Tokenizer Comparison Calculator
Compare estimated token counts for the same text under BPE, WordPiece, and SentencePiece tokenization schemes.
Inputs
Fewest Tokens
426tokens
Most Efficient Tokenizer
WordPiece (BERT-style)
Most Tokens
500tokens
Least Efficient Tokenizer
BPE (Byte-Pair Encoding, GPT-style)
Step by step
Most token-efficient
WordPiece (BERT-style)
= 426 tokens
Least token-efficient
BPE (Byte-Pair Encoding, GPT-style)
= 500 tokens
Tokenizer Comparison
| Tokenizer | Chars/Token (avg) | Estimated Tokens |
|---|---|---|
| — | 4.0 | 500 |
| — | 4.7 | 426 |
| — | 4.3 | 465 |
How it works
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.
Formula
estimated_tokens = characters / chars_per_token
- characters
- Total character count of the text
- chars_per_token
- Average characters per token for the tokenizer type
Frequently Asked Questions
Why does tokenizer choice matter for cost?
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.
Are these ratios exact for any text?
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.
Which tokenizer is generally most efficient?
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.