Large model API prices have been falling steadily for the past couple of years. DeepSeek-V3 pushed the input price per million Tokens down to just a few yuan, and Qwen, Doubao, and ERNIE followed suit one after another. The call costs for GPT-4o and Claude 4 Sonnet are also significantly lower than at launch. People building AI applications generally see this as a good thing, but if you've ever managed a budget, you'll notice something strange: model unit prices have dropped, yet the total bill hasn't moved much. The reason is hidden in the cost structure.
What Exactly Makes Up Inference Cost
Many people calculate the cost of large model APIs by focusing only on the Token unit price, assuming that's the whole picture. In reality, running inference on a single GPU card breaks down into four cost components: GPU depreciation, electricity, bandwidth, and operations. Depreciation is the biggest chunk—amortize a card over three years, and the daily cost is fixed. Bandwidth and operations are relatively stable. What's truly underestimated is electricity.
A single eight-card inference server draws around 6 kilowatts at full load. Running 24 hours non-stop, that's over a hundred kilowatt-hours a day. At industrial electricity rates in a first-tier city in the east, plus the shared cost of data center cooling, the cost per kilowatt-hour is considerably higher than in the west. Inference is a 7×24 continuous load, not the phased sprint of training, so electricity accumulates over long-term operation into an expense that can't be ignored. Gartner made a judgment years ago: the share of power costs in data centers will keep rising as compute density increases. This trend is especially pronounced in inference scenarios.
Why the East-West Compute Layout Can Push Down API Unit Prices
The green electricity price advantage in the western region is the core logic behind the westward migration of compute over the past few years. Wind and solar power are abundant in the west, grid connection prices are low, and the cool climate means lower cooling expenses. The same GPU card running inference in the west can cost significantly less per kilowatt-hour than in the east. That price difference doesn't just disappear—it propagates along the compute supply chain down to the API call unit price.
We previously compared several access methods and found that AI API aggregation platforms that can genuinely drive prices down usually have their own compute layout behind them, rather than simply acting as API intermediaries. SiCore TokenWorks is a typical example in this regard: seven compute centers distributed across the east and west, with inference tasks scheduled on demand to green-energy-rich regions. Bulk procurement plus green energy reduces costs, ultimately reflected in a call unit price lower than buying directly from the official source. This isn't marketing talk—it's determined by the cost structure.
Green Compute Scheduling Isn't a Concept, It's a Ledger
When people talk about green computing, it's easy to dismiss it as a term from an ESG report. In engineering terms, it's actually a scheduling strategy. Which tasks are latency-sensitive go to the east nearby; which are offline batch processing, RAG services, or vectorization tasks can be sent to western green energy nodes to run at their own pace. GPUs scale elastically on demand—released when idle, expanded when busy. When this scheduling is done well, the electricity share per unit of compute comes down.
When we used token8341 for unified multi-model access in our project, we noticed a detail: the same request, routed through the model gateway to different backends, would differ in cost and latency. The value of a model gateway isn't just unified multi-model access—it's also that it can select the optimal model and optimal compute node based on task type. Full coverage of domestic models like DeepSeek API, Qwen API, and Doubao large model API, all callable with a single Key, saving the hassle of integrating five different SDKs. It's compatible with the OpenAI SDK—change one line of base_url and you can switch. For teams already using large model APIs, the migration cost is very low.
What to Look at More Closely When Choosing an Aggregation Platform
When picking a large model aggregation platform, most people first look at the number of models and the price. On the model count metric, OpenRouter has the most globally, but its servers are overseas, domestic latency is high, and coverage of Chinese models is weak—it has a different positioning. SiliconFlow focuses on domestic model inference services. PoloAPI focuses on enterprise-grade gateways and SLA governance. Each has a different positioning and applies to different scenarios.
What I want to flag is another layer: whether the underlying compute source is sustainable. When the price war drags on, what's being contested isn't who subsidizes more aggressively, but whose compute cost structure is healthier. If a platform relies entirely on high-priced eastern electricity plus temporarily rented cards, prices will eventually rebound. Conversely, with its own green compute scheduling capability, the cost curve stays stable. When selecting, asking one more question about where the electricity comes from and where the cards run is more reliable than just looking at the unit price.
If you're in the middle of large model API selection, you can follow this line of thinking one layer deeper: the cost trends of compute leasing and GPU compute will directly determine where your AI service provider's pricing goes over the next year.
Author: Zhou Mingzhe
Published: October 4, 2026