A hamburger from a drive-through, handmade pasta from a neighborhood restaurant, and a twelve-course tasting menu. All of these are “meals,” but they are not interchangeable. They require different chefs, ingredients, preparation times, prices, and reservations.
AI tokens work the same way.
A token is a small unit of information an AI model reads or produces — roughly a word or part of a word. Companies often discuss tokens as though they were identical units. They are not. The same number of tokens can deliver very different cost, speed, quality, and reliability depending on the model and provider behind them.
Model types resemble different restaurants. A smaller model is the quick-service kitchen: fast and inexpensive for routine work. A large reasoning model is the tasting-menu kitchen: more capable on difficult problems, but often slower and more expensive. Some models specialize in text, coding, images, or audio.
Providers matter too. Frontier companies such as OpenAI, Anthropic, and Google operate the restaurant for you. Open-weight models are more like receiving the recipe and running the kitchen yourself: you gain control but must provide the equipment, staff, security, and maintenance.
Then comes the lunch rush. If too many orders arrive at once, latency — the wait for an answer — can increase. Providers use rate limits, much like a restaurant holding only so many reservations at a sitting, to prevent one customer from taking every burner. OpenAI and Anthropic both document these capacity controls.
These differences hit both the budget and the customer. Choose an oversized model for every simple task and costs soar. Choose a cheap or constrained service for critical work and customers may receive slow or inadequate answers. Worse, a company can build workflows around inference it later cannot afford.
That’s where Inference Exchange can help. IX is designed to help buyers and sellers manage token price, performance, and availability together. Like companies using energy futures to reduce exposure to changing fuel or electricity costs, AI buyers can hedge future token spending and capacity needs while sellers gain more predictable demand.