OpenAI vs Llama tokenizers: why the same text produces different token counts
This counter provides text counts for supported OpenAI encodings and Llama 3. Llama 4, Claude, and Gemini are estimates. Use the target tokenizer or provider API for production budgets and full chat formatting.
A sentence that costs 50 tokens on GPT-5 might cost 55 tokens on Llama 4 or 48 on Claude. The differences come from the tokenizer's vocabulary, its merge rules, and how it handles whitespace, capitalization, and non-English text. Even among OpenAI models, GPT-5 (o200k_base) and GPT-4 (cl100k_base) use different tokenizers with different counts.
By TechCompare · Updated
How this is calculated
OpenAI encodings and Meta tokenizer generations have different vocabularies and merge rules. Llama 3 uses its own BPE tokenizer, while Llama 4 has a distinct tokenizer with a 202,048-entry model vocabulary. Vocabulary size alone cannot establish which model uses fewer tokens for a particular prompt. Compare the actual target tokenizers.
Verdict
Do not substitute the Llama 3 tokenizer for Llama 4. They use different vocabularies and can segment the same text differently. This page labels Llama 4 as an estimate until its own tokenizer is supported. Benchmark real prompts instead of assuming that a larger vocabulary always reduces cost.
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Frequently asked questions
Why does the same text tokenize differently on GPT and Llama?
Which is more token-efficient, GPT-5 or Llama 4?
Does tokenizer efficiency change my API bill?
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