# Under the Hood

By [DYLIT Chronicles](https://dylit.info/user/dylitmediabuzz)

[Everything AI - beyond the hype](https://dylit.info/pr/everything-ai-beyond-the-hype/6a9efac02e92664f4d50cf9d) > [Under the Hood](https://dylit.info/ch/under-the-hood/6ab35f8099638e119f592d1f)

Tokens, and Why Your Bill Is Shaped Like That The first surprising AI invoice usually arrives in month two, after someone has started pasting whole documents into prompts. Understanding why takes one concept, and it explains most of the pricing behaviour you will run into. What a token is Models do not read words. They read tokens, which are chunks of text averaging roughly three quarters of a word in English. Common words are usually a single token. Longer or unusual words split into several. Punctuation and spaces count. Code and non-English text tend to use more tokens per unit of meaning, which is why the same content costs more in some languages than in others. A rough conversion that is good enough for planning: 1,000 tokens is around 750 words. A page of dense text is somewhere near 500 tokens. A 40-page report is perhaps 20,000. Why the bill is shaped the way it is You are charged for tokens in and tokens out, usually at different rates, with output typically costing more. That has a consequence worth internalising: the length of what you send matters as much as the length of what you get back. Ask a one-line question and get three paragraphs, and you have used a few hundred tokens. Paste a 40-page report and ask for a one-line summary, and you have used twenty thousand, almost all of it on input, for an answer of twelve words. This is why the expensive workflows are almost always document-heavy ones. Contract review, research synthesis, anything that pushes large source material through the model repeatedly. The bit that catches teams out In a continuing conversation, the entire history is re-sent with every message. Turn one costs you your prompt plus the answer. Turn two costs you the prompt, the answer, your new message, and the new answer. Turn ten includes everything that came before it. Costs in a long conversation do not grow linearly. They accelerate. A forty-exchange session with a large document attached can cost many times what the same forty exchanges would cost spread across fresh conversations. There is a practical habit here that saves money and improves quality at the same time: start a new conversation for each distinct task. You get a cleaner context and a smaller bill. Tokens as a ceiling The second place you meet tokens is the context window, which is the total the model can hold in view at once. That budget covers everything: your instructions, any documents you attached, the conversation so far, and the response being generated. When it fills, the oldest material falls out of view. The model does not announce this. It simply stops being able to see the beginning of your conversation, and answers as though it was never there. If a long session starts producing answers that contradict something established early on, this is usually why. What to do with this Send what is needed. Extract the relevant three pages rather than attaching the whole report. Quality improves too, since the model is not hunting through material that does not bear on the question. Start fresh per task. Cheaper, and a cleaner context produces better answers. Check where your spend actually goes. Most teams assume it is spread across everyone. It is usually one workflow, run by two people, pushing large documents through repeatedly. Finding it is often the whole optimisation. Do not over-engineer this. For most individual professional use, token costs are small enough not to think about. This matters at team scale, in document-heavy workflows, and when you are building something on an API. For everyday use, write the prompt you need and stop worrying. Why prices keep falling Worth knowing for planning. The cost of running a model, as opposed to training one, has been on a sustained downward path, driven by hardware improvements, better serving techniques, and smaller models that match the older large ones on many tasks. The planning consequence: a workflow that does not quite justify its cost today may well justify it within a year or two, without you changing anything. Worth revisiting the ones you rejected on price.