Token Economics

Large language model (LLM) pricing runs on tokens, the chunks of text models process as input and output, with output tokens typically costing 3 to 5 times more than input tokens and costs accumulating fast inside agent loops that call the model repeatedly. An unoptimized agent loop can burn through hundreds of dollars per day in production, so understanding tokenization, context window costs, and price differences between models is a prerequisite for building economically viable systems at scale. Prompt caching, model routing to cheaper models for simpler tasks, and context window discipline can reduce costs by 10x or more without sacrificing output quality.