Vietnamese text costs 1.5-3x more tokens than English in LLMs

Processing non-English text, particularly Vietnamese, with large language models incurs higher costs due to tokenization inefficiencies. Vietnamese can require 1.5 to 3 times more tokens than English for the same meaning, leading to increased API bills, latency, and context window usage. This is largely because tokenizers are primarily trained on English data, and characters with diacritics in Vietnamese can be split into multiple tokens. Developers can measure these costs using tools like tiktoken for OpenAI-compatible models or llama-tokenize for local models, and should normalize text to NFC form to avoid further tokenization issues. AI

IMPACT Increases operational costs and latency for LLM applications serving non-English languages, prompting developers to optimize tokenization strategies.

RANK_REASON The item discusses the practical implications and technical reasons behind LLM tokenization costs for non-English languages, offering advice and measurement techniques.

Read on dev.to — LLM tag →

• cl100k_base
• GGML
• GGUF
• Hacker News
• llama-8b-q4_k_m.gguf
• llama.cpp
• o200k_base
• qwen-7b-q4_k_m.gguf
• tiktoken

• infra

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