A token is a unit of text that an LLM processes. It can be a word, part of a word, or even a single character, depending on the model's tokenization method. For example, "hello" might be one token, while "unconventional" might be split into multiple tokens.

LLMs break up text into tokens to process language more efficiently and flexibly. Tokenization allows models to work with standardized units, manage vocabulary size effectively, and handle unknown words by breaking them into familiar subcomponents. This approach enables LLMs to operate across multiple languages, capture nuances in word meanings, and maintain a consistent input format. By tokenizing, models can better understand context and relationships between different parts of text, which is crucial for accurate language processing and generation. Essentially, tokenization is a fundamental technique that allows LLMs to interpret and produce human language more effectively.

Token counting is crucial for managing API costs, estimating processing time, and ensuring inputs don't exceed model limits. Many LLM providers charge based on token usage, and models have maximum token limits for inputs and outputs.

For OpenAI models, we use their exact recommended tiktoken library, so the accuracy should be incredibly high. For Anthropic models, we are currently using an older method as Anthropic has not yet provided updated tokenization information for their latest models. We are actively monitoring this situation and will update our tokenization approach as soon as Anthropic releases new information.

Yes, our tool supports multiple languages. While our interface is entirely in English, the tokenization process works the same way for all languages. This means you can input text in any language, and it will be tokenized accurately according to the selected model's method.

Our tool primarily supports token counting for models by OpenAI and Anthropic. We focus on these as they are currently the most widely used in the industry.

Special characters and emojis are processed according to each supported model's tokenization method. Some may count as single tokens, while others might be split into multiple tokens.

Yes, our tool offers real-time token counting. As soon as you paste or type your text into the input area, it immediately displays the number of tokens and provides an estimate of the input cost.

Currently, there is no set maximum input length for our tool. However, we haven't extensively tested the upper limits. If you encounter any issues with large inputs, please let us know by submitting feedback through our feedback form. This will help us improve the tool and establish any necessary limits.

The tool doesn't explicitly define sentence or word boundaries. It follows the tokenization method of each LLM, which may not always align with linguistic boundaries.

Currently, the tool provides a total token count. We're considering adding a feature for per-sentence or per-paragraph breakdowns in a future update.

Currently, we don't offer an API for direct integration. However, if you're interested in using our token counter in your applications, please let us know through our feedback form. Your input will help us gauge interest and potentially develop this feature in the future.

Different models use different tokenization methods. For example, GPT models use byte-pair encoding, while BERT uses WordPiece. Our tool applies the appropriate method for each supported model.

We currently don't offer visualization, but it's on our roadmap. We plan to introduce a feature that shows how text is split into tokens for better understanding.

The tool primarily works with plain text input. For other formats, you'll need to extract the text content before using our counter. We're exploring options to support direct file uploads in the future.

The tool performs well with specialized vocabulary, as it uses the same tokenization methods as the LLMs. However, very rare or newly coined terms might be split into smaller tokens.