this post was submitted on 24 May 2026
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I'm unfamiliar with AI chatbots that you pay for. What is a token?
A token is basically just a word. Know how your phone’s auto suggest tries to anticipate the words you want to use as you type? In this case, your phone is using an extremely small token amount (typically only the previous two or three words you have typed) to try and predict your next word, which would also be a token. Your phone only uses a few tokens at a time, because as token count rises, processing requirements also quickly balloon.
And AI chat is basically the same concept, but with a massively inflated token limit. Instead of looking at your previous two or three words, it looks at entire conversations. And it also uses tokens to generate responses, the same way your phone is using one token at a time to predict your next word.
So when you pay for tokens, you’re essentially paying for a word count. As you continue a conversation, the token requirement for each subsequent request will increase, because it is attempting to look at the entire context of the conversation you have had.
Models have built-in token limits, to put a cap on how much memory is required to run the model. As conversations stretch on and you reach the model’s token limits, it will begin losing context for things that happened earlier. It will try to summarize earlier parts of the conversation to shorten them but keep relevant pieces in memory, or it will just outright drop old parts of the conversation and “forget” that context, the same way my phone has already forgotten the start of this sentence.
It’s a little more complicated that “each word is a token”, because the chatbot will combine your prompts with its own internal systems. Especially as conversations stretch on, and it begins to summarize old parts to keep them in memory. But that’s the most straightforward way to explain it.
My understanding is that tokens are basically words, and that when you ask a question it charges for all the tokens it consumes, produces, or processes. There's a lot of internal processing for each request, where the input text is summarized in different ways and combined with previous parts of the conversation, so it's not as straightforward as "word count of what you say plus what it says".
Worth noting that a token is not necessarily a word, though can be. One word could also take multiple tokens. It can also vary from LLM to LLM and their tokenization methods.
In other words obfuscation so they can charge whatever they want using some obscure formula that only they know.
Not really, there are ways to count tokens before running an inference. Some providers make tokenizers public, so they even work offline. APIs also usually return rolling costs per response and have budget limits - though some could have more fine-grained limits.
Users who are surprised by the bill are usually not paying attention to each call, or using autonomous subagents, or a setup where they have little or no control to what is sent to the provider.
So the problem isn't really the API provider, as much as it's the tooling around it, which makes it too easy to overspend.
In very simple terms, a token is more or less a word. You pay per input and output tokens (your prompts and the answers) as they correlate the most closely to the energy expended by the LLM to process your request.