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A token is a chunk of text that a language model processes. It might be a whole word, part of a word, a character, or punctuation—so tokens are not the same thing as words. Token counts matter because they help determine how much a model can handle in one request and, for API users, how usage is measured and billed.
What is a token?
OpenAI defines tokens as “the units that OpenAI models use to process text.” The process of breaking text into those units is called tokenization. A tokenizer might divide “tokenization” into “token” and “ization,” for example. The precise split depends on the model and its encoding; a token is not a universal unit of meaning or a fixed-size piece of text. OpenAI’s token guide explains the concept and provides examples.
Token boundaries can be affected by spelling, capitalization, spaces, punctuation, language, and the model’s tokenizer. As a result, the same passage can have different token counts in different models. Even small text changes can affect how it is divided.
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How many tokens are in a word?
There is no exact word-to-token conversion. For a rough English estimate, OpenAI suggests that one token is about four characters or three-quarters of a word. Google’s Gemini guidance estimates that 100 tokens correspond to about 60–80 English words. These are approximate rules of thumb, not promises for a particular passage or model.
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- Short, common words may each be a single token.
- Long or less common words may be split into multiple tokens.
- Spaces and punctuation also contribute to tokenization.
- Other languages and writing systems may produce different ratios.
For a rough sense of length, the estimates can help. For an accurate count, use the tokenizer for the model you plan to use.
What is a context window?
A context window is the token budget a model can use for a single request. OpenAI describes it as “the maximum number of tokens that can be used in a single request.” That budget is not necessarily just the text you send: depending on the model, it can include input, generated output, and reasoning tokens. Context windows and maximum-output settings vary by model, so there is no single capacity that applies to all LLMs. See OpenAI’s conversation-state documentation for its explanation.
A maximum-output setting limits how many tokens a model may generate; it is distinct from the total context window. A request with a long input leaves less room in the shared budget for a response. If your material does not fit, shorten it, split it into multiple requests, or summarize it—and reserve enough space for the answer you need.
Which token types affect API usage and cost?
API usage can distinguish among input, cached input, output, and reasoning tokens. Their rates may differ. Reasoning tokens may not appear in the visible answer, but they can still count toward output usage and billing. Check the documentation and pricing for the specific provider and model because rates and usage rules can change. OpenAI’s API pricing page lists its current categories and rates.
A lower price per million tokens does not automatically mean a cheaper result. The total depends on the input and output lengths, the model’s tokenization, whether input is cached, and any reasoning tokens counted. To compare models or providers fairly, estimate the full workload using representative prompts and responses—not just the input or the headline rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you count tokens?
Count plain text
- Choose the target model. Token counts are model- and encoding-dependent, so a count from a different tokenizer may not match.
- Use the matching tokenizer. For OpenAI text, use the OpenAI Tokenizer for a quick inspection, or the tiktoken library in software.
- Check the complete request when precision matters. A plain-text count may omit formatting or other request components.
Count a structured or multimodal request
Messages, tools, images, files, and other modalities can affect the full request count. For a complete OpenAI Responses API input, use the documented input-token counting method, which is intended to account for request structure and supported content. Google also documents token counting for Gemini, including non-text inputs; use the method for the provider and model you are actually calling.
When evaluating a counting tool, check three things: whether it targets the same model or encoding, whether it counts only plain text or the full structured request, and whether it includes tools or non-text content. A plain-text tokenizer is useful for inspecting text; a provider’s request-level counting method is more appropriate for estimating a full API payload.
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