I typed some German and it was breaking up words so much more than English. Not really surprising given tokenizers are optimized for most commonly used text.
Here's the token efficiency of a corpus translated into various languages and tokenized with the latest OpenAI one:
Language Relative tokens
--------------------------------------
English 1.00x
Portuguese 1.23x
Chinese (Simplified) 1.25x
German 1.31x
Spanish 1.32x
French 1.37x
Arabic 1.38x
Chinese (Traditional) 1.42x
Korean 1.47x
Swahili 1.49x
Hindi 1.57x
Japanese 1.66x
Burmese 3.16x
Amharic 5.78x
Santali 13.70x
Source: "Tokenizer Fairness in 2026", a reproduction/extension of
Petrov, La Malfa, Torr & Bibi, "Language Model Tokenizers Introduce
Unfairness Between Languages" (NeurIPS 2023), using FLORES-200.
I typed some German and it was breaking up words so much more than English. Not really surprising given tokenizers are optimized for most commonly used text.
Here's the token efficiency of a corpus translated into various languages and tokenized with the latest OpenAI one:
Source: "Tokenizer Fairness in 2026", a reproduction/extension of Petrov, La Malfa, Torr & Bibi, "Language Model Tokenizers Introduce Unfairness Between Languages" (NeurIPS 2023), using FLORES-200.https://github.com/partyfly/tokenizer-fairness-2026