Cost per token is the price you pay for every token a model processes, where a token is a small chunk of text roughly equal to a few characters. Most providers charge separately for input tokens, the text you send, and output tokens, the text the model generates.

This matters because token costs are the main driver of running expenses for language model applications. Long prompts, large retrieved context, and verbose outputs all add up quickly across thousands of users, and output tokens usually cost more than input. Understanding the unit price is essential to forecasting spend and choosing a model.

At arosplatforms we model cost per token against expected volume before launch, then reduce it with shorter prompts, caching, smaller models where accuracy allows, and tighter retrieval. This keeps unit economics healthy and ties directly to a client's AI ROI.