cjk-aware-text-metrics
DevelopmentCJK/Latin weighted token estimation for multilingual LLM pipelines. Use when processing Japanese/Chinese/Korean text with fixed chars-per-token constants.
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CJK-Aware Text Metrics
Extracted: 2026-02-11 Context: Multilingual LLM pipelines where token estimation affects cost, chunking, or rate limits
Problem
Fixed chars-per-token constants (e.g., CHARS_PER_TOKEN = 4) assume Latin text.
Japanese/Chinese/Korean text uses ~2.5 chars/token, causing ~60% underestimation
in token counts, cost previews, and chunk sizing for CJK-heavy documents.
Solution
- Detect CJK characters by Unicode range:
def _is_cjk(char: str) -> bool:
cp = ord(char)
return (
0x4E00 <= cp <= 0x9FFF # CJK Unified Ideographs
or 0x3040 <= cp <= 0x309F # Hiragana
or 0x30A0 <= cp <= 0x30FF # Katakana
or 0x3400 <= cp <= 0x4DBF # CJK Extension A
or 0xF900 <= cp <= 0xFAFF # CJK Compatibility
)
- Weighted token estimation:
CJK_CHARS_PER_TOKEN = 2.5
LATIN_CHARS_PER_TOKEN = 4.0
def estimate_tokens(text: str) -> int:
cjk_count = sum(1 for c in text if _is_cjk(c))
other_count = len(text) - cjk_count
return int(cjk_count / CJK_CHARS_PER_TOKEN + other_count / LATIN_CHARS_PER_TOKEN)
- Chunk splitting must use token-based accumulation (not char-based):
# BAD: char_limit = token_limit * FIXED_CONSTANT
# GOOD: accumulate estimated tokens per paragraph
current_tokens += estimate_tokens(para)
if current_tokens > token_limit:
flush_chunk()
When to Use
- Building LLM pipelines that process Japanese/Chinese/Korean text
- Implementing chunk splitting for multilingual documents
- Estimating API costs for non-English content
- Any text metric (token count, cost, rate limit) using a fixed chars-per-token constant