Gemini TokenizerGet Gemini Tokens for your text
Get the number of tokens for your text with Gemini Tokenizer.
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How Gemini Tokenization Works
Gemini language models process text using tokens—common sequences of characters found in text datasets. These models learn statistical relationships between tokens and excel at predicting the next token in a sequence.
This tool uses your selected Gemini AI model to provide accurate token counts, helping you understand how text is tokenized and optimize your API costs by knowing exactly how many tokens your prompts will consume.
Token Counting Accuracy
For Gemini models, tokenization aligns with standard subword methods. While the “1 token ≈ 4 characters” rule often applies to English text, precise counts vary by language, content complexity, and model version.
This tool provides the most reliable method by using the actual Gemini API to count tokens, just like Google AI Studio and Vertex AI do.
Important Considerations
- • Code and non-English languages may have different token counts
- • Complex formatting affects tokenization patterns
- • Tokenization can evolve with model updates
- • API-provided counts are the most accurate method
- • No separate tokenizer library exists for Gemini models
Advanced Gemini Tokenizer Features
Our AI-powered gemini tokenizer helps you write with confidence and clarity.
Get Gemini Tokens
Get the number of tokens for your text with Gemini Tokenizer.