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OpenAI vs Llama tokenizers: why the same text produces different token counts

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.

Knowledge area
Model Comparison
Tokenization across model families
Topic focus
OpenAI vs Llama tokenizer
tokenizer-comparison

How this is calculated

OpenAI's o200k_base tokenizer has a larger vocabulary (200K tokens) than cl100k_base (100K tokens), which generally means fewer tokens for the same input because more common words and subwords are stored as single tokens. Llama's tokenizer is based on sentencepiece BPE with a 128K vocabulary and tends to be slightly less efficient for English but better for code and multilingual text. Anthropic doesn't publicly release its Claude tokenizer, so all Claude token counts are estimates. The practical impact is on API costs: at GPT-5 pricing, a 10% difference in tokenization efficiency can mean hundreds of dollars per month for high-volume applications.

Verdict

Token counts vary by model. For cost-critical applications, benchmark the actual token count with the target model's tokenizer. The built-in token counter on this page gives exact counts for OpenAI and Llama models and reasonable estimates for Claude and Gemini.

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Frequently asked questions

What is a token in an LLM?
A token is a chunk of text that a language model reads as a single unit. It is usually a common word, part of a longer word, or a piece of punctuation rather than a whole word or a single character. As a rough rule of thumb, one token is about four characters of English text, and 100 tokens is roughly 75 words.
How accurate is this token counter?
For OpenAI and Llama models the count is exact, because it uses the same Byte Pair Encoding tokenizers those models ship (o200k_base for GPT-5 and GPT-4o, cl100k_base for GPT-4 and GPT-3.5, and the Llama 3 tokenizer for Llama 3 and 4). For Claude and Gemini the count is a labelled estimate, since those tokenizers are not publicly available to run in the browser. Estimates are typically within about 10% of the real value.
Why do different models report different token counts?
Each model family is trained with its own tokenizer and vocabulary, so the same sentence can split into a different number of tokens depending on the model. Newer vocabularies like OpenAI's o200k_base are generally more efficient, packing more characters into each token, which lowers the count compared to older tokenizers.
Is my text sent to a server?
No. All tokenization and counting happens locally in your browser using a tokenizer that loads on the page. Nothing you type or paste is uploaded, logged, or stored, which makes the tool safe to use for private prompts and confidential text.

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