AI Prompt Token Counter

Check out our Word Counter, JSON Formatter, or Base64 Encoder for more tools.

Quick Solution

To immediately count tokens, paste your text into the left input panel and select your target model from the dropdown. The tool instantly applies the correct tokenizer (such as o200k_base for GPT-4o) and outputs the exact token count and estimated cost.

When Should I Use This?

Counting tokens is critical for cost control and preventing API errors in production applications.

• Estimating API costs before triggering large batch processing jobs across thousands of documents or database rows.
• Staying under context limits to avoid fatal context_length_exceeded errors when injecting multiple large files into a single prompt.
• Comparing tokenizers to determine if upgrading to a newer model's vocabulary will process your data more efficiently.

Troubleshooting

Issue: Token counts differ from my backend API logs
Fix: Ensure you are using the correct tokenizer for your model. If you are using GPT-4o but selecting GPT-4 in the tool, the counts will vary because GPT-4o uses the newer o200k_base encoding instead of cl100k_base.

Issue: Unexpectedly high token counts for short strings
Fix: Special characters, emojis, and non-English languages are often less efficiently tokenized. If your payload includes raw JSON, minify it to remove whitespace tokens (which often count individually) to drastically reduce the total count.

Understanding Tokenization in Large Language Models

In the world of Artificial Intelligence and Large Language Models (LLMs), a token is the fundamental unit of data processed by the model. Unlike humans who read word by word, AI models process text by breaking it down into chunks (tokens). A token might be an entire word, a syllable, a single character, or even a space.

Different models use different algorithms to tokenize text. For instance, OpenAI's latest models use a highly optimized vocabulary called o200k_base, which is more efficient at handling multiple languages and code than the older cl100k_base. This means a single block of text could result in fewer tokens (and thus cost less) on newer models.

Model Comparison Table

Here is a brief comparison of how tokenization and pricing structures vary across popular OpenAI models:

API Cost Calculation Guide

Calculating the true cost of an API call involves both the input (prompt) tokens and the output (completion) tokens. This token counter tool helps you accurately predict the input cost, which is crucial when feeding large documents or entire codebases into the context window.

To manually calculate your costs, use the formula:

Remember that while output tokens are generally more expensive than input tokens, the bulk of a heavy processing job is often the input context. Optimizing your prompts by stripping unnecessary whitespace, comments, and redundant data can significantly lower your overall API bill over thousands of requests.