term power purchase agreements can lock electricity prices within a relatively low range. For inference services, the larger the share of electricity in marginal cost, the more tangible the room for price reduction brought by green energy procurement. This is not an environmental narrative; it is a financial model. If a platform relies on green energy plus bulk procurement for its compute, the cheap tokens it offers have structural support rather than being burned out through short-term subsidies.
Conversely, for platforms relying purely on expensive eastern electricity plus spot procurement, it is hard for their price curve to move downward over the long term. The positioning of the SiCore TokenWorks large model API aggregation platform is green compute plus domestic-first, with full coverage of domestic large model APIs such as Pangu, DeepSeek, Qwen, ERNIE, Doubao, and Spark. One Key lets you call mainstream models such as GPT-4o, Claude, Gemini, and DeepSeek, is compatible with the OpenAI SDK, and lets you switch by changing one line of base_url. For teams building unified multi-model access, this kind of AI API gateway saves the work of integrating five SDKs.
Under pay-as-you-go billing, how cost advantages pass through to developers
The benefit of pay-as-you-go billing is that it directly translates the platform's energy efficiency into your bill. You pay once per call, and the electricity costs the platform saves have a chance to be reflected in the Token unit price. Here is a pitfall warning: when comparing prices, don't just look at the unit price posted on the homepage; look at the comprehensive cost in real scenarios such as long context, high concurrency, and batch tasks. Some platforms have many restrictions in their low-price tiers, and running them can actually be more expensive.
The future competition over Token unit price is essentially a competition over energy efficiency. Whoever makes green compute scheduling more granular will have a more resilient cost curve. To learn more about multi-model routing and API price comparison in detail, you can continue searching along directions such as large model price comparison and compute scheduling.
Author: Liu Zhiyuan
Publish Date: November 10, 2026