Compare how different tokenizers split the same text into tokens.
Different subword tokenization algorithms — BPE (used by GPT models), WordPiece (used by BERT), and SentencePiece (used by T5 and Llama) — segment the same text into different numbers of tokens because they build their vocabularies using different merge and scoring rules. This calculator applies representative average characters-per-token ratios for each scheme to the same input length, giving a rough comparative estimate of tokenization efficiency across model families.
estimated_tokens = characters / chars_per_token
Since most LLM APIs bill per token, a less efficient tokenizer (more tokens for the same text) directly increases your API costs and consumes more of the model's context window for the same content.
No — actual chars-per-token varies by language, domain (code vs. prose), and the specific vocabulary each tokenizer was trained on; these are representative averages for English prose.
BPE-style tokenizers (as used by GPT models) are often tuned to be quite efficient for English text, but SentencePiece and WordPiece can be more efficient for other languages depending on training corpus.