Introduction to Token Limits

When working with Generative AI models, one key factor to understand is the token limit. Token limits determine how much text an AI model can process or generate in a single request. Knowing how to manage these limits is essential for optimizing performance, reducing costs, and ensuring quality output.

What is a Token?

In generative AI, a token is a unit of text processed by the model. Tokens can include:

• Words (e.g., "hello")

• Punctuation marks (e.g., ".", ",")

• Parts of words (e.g., "un" in "unfold" or "ly" in "promptly")

On average, a token corresponds to about 4 characters or 3/4 of a word, but this can vary based on the text's complexity.

Why Do Token Limits Matter?

Token limits play a crucial role in how efficiently a model functions. If the input or output exceeds the limit, the model may truncate text, ignore earlier parts of the input, or stop generating midway through a response.

Three Key Implications of Token Limits:

1. Performance and Efficiency:

   • When a conversation or text exceeds the limit, the model will only consider the most recent tokens, ignoring earlier content. This can lead to incomplete or contextually incorrect responses.

2. Quality of Output:

   • If your token limit is too low, the model might generate incomplete sentences or fail to convey the full idea.

   • Setting the token limit appropriately ensures that the generated text is both coherent and complete.

3. Cost Management:

   • Many AI providers charge based on the number of tokens processed (input and output). Managing token limits efficiently can help minimize costs, especially for large-scale projects.

Token Limits Across Different Models

Different generative AI models have varying token limits, which affects how much text they can process and generate. Let’s look at the most popular models and their capabilities:

What Do These Numbers Mean?

• Context Window: This is the maximum number of tokens that the model can consider at once. For example, GPT-4o via API can handle up to 128k tokens, which makes it suitable for long documents.

• Max Output: This is the maximum length of the generated response. GPT-4o via ChatGPT can generate up to 8,192 tokens in a single response.

Example: Token Limits in Action

Let’s see how these token limits impact text generation:

• Scenario: You are generating a long-form report using Claude 3.5 Sonnet, which has a 200k token context window. You set the output limit to 8,000 tokens. The model will process the entire input efficiently but might stop generating once the output reaches 8,000 tokens.

• Another Scenario: Using GPT-4o via API with a limit of 4,096 tokens, you might find the output truncated if your input text is too long.

Important: If you use GPT-4o via API for lengthy content, break it into smaller sections to ensure completeness.

Practical Tips for Managing Token Limits

1. Choose the Right Model:

   • For short responses or interactive dialogue, GPT-4o via ChatGPT is sufficient.

   • For processing large texts or generating extensive content, use Claude 3.5 Sonnet or GPT-4o via API.

2. Plan the Output Length:

   • If you need lengthy content, set a higher token limit where possible.

   • Use models with higher context windows for tasks like document analysis or summarization.

3. Split Long Texts:

   • If your input text is too long for a single request, split it into smaller segments to fit within the model's context window.

4. Monitor and Adjust:

   • Keep track of how many tokens are used during a session to prevent unexpected truncation.

Using the Online Tokenizer for Token Management

The OpenAI Online Tokenizer is a valuable tool to estimate how many tokens your text will consume when using generative AI models. Tokens can include words, punctuation, or parts of words, and knowing the exact count helps manage both performance and costs. By pasting your text into the tokenizer, you can see how it’s broken down and get an accurate token count. This helps you ensure your input fits within the model’s token limit and avoids incomplete outputs.

Why Use It:

• Estimate Token Count: Check how many tokens your input will use before sending it to the model.

• Optimize Inputs: Adjust phrasing to reduce token usage, saving costs.

• Plan Efficiently: Make sure long texts fit within the model’s context window.

• Real-World Example: Before using GPT-4o via API (4,096 token output limit), check your input to avoid truncation.
  Visit the OpenAI Online Tokenizer and test different phrases to see how they’re tokenized!

Interactive Activities

Activity 1: Token Calculation Exercise

Objective: Estimate the number of tokens for a given text.

1. Choose a sentence or paragraph.

2. Count the characters (including spaces).

3. Divide by 4 to estimate the number of tokens.

4. Compare your estimate with the actual number of tokens using an online tokenizer tool if available.

Example:

• Sentence: "Generative AI models are transforming industries."

• Character count: 48

• Estimated tokens: 48 / 4 = 12

Activity 2: Model Comparison Task

Objective: Understand how different token limits impact text generation.

1. Generate a text of around 3,000 words using Claude 3.5 Sonnet.

2. Try generating the same text with GPT-4o via ChatGPT.

3. Compare the output length and quality from both models.