AI Content Hub

Regularly check model provider documentation for updated tokenization rules and pricing.

Most modern LLMs use subword tokenization (e.g., BPE, WordPiece). This allows the model to handle rare or unknown words by breaking them down into smaller, known subwords. It strikes a balance between the simplicity of word-level tokenization and the flexibility of character-level tokenization, allowing for a manageable vocabulary size while still being able to represent any word.

Some tokenizers treat words with leading spaces differently. For example, ' hello' might be a different token than 'hello'. Be consistent with spacing, especially when providing examples, to ensure token efficiency.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

Use a tokenizer tool (like the one on TokenCalculator.com!) to see how your text is split into tokens. This helps you understand why certain phrasing might consume more tokens and how to optimize it.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.