LLM Tokenizer Comparison
Compare token counts for the same text across GPT, Claude, Gemini, Mistral, and Llama tokenizers. See how provider choice affects your token bill.
GPT-4o/3.5 counts are exact (cl100k_base runs in-browser). Other provider counts are ±5–10% estimates. Dataset verified 2026-08-20.
How to Use
Choose a sample or paste text
Select a sample text type (code, prose, multilingual, JSON) or paste your own content.
See token counts per provider
The bar chart shows relative token counts. GPT-4o uses the real tokenizer; others are estimates.
Understand cost implications
Different token counts mean different costs even at the same per-token price. Non-English text varies most.
Pick the most efficient provider
For your specific content type, choose the provider with the best token efficiency relative to price.
Tokenizer efficiency matters at scale
A 10% difference in tokens sounds small. At 10 million API calls/month with 1,000 tokens each, that's 1 billion extra tokens — potentially thousands of dollars. Non-Latin scripts amplify this: Japanese text can use 3× more tokens on less efficient tokenizers. Always test tokenizer efficiency for your specific use case before choosing a provider at scale.
FAQ
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Last verified: 2026-08-20 · methodology · data sources