Electricity has peak and off-peak rates. Airline tickets use dynamic pricing. Hotels adjust prices according to occupancy. Now one of the most important consumables of the AI era—tokens—is also beginning to be priced according to supply and demand.
On August 17, DeepSeek introduced a time-of-use pricing mechanism for its API. Every day from 9:00 a.m. to noon and 2:00 p.m. to 6:00 p.m. Beijing time is classified as peak hours. At all other times, API prices are half the peak-hour rates.
For DeepSeek V4 Pro, cache-miss input costs RMB 9 per million tokens during peak hours and RMB 4.5 off-peak. Output tokens fall from RMB 27 to RMB 13.5. V4 Flash follows the same 2:1 ratio. DeepSeek now lists prices across three dimensions: model, token type, and time period.
Although this can look like a price increase, the more important change is that tokens are beginning to be priced like electricity, based on the supply and demand of underlying resources.
Tokens Are Becoming the Electricity of the AI Era
Every kilowatt-hour may look identical, but the grid faces different costs at 3:00 a.m. and during the afternoon peak. The system must prepare generation, transmission, and reserve capacity for maximum demand.
Peak and off-peak pricing uses price to shift delay-tolerant demand into less busy hours. AI compute faces almost the same problem. Purchasing one million tokens actually consumes GPU compute, memory, networks, electricity, and model-serving capacity.
When many developers and enterprises call models simultaneously, concurrency and compute pressure rise. When GPUs operate at low load overnight, the utilization economics are completely different. DeepSeek’s mechanism is an early example of this relationship being reflected directly in token prices.
Every resource network that reaches scale eventually enters a stage of refined scheduling. When a resource is used, where it is used, and what service level it requires ultimately affect price. The token market is moving in the same direction.
Pricing by Time Is Only the First Step
DeepSeek addresses the time dimension of the token economy. The next stage must address token efficiency: how much intelligence does each task actually deserve?
Many applications still send every task to one selected model. Writing a sentence, solving advanced mathematics, translating text, and analyzing hundreds of pages of financial documents all use the same one-stop model.
That is like requiring an entire city to use the highest-specification energy solution for boiling water, lighting rooms, and operating supercomputers. It is technically possible, but economically irrational.
Classification, translation, speech recognition, and structured extraction may not need the largest model. Complex coding, deep reasoning, multimodal analysis, and high-value agent tasks need stronger capabilities.
The real question is not simply when the price is lowest. It is: while maintaining quality, which model and compute resource should each task use, and at what cost should it be completed?
From Peak and Off-Peak Pricing to a Smart Grid
If time-of-use pricing is the equivalent of peak and off-peak electricity rates for tokens, GoodVision AI’s Smart Routing Engine is closer to a smart grid for AI inference.
Its principle is The Right Model for the Right Task. Translation and basic information processing can prioritize lighter, lower-cost models. Complex reasoning, coding, image generation, and video retain access to capable large or multimodal models and private deployments.
Enterprises will no longer simply purchase one million tokens from one model. They will purchase a solution that helps complete one million AI tasks at the most appropriate cost. Models move into the background while routing evaluates complexity, price, latency, privacy, and resource availability.
In the agent era, this extends to Agent-to-Agent calling. Multiple agents call models, tools, and intelligent services together. If each agent defaults to the most expensive model, costs compound quickly. A scheduling system can assign lightweight models to simple stages and reserve high-capability models for genuinely difficult work.
This is the token efficiency layer the agent economy needs.
Will Tokens Become an Intelligent Commodity?
Token prices already vary by model, input or output type, cache status, and time period. Future variables may include geography, latency, compute source, privacy level, and task complexity.
The market will increasingly resemble electricity. Underlying compute resources sit in different locations with different costs. Above them is continuously changing demand from enterprises and agents. Between the two, a real-time scheduling system must balance price, performance, latency, and reliability.
The electricity industry took more than a century to evolve from individual power plants into complex grids, time-of-use pricing, and trading markets. AI may compress that process into a few years.
A token is no longer merely a fixed number on a provider’s pricing table. It is becoming a factor of production that can be priced, traded, and intelligently scheduled.
Intelligent scheduling networks built around token flows will become essential infrastructure connecting models, compute, and applications—and a new focus of competition in the AI era.