How it works
Before processing a message, a language model transforms the text into a sequence of tokens. This step is called tokenization and uses a vocabulary and rules specific to the model. Because of that, a token does not necessarily equal a word: “computador” can be a token in one situation and be split into parts in another.
The model receives the tokens as numeric identifiers, calculates relationships between them and produces a response also in tokens. A tokenizer is the component that does this splitting. Different models can use different tokenizers, so the same text can generate different counts.
Spaces, punctuation, accents, language and formatting influence the split. Code can also be tokenized differently from a common sentence, because variable names, symbols and indentation are part of the input. Each model's documentation explains how to count tokens more accurately.
Tokens also appear in technical limits. A context window defines how many input and output tokens the model can consider in a call. If a conversation, document or code snippet exceeds that limit, it may be necessary to summarize, split or select the relevant content.
Practical example
Imagine someone asks an assistant to explain a JavaScript function. The input can include the question, the code and additional instructions. The system transforms everything into tokens before sending the content to the model. The model then generates the explanation token by token, until it completes the response or reaches the configured limit.
This helps understand why counting words in the editor is not enough to estimate the size of the call. A short sentence with technical names, special characters or code can consume a different amount of tokens than another sentence with the same number of words. To plan an integration, use the tokenizer indicated by the model provider, instead of converting words by a fixed rule.
In an application, the team can measure token usage at each step: system instructions, conversation history, retrieved documents and generated response. This separation makes it easier to identify which part is taking up more context. If a history grows too much, the application can store a summary and send only the messages needed for the current task.
It is also useful to compare input and output counts. Input includes everything the model receives; output corresponds to what it generates. This distinction matters for controlling API limits, monitoring consumption and investigating interrupted responses. Exact details vary by model and service, so they should be consulted in the documentation used by the project.
What it's for
Understanding tokens helps estimate prompt size, organize documents, avoid exceeding the context window and interpret model usage metrics. For programmers, it is an operational unit: it allows measuring the content that passes through the API and detecting excessively long calls.
Tokens also help compare context strategies. A team can test whether a smaller summary keeps the important information, or whether it is worth sending selected snippets instead of an entire file. The goal is not to reduce tokens at any cost, but to provide the model with the necessary context without including irrelevant material.
When analyzing results, remember that token count does not measure quality, human effort or number of ideas. It measures representation units used by the model. An official tokenizer or a provider tool is the appropriate reference for an accurate estimate.