What is Token (AI)?
The basic unit of text that AI language models process, typically representing a word, part of a word, or punctuation mark.
Token (AI) Explained
In AI language models, a token is the fundamental unit of text processing. Tokenization breaks text into pieces the model can work with. A token can be a whole word ("content"), a subword ("un" + "happy"), a character, or punctuation. In English, one token averages about 4 characters or roughly 0.75 words. Token counts matter for two practical reasons: they determine API pricing (models charge per token processed) and they define context window limits (the maximum amount of text a model can consider at once). Understanding tokens helps content teams estimate costs, manage context windows for long documents, and optimize prompt efficiency.
Frequently Asked Questions
How many tokens are in a word?
In English, a word averages about 1.3 tokens. Short common words are often a single token. Longer or uncommon words are split into multiple tokens. A 1,000-word article is approximately 1,300-1,500 tokens. Pricing is typically per 1,000 or 1,000,000 tokens.
What is a context window?
The context window is the maximum number of tokens a model can process in a single request (input + output combined). GPT-4 supports up to 128K tokens; Claude supports up to 200K tokens. Larger context windows allow processing longer documents but cost more.
Why do tokens matter for content teams?
Tokens determine AI costs (price per token), document length limits (context window), and response length. Understanding tokenization helps teams estimate review costs, decide when to chunk long documents, and optimize prompts for efficiency.
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