Token Counter

Estimate AI Model Costs by Counting Tokens

Free

Description

Token Counter is a utility designed to help users understand the token usage and associated costs when working with various AI language models, particularly those from OpenAI like ChatGPT. It functions by converting user-provided text into the corresponding number of tokens based on the specific model selected (e.g., GPT-4, GPT-3.5 Turbo). This conversion is crucial as AI models typically charge based on token count, and the process isn't a simple character count but involves algorithmic computation based on each model's unique tokenization strategy.

Beyond just providing the token count, Token Counter calculates the estimated cost for processing that text, referencing the known input and output pricing structures for different models. This allows users to effectively gauge potential expenses before running tasks through AI APIs, aiding in budget management and informed decision-making when utilizing these powerful technologies. The tool also explains the reasons behind varying token counts across different models, highlighting factors like whitespace and punctuation handling.

Key Features

Text-to-Token Conversion: Converts input text into token counts.
Multi-Model Support: Calculates tokens for various OpenAI models (GPT-4, GPT-3.5, etc.) and indicates support for Llama & Claude.
Cost Estimation: Calculates estimated usage costs based on token count and model pricing.
Tokenization Explanation: Provides insights into why token counts differ between models.
Model Pricing Display: Shows input/output costs per million tokens for supported OpenAI models.

Use Cases

Estimating API costs for AI projects.
Comparing token usage across different AI models.
Budgeting for AI application development.
Understanding AI model pricing structures.

Frequently Asked Questions

What is Token Counter?

Token Counter is a tool that converts text into tokens for different AI models and calculates the estimated cost based on the token count and the specific model's pricing.

Why do different AI models show different token counts for the same text?

Different models (like GPT-3, GPT-3.5, GPT-4) use unique tokenizers designed for their specific architecture and training data. These tokenizers handle text elements like whitespace and punctuation differently, leading to variations in the total token count for identical text.

How does Token Counter estimate costs?

It uses the published input and output costs per million tokens for various AI models (e.g., $10 input / $30 output per 1M tokens for GPT-4 Turbo) and applies these rates to the calculated token count of the user's text.