A token is the thing you are actually paying for, and almost nobody thinks in tokens. They think in words, or messages, or vibes. Then the invoice arrives and the math does not line up. Here is the plain version of what a token is, how your text gets chopped into them, and why the model bills this way instead of the way you would expect.
Not a word, not a letter, something in between
When you send text to a model, the first thing that happens is that a piece of software called a tokenizer breaks your text into pieces. A token is one of those pieces. Sometimes a token is a whole word. Often it is a fragment. The word "sleeping" might become "sleep" plus "ing". Common words like "the" are usually a single token, while a rare or long word gets split into two or three.
The rough rule people quote is that one token is about four characters of English, or roughly three quarters of a word. OpenAI's own guidance says about 750 words works out to around 1000 tokens. That heuristic is fine for a quick estimate and wrong the moment your text stops looking like a plain paragraph. Code, punctuation, JSON, and tables all push the count up, because symbols and indentation do not compress into neat word-sized chunks. So does anything not in English. The tokenizers were trained mostly on English, so "strawberry" costs one or two tokens while the same word in Chinese or Arabic can cost several times more. Same meaning, bigger bill.
Why chop words up at all? Because the model has a fixed vocabulary, usually somewhere between 50,000 and 100,000 tokens. If every distinct word needed its own entry, the vocabulary would explode and the model would choke on any word it had never seen. Subword tokens are the compromise. The model learns a set of common fragments and reassembles anything, including words that did not exist when it was trained, out of pieces it already knows.
Why the meter runs on tokens
Providers charge per token because tokens are what the model literally processes, one step at a time. Every token in your prompt has to be read before the model writes anything, and every token it writes is another unit of compute. There is no natural billing unit called a "word" inside the machine. There are only tokens going in and tokens coming out.
That is also why input and output are usually priced differently, with output costing more. Generating a token is more expensive than reading one, because the model does a full forward pass for each new token it produces. When you see two prices on a pricing page, that is what the gap is about.
Where the bill bites
The practical consequences follow directly, and they are easy to miss until they cost you money.
- Long context is not free context. If you paste a 40-page document into every request, you pay for all of it on every request, whether or not the model needed most of it to answer. A chat that remembers the whole conversation is re-reading and re-charging that history each turn.
- Verbose system prompts are a recurring tax. That 800-token instruction block you wrote once gets billed on every single call.
- "Make it concise" saves output tokens, which are the expensive ones. Asking for a one-line answer instead of an essay is a real cost lever, not just a style choice.
None of this means you should obsess over trimming tokens like a coupon-clipper. Most single requests cost a fraction of a cent. It matters at volume. A prompt that is 30 percent heavier than it needs to be does not hurt when you run it twice. Run it two million times and it is a line item someone will ask you about.
Count before you commit
The one habit worth building: stop estimating in words when real money is involved. Run your actual prompts, the messy ones with the code blocks and the pasted logs and the non-English names, through a token counter before you ship. Most providers give you one, and the number is almost always higher than your gut said, because your gut counts words and the model counts something stranger.
The word is a unit humans invented for humans. The token is a unit the machine invented for itself. Your bill is written in the machine's units, and once you can see the text the way the tokenizer sees it, the invoice stops being a surprise and starts being something you can actually plan around.