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Gemini tokenization: how Google's models handle tokens differently from OpenAI and Anthropic

Gemini uses Google's internal SentencePiece tokenizer with a large vocabulary that handles 100+ languages natively. For English text, Gemini tends to use slightly more tokens than GPT-5's o200k_base tokenizer (roughly 5-10% more) because the vocabulary is optimized for multilingual balance rather than English-specific efficiency.

Knowledge area
Model Comparison
Tokenization across model families
Topic focus
Gemini tokenization
gemini

How this is calculated

Gemini's tokenizer is particularly efficient for non-English languages, especially Asian scripts (Chinese, Japanese, Korean) and Indic languages where GPT tokenizers sometimes produce very high token counts. A Japanese sentence that takes 50 tokens on GPT-5 might take 30 tokens on Gemini 3.1 Pro. For purely English workloads, GPT-5's tokenizer is slightly more efficient. For multilingual applications, Gemini's tokenizer is often the better choice. Google doesn't publish the tokenizer for external use, so all pre-request token counts are estimates. After a request, the API response includes usageMetadata with the exact token count.

Verdict

Gemini excels at multilingual tokenization. For English-only apps, GPT-5 is slightly more token-efficient. For apps serving a global audience in multiple languages, Gemini's tokenizer can meaningfully reduce costs.

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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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