Token Quotas is an online token counter and token optimizer. As we lean more on the power of AI and LLMs to enhance our daily lives and even our jobs, we need to be able to ensure we are using these tools effectively and efficiently.

Most of the models that we rely on have token limits and it can be frustrating to hit these limits unexpectedly, especially when dealing with a large amount of text.

Our tool is designed to help you build more efficient and effective propmts by, not only providing a token count, but also by providing suggestions on how to optimize your text to fit within the token limits of these models while keeping the context.

What is a token and how is it different from a word?
Tokens are the basic units of text used in natural language processing (NLP) and machine learning models, such as OpenAI's GPT models. A token can be as short as one character or as long as one word, depending on the language model's tokenization process. For example, the sentence "Hello, world!" might be tokenized into ["Hello", ",", "world", "!"] resulting in 4 tokens.

How does knowing my token count help me?
Knowing the token count of your text or code is crucial for staying within the token limits of different AI models. By ensuring your content fits within these constraints, you can optimize performance, save costs, and ensure your content is compatible with various AI models and APIs.

Prompt engineering efficiency
The development of LLM prompts is called prompt engineering. One of the most important aspects of prompt engineering is ensuring that the prompt is clear and efficient. Prompt optimization is crucial and being awware of the token count of a prompt is a key part of this process. Sometimes you can come up with a prompt that is too long and you need to shorten it and there are some very easy techniques to do this.

One technique you can use is to remove unnecessary words. The unnecessary words used in prompts are called stop words in the NLP world. They are commonly used words (like articles, prepositions, and conjunctions) that are often automatically filtered out in natural language processing because they don't carry significant meaning in the context of analysis. Though "stop words" are usually the most common words in a language, there is no single universal list of stop words used by all natural language processing but you can find a list of the most common stop words in any language.

Our tool uses the specific list of stop words depending on what model you choose.

Take a look at the following prompts:

The first prompt is 33 tokens long, while second prompt is only 14 tokens long but both of them are essentially the same. The second prompt is more efficient and will save you tokens. This is a simple example but it shows how you can shorten a prompt by removing unnecessary words.

How do I use Token Quotas?

• Select the language model that you want to use for token counting. Different models may count tokens differently.
• Enter the text you want to analyze in the input field labeled "Text".
• Click the "Analyze" button to calculate the token count and other data about the text you entered.

Is Token Quotas free to use?
Absolutely! Our tool is free to use and doesn't require any sign-up. We are on a mission to empower you with the tools to help you succeed in your prompt writing.

Will the text I enter be stored anywhere?
We respect your privacy. The text you enter is not stored or saved anywhere. Our tool is solely for calculating token counts and doesn't retain any user data. Check out our privacy policy for more details.

Which GPT models does this token counter support?
This token counter supports GPT models such as GPT-3.5, GPT-4, and other models in OpenAI’s suite. Each model may tokenize text slightly differently, though the method of tokenization is generally similar across models. It’s important to select the correct model for an accurate token count. We are adding logic for new models weekly.

How many tokens are allowed per model?
GPT-3.5 can handle up to 4,096 tokens per request. GPT-4 has different versions: GPT-4-8k (up to 8,192 tokens) and GPT-4-32k (up to 32,768 tokens). This limit includes both the input tokens and the output tokens from the model’s response. Once you choose your model the tool will show you the token limit for the model you choose.

Is this token counter accurate for other languages?
This token counter is accurate for many languages. However, languages with complex scripts or characters (such as Chinese or Japanese) may have different tokenization patterns compared to English, where a single character might count as a full token. Additionally, languages with many compound words (like German) may split a long word into multiple tokens.

Does token count affect the cost of using GPT models?
Yes, the cost of using GPT models is typically based on the number of tokens processed. OpenAI charges for both the input tokens (your prompt) and the output tokens (the model’s response). The more tokens you use, the higher the cost. Keeping track of token count is important to avoid hitting token limits and managing API costs efficiently.

Contact Us

We'd love to hear from you! Whether you have a question, feedback, or just want to say hello, our team is here to help. Please fill out the form below, and we'll get back to you as soon as possible. Your thoughts and inquiries are important to us, and we look forward to connecting with you.