What is Token Counter?

Token Counter is a tool that converts text input by users into tokens. Nowadays, many people utilize various AI models to process information, and these AI models charge based on the number of tokens. The conversion from regular text to tokens is not a direct one-to-one mapping; it requires algorithmic computation to accurately convert text into tokens.

Token Counter assists users by converting their text into the corresponding token count, providing them with the correct answer. Additionally, Token Counter will calculate the actual cost associated with the token count, making it easier for users to estimate the expenses involved in using AI models.

With Token Counter, you can easily determine the token count for your text inputs and gauge the potential costs of utilizing AI models, streamlining the process of working with these advanced technologies.

Why do different models have different token counts?

The number of tokens calculated by different models using OpenAI's tiktoken varies due to differences in tokenization strategies. Each model, such as GPT-3, GPT-3.5, and GPT-4, uses a unique tokenizer designed for its specific architecture and training data. These tokenizers break down text into subwords or tokens differently, impacting the total token count.

Factors influencing tokenization include the handling of whitespace, punctuation, and special characters. Thus, identical text can result in varying token counts across different models. This distinction ensures that each model can effectively manage its context window and optimize performance for its intended tasks.

How much does token count?

Understanding the cost of token usage is essential when working with various language models. The table outlines the costs associated with different models, helping users make informed decisions based on their budget and requirements.

The costs are divided into input and output categories. Input cost refers to the price of processing the tokens you send to the model, while output cost refers to the price of generating the tokens in response. For example, using the GPT-4 (turbo) model, processing one million input tokens costs $10.00, and generating one million output tokens costs $30.00. This cost structure allows users to predict and control their expenses based on the volume of tokens processed and generated.