Token Calculator

Calculate tokens and estimate costs for OpenAI models

Token Count: 0
Input Price: $0.000000
Output Price: $0.000000
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Frequently Asked Questions

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.
Tracking token usage ensures you don't exceed model limits, control costs and maintain efficient communication.
The tool uses the same tokenization method as GPT-3.5 to GPT o1, allowing you to accurately count tokens in your input.
The word-to-token ratio varies by language. For example, in English, 1 word is about 1/3 tokens, while in Spanish, it's about 1/2 tokens.
Punctuation marks count as 1 token, while emojis range from 2 to 3 tokens each, depending on the model's tokenization.
LLMs tokenize text to process it more efficiently, standardizing the input and helping with computational resource management.
Token-based models charge based on the number of tokens used, so managing token counts helps you optimize costs.
A prompt is the input or instruction provided to a model to guide its response. A clear prompt ensures effective output.
Yes, the tool supports multiple languages for tokenization. While the interface is in English, the tokenization process applies to any language you input.
Our tool exclusively uses OpenAI models, which utilize byte-pair encoding (BPE) for tokenization. This method ensures efficient handling of text across all supported languages and tasks.
Yes, the tool offers real-time token counting as you type or paste text, immediately displaying the token count and estimated input cost.
GPT-3.5-turbo has a maximum token limit of 4096 tokens per interaction. GPT-4 models include GPT-4-8k with a limit of 8192 tokens and GPT-4-32k with a limit of 32,768 tokens. The latest GPT-4o and GPT-4o-mini models offer a 128K token context. Additionally, OpenAI's o1-preview and o1-mini models also provide 128K token context, making them suitable for handling large inputs and more complex tasks.
The tool uses OpenAI's official tokenization libraries, ensuring high accuracy for models like GPT-3.5, GPT-4 and above.
Currently, the tool only supports plain text input. For other file formats, you need to extract the text content before using the tool.
Currently, there is no direct API integration, but feedback is welcomed to gauge interest and potentially develop this feature in the future.
By keeping prompts concise and managing token usage, token counting ensures effective communication with the model, avoiding unnecessary verbosity and improving response quality.
If you exceed the token limit, the model will truncate or reject the input, leading to incomplete responses or errors. Managing token counts helps prevent this.
The tokenization process handles specialized vocabulary well, although very rare or newly coined terms may be split into smaller tokens depending on the model.
Prompt tokens are the tokens in your input to the model, while response tokens are the tokens the model generates in its output. Both contribute to the overall token count in an interaction.
While visualization is not currently available, it is on the roadmap for future updates to help users understand how their text is split into tokens.