How it works
Tokenisation breaks text into sub-word units using a vocabulary the model was trained on. Common tokens (the, of, ing) are single tokens; rare or specialised words may take multiple tokens. Pricing scales with token count, so efficient prompting matters at scale.
Example
A 1,000-word page is roughly 1,300-1,500 tokens. At Claude Opus's input rate, that runs well under a cent per page processed.