Tokens, words and pages

A model does not read letters or words. It reads tokens: short pieces that its tokenizer cuts text into. A common word is often one token. A rare word, a long number or a name is cut into several.

Spaces, punctuation and line breaks are tokens too. So is every bracket and quote mark in a block of structured data.

Each model family has its own tokenizer. Give two models the same page and they report two different token counts. Neither is wrong. They cut the text differently.

This matters for tokens per dollar. If one model needs more tokens for your text, its tokens buy less text, and a higher tokens-per-dollar figure can hide a higher cost per page.

Plain English prose is the most compact. Code, tables and structured formats use more tokens for the same content, because of their symbols. Languages other than English often use more tokens for each word.

Images and video, on models that accept them, are converted into tokens as well and billed as input.

Measure your own text. Send a few typical documents to the model and read the input token count that comes back. Divide by the pages. That is tokens per page for your material on that model.

Then divide tokens per dollar by tokens per page. The result, pages per dollar, compares fairly across models with different tokenizers.

Runcrate serves 124 models through one OpenAI-compatible API, paid from one prepaid balance. You can send the same sample to several models from one key and read each one’s token count for your own material. Runcrate lists DeepSeek V4.1 Flash 1017 at $0.103 per million input tokens.

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