Count Tokens in a Paragraph

Token counting is the foundation of every cost and context window calculation when working with large language models. Unlike word counting, tokenization splits text at subword boundaries — common words like "the" become a single token, while less common words like "tokenization" may split into two or more tokens. This example shows a realistic paragraph about AI and neural networks so you can see exactly how modern tokenizers handle technical vocabulary, punctuation, and capitalization. The rule of thumb that "1 token ≈ 4 characters" holds well for plain English prose, but technical writing, code, and non-English languages tokenize very differently. In this sample you will notice that model names like "GPT-4" and "Anthropic" are often single tokens, while compound technical terms may split unexpectedly. Running your actual prompts through the counter before deploying prevents surprise cost overruns in production. Use the token count from this example as a baseline: a 200-token paragraph costs roughly $0.0003 with GPT-4o at input pricing. Multiply by the number of requests per day to project monthly costs before committing to an architecture.

Large language models process text by breaking it into tokens — subword units that balance vocabulary size with coverage. A model like GPT-4 uses a vocabulary of roughly 100,000 tokens, allowing it to represent any Unicode text including code, mathematics, and multiple languages. The tokenizer splits words at morpheme boundaries: "tokenization" might become ["token", "ization"] while "the" remains a single token. Understanding token boundaries matters for prompt engineering, cost estimation, and staying within context window limits.