Tokenizer multilingual issues

How tokenizers treat different languages unevenly—some languages need more tokens for the same meaning, raising cost and changing quality.

Global products inherit fairness and pricing issues from tokenization alone.

Vocabularies trained mostly on some languages fragment others into many pieces. User-facing: higher latency/cost and sometimes worse modeling for under-represented scripts.

The same paragraph in English vs another language may bill very different token counts.

Tokenize one sentence in two languages with a public tokenizer UI/library; compare counts.

⚠️ Myth: Tokens equal words universally.

✓ Reality: Token ≠ word; languages differ sharply.

• Course 07 tokenization
• Provider tokenizer docs (cite specifically)