Yes, this is my point - the tokenizer OpenAI uses is optimized for european languages as it is an alphabetic tokenizer designed for consonants and vowels. I'm wondering why they don't move away from BPE all together and just increase the vocabulary size to give each symbol in each logographic language its own token. This problem must eventually be solved for multilingual models to have similar cost and capabilities across languages.

So the real question is what is the best tokenization approach to use for a truly multilingual model, and why?