What a Token Actually Costs

A token is not a fixed unit of text. It is the output of a tokenizer. The same content can produce meaningfully different token counts depending on language, model version, and even quiet tokenizer updates. Non-English text routinely consumes more tokens. Output tokens are priced much higher than input tokens. Reasoning or “thinking” tokens are usually billed as output as well.

These mechanics create systematic opacity. Listed per-million-token prices are unreliable proxies for real cost. Cheaper models can end up more expensive overall because they consume more tokens to reach the same result. Subscription plans hide limits through silent model downgrades and non-comparable quotas. The practical consequence is that teams cannot budget accurately from published rate cards alone.

The only reliable approach is to measure the actual token behavior of your own workloads across models, languages, and task types. Pricing is both a technical unit and a strategic surface that providers can adjust without changing the headline numbers.

This Ledger Entry expands how the reader thinks by replacing a simplistic view of token pricing with a clearer picture of a unit that is both technical and strategically malleable.