Tokens

Tokens are the fundamental units that AI language models use to process text. A token can represent a whole word, a subword fragment, a punctuation mark, or a special character. Tokenization—the process of splitting text into tokens—is how models convert human language into a numerical format they can manipulate.

In English, one token roughly equals 0.75 words (or about 4 characters), though this varies by tokenizer and language. The phrase "artificial intelligence" might become two or more tokens depending on the model. Complex words, technical jargon, and non-English languages typically require more tokens per concept.

Tokens matter for three practical reasons. First, context windows are measured in tokens—GPT-5.4's 256K context window means it can process approximately 192,000 English words at once. Second, API pricing is token-based: providers charge per input and output token, making token efficiency a cost concern for high-volume applications. Third, rate limits and quotas are often expressed in tokens per minute.

Different models use different tokenization schemes—byte-pair encoding (BPE), SentencePiece, and tiktoken are common approaches. Modern tokenizers from OpenAI, Anthropic, and Google have become more efficient, reducing the token count for equivalent text compared to earlier generations.

For content creators and GEO optimization, token efficiency influences how much of your content an AI model can process in a single pass. Concise, well-structured writing uses fewer tokens, allowing more content to fit within context limits. Avoiding unnecessary repetition and choosing clear language over jargon helps maximize the information density AI systems can work with.