What is the role of Tokenization in NLP and how does it affect model costs?

All answers to this question.

You can use a local "tokenizer" library like Tiktoken to count your tokens before you send the request. This helps you stay within your budget and limits.

   Answered 2025-01-20 by Donna Clark

• That's a great tip, Donna. We integrated Tiktoken into our middleware to automatically truncate any user inputs that would exceed our cost-per-request threshold.

     Commented 2025-01-30 by Kimberly Adams

A token is rarely a full word in modern NLP. Most models use sub-word tokenization like BPE to handle rare words or suffixes. For example, the word "unhappy" might be split into "un" and "happy." In late 2023, we analyzed our billing and realized that code snippets use significantly more tokens than plain English because of whitespace and special characters. You aren't just charged for words; you're charged for every fragment. To save costs, try stripping unnecessary whitespace and using shorter system prompts, as every single token in the prompt is processed for every single API call.

   Answered 2025-11-05 by Mary Robinson

Does using different languages like Spanish or Mandarin significantly increase the token count compared to English for the same paragraph of text?

   Answered 2025-12-15 by Charles Wright

• Charles, unfortunately, yes. Most tokenizers are trained primarily on English data. A single Mandarin character might be represented by 2 or 3 tokens, making non-English prompts more expensive.

     Commented 2025-01-02 by Thomas Miller