How does this tool help with prompt engineering?

This prompt calculator is designed for prompt engineering to ensure that user requests and AI responses stay within the model's token boundaries. By analyzing your text, it helps you understand the constraints, make the fullest use of the AI's context window, and steer clear of catastrophic mistakes, such as truncated or incomplete answers, when relying on AI.

Why is the site called mfatnz?

MFAT stands for Maximum Fixed Allocation of Tokens. Think of it as a 'quota' for each request that an AI model gives users for each question or command they send it. It's important to know that the 'limit of tokens' allocated for the AI's answer (output) is often more limited and usually quite small compared to the space you have for your input request. This 'space' is measured in tokens, which are like individual words or pieces of words. So, MFAT tells those interacting with AI the highest number of tokens (words or parts of words) their question and the AI's answer can take up for each single interaction. It's really important for not only the New Zealand developers, but all people using AI to understand MFAT. It helps them make sure the requests (and the answers or output) aren't too long and fit within the AI's limits. Knowing this helps to get the most out of the AI and avoid terrible errors.

What is a token in the context of AI models like Gemini or ChatGPT?

A token is the basic unit of text that an AI model processes. It can be a word, a part of a word, or a punctuation mark. For example, the phrase 'token counter' consists of two tokens. Counting tokens is crucial for staying within the model's context limits and managing API costs.

What's the difference between 'Context Window' and 'Max Output Tokens'?

The 'Context Window' is the total amount of information (input + output) the model can process at once. 'Max Output Tokens' is a separate, often smaller, limit on how many new tokens the model can generate in a single response. For example, a model might have a 1,000,000 token context window but can only generate 8,192 tokens in its answer.

Why do models have a max output limit smaller than their context window?

This is done for several reasons: to manage computational costs (generating long text is expensive), to ensure response quality and prevent endless or repetitive outputs, and to encourage more focused and efficient prompting from developers.

Why are AI responses sometimes shorter than expected?

AI models use a portion of their token budget for internal processing, such as following system instructions, remembering chat history, and ensuring safety. This 'hidden' token usage means the usable output for the user is slightly less than the total maximum limit. For instance, a model with a 65,536 token limit might only produce 60,000-62,000 tokens of user-visible text.

Why do I need a token calculator?

This tool is designed to be an instant calculator, providing accurate token counts and output analysis for major AI platforms. It allows you to optimize your input prompts and predict the available output space to prevent your responses from being truncated unexpectedly. It's essential for effective prompt engineering.

AI Token Calculator: Analyze Inputs & Outputs 🚀

Is your text an input prompt or a model's output? This tool helps with both. Paste your text to calculate its token count. Then, see how it performs as an input (predicting the AI's available response room) or analyze it as an output (to check if it was truncated by the model's limit).

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Why Your AI's Answers Seem Shorter Than Expected 🤖

Have you noticed your AI's responses aren't quite as long as the total limit it advertises? There's a good reason for that!

When an AI tells you it has a limit of 65,536 tokens, it sounds like you should get that many units of information. But in reality, you'll usually see the actual message you get is closer to 60,000 to 62,000 tokens.

Why the difference? Because the AI uses some of its "token budget" for its own internal work, sort of like background thinking. These hidden tokens are used for things like:

Secret instructions: Rules the AI follows that you don't see.
Remembering your chat: Keeping track of what you've talked about before.
Making things safe and neat: Tokens for formatting and making sure the response is appropriate.
Figuring things out: The AI's own planning and problem-solving before it gives you an answer.

So, while the AI has a big maximum limit, a part of it is always used for its own operations. What you see is the usable output after all that internal work is done.

Important: Once a model hits its maximum output limit, it will stop generating content, which can result in an incomplete response. To avoid this, it is critical to ensure your requested output fits well within the limit. This tool's 'Output Truncation Analysis' will change to Approaching Limit at 70% and High Risk when your content exceeds 90% of the selected model's limit, signaling a high probability of truncation.

AI Token Calculator: Analyze Inputs & Outputs 🚀

Model Efficiency Analysis

As an Input Prompt

As a Model's Output

Get answers and insights with AI-powered search.

Why Your AI's Answers Seem Shorter Than Expected 🤖

AI Token Calculator: Analyze Inputs & Outputs 🚀

Model Efficiency Analysis

As an Input Prompt

As a Model's Output

Get answers and insights with AI-powered search.

Why Your AI's Answers Seem Shorter Than Expected 🤖

Search Results

Sources:

Major AI API Providers