Tokenization Visualizer

📊 Same Meaning, Different Token Counts

The same semantic content requires wildly different numbers of tokens depending on language. This affects model capacity and inference cost.

🇺🇸 English 6 tokens

"Hello, how are you?"

Hello , how are you ?

🇯🇵 Japanese 11 tokens

"こんにちは、お元気ですか？"

こんにちは、お元気ですか？

🇹🇭 Thai 18 tokens

"สวัสดี สบายดีไหม?"

ส ##วัส ##ดี [space] ส ##บา ##ย ##ดี ##ไหม ? ...

🇸🇦 Arabic 14 tokens

"مرحبا، كيف حالك؟"

مر ##حب ##ا ، كي ##ف حال ##ك ؟ ...

⚠️ Why Tokenization Matters for Multilingual Models

Token Budget Inequality

If Thai uses 3x more tokens than English for the same content, Thai users hit context limits 3x faster. This is a fairness issue.

Cost Disparity

API pricing is per-token. The same query costs more in languages with lower tokenizer efficiency. CJK and Thai users pay more.

[UNK] Failures

Characters not in the vocabulary become [UNK] tokens, losing all semantic information. Common with rare scripts and emoji.

Subword Splits

When words split into many subwords, the model sees fragments instead of meaning. This hurts accuracy on low-resource languages.

🧪 Tokenization Stress Tests

Click to load each test case. These reveal common tokenization failure modes.

Emoji Overload High Token Count

🔥💯👏🏽 This is fire!!! 🚀🚀🚀

Emoji often become multiple tokens or [UNK]. Skin tone modifiers add complexity.

Code-Switching + Emoji Error Prone

I was like omggg 😭😭 pero también un poco feliz you know

Mixing English, Spanish, internet slang, and emoji. A perfect storm for tokenizers.

Numbers & Special Formats Format Sensitive

The meeting is at 3:30pm on 12/25/2024 @ conference room #42-B

Times, dates, and alphanumeric codes often split unpredictably.

Long English Words Subword Heavy

pneumonoultramicroscopicsilicovolcanoconiosis antidisestablishmentarianism

Even English has words that fragment into many subwords, affecting rare medical/technical terms.

Thai (No Spaces) Script Challenge

กรุงเทพมหานคร อมรรัตนโกสินทร์ มหินทรายุธยา

Thai has no word boundaries. Tokenizers must guess where words start and end.

URL Structure Lost

https://example.com/path?query=value&foo=bar#section

URLs often become many fragments, losing their semantic structure as a "link".