OpenAI: How to estimate tokens before sending a query

Posted: Feb 4, 2025.

What are Tokens?

When you input text, it's broken down into smaller pieces (tokens) that can be words, parts of words, or characters. Each token is assigned a unique numerical ID from the model's vocabulary.

This tokenization helps computers understand and process language mathematically, manage memory efficiently, and handle text consistently.

Token Estimation with Tiktoken

Tiktoken is a fast BPE (Byte Pair Encoding) tokenizer specifically designed for OpenAI models. It provides a convenient way to tokenize text and count tokens programmatically.

Here's an example of how to use Tiktoken to count tokens:

pip install tiktoken

import tiktoken

def count_tokens(text, model="gpt-3.5-turbo"):
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))

# Example usage
text = "You are a helpful, pattern-following assistant that translates corporate jargon into plain English."
model = "gpt-3.5-turbo"
token_count = count_tokens(text, model)
print(f"Token count for '{text}' using {model}: {token_count}")

npm i tiktoken

import { getEncoding, encodingForModel } from "js-tiktoken";

function countTokens(text, model = "gpt-3.5-turbo") {
    try {
        const enc = encodingForModel(model);
        const tokens = enc.encode(text);
        return tokens.length;
    } catch (error) {
        console.error('Error counting tokens:', error);
        return 0;
   }
}

// Example usage
const text = "You are a helpful, pattern-following assistant that translates corporate jargon into plain English.";
const model = "gpt-3.5-turbo";

const tokenCount = countTokens(text, model);
console.log(`Token count for '${text}' using ${model}: ${tokenCount}`);

Why Count Tokens Programmatically?

Counting tokens is important for two main reasons:

Text Length Limit: OpenAI models have a maximum token limit for input text. By counting tokens programmatically, you can determine whether a given text string exceeds the model's capacity before sending the API request.
API Usage Costs: OpenAI's API usage is priced based on the number of tokens processed. Knowing the token count helps you estimate and manage the cost of your API calls.

Lunary Tokenizers

For a more interactive approach, the Lunary Tokenizers Page offers a user-friendly way to analyze your text.

See how your text is broken down into tokens
Estimate the cost of your API calls based on current pricing
Compare costs across different models
Refine your prompts to fit within model limits

Try our tokenizers for various AI providers:

OpenAI Tokenizer: https://lunary.ai/openai-tokenizer
Mistral Tokenizer: https://lunary.ai/mistral-tokenizer
Anthropic Tokenizer: https://lunary.ai/anthropic-tokenizer
Llama Tokenizer: https://lunary.ai/llama3-tokenizer

OpenAI Tokenizer

The price estimation feature helps understand the cost implications of your API calls before you make them. This is especially useful when working with longer texts or when you need to budget for large-scale applications.

FAQ

Q: Why is it important to count tokens?

A: Counting tokens helps you avoid exceeding model input limits and manage API usage costs.

Q: How do different models affect token counts?

A: Different models use different encoding schemes, which can result in varying token counts for the same text. For example, the GPT-4 tokenizer may produce different tokens than the GPT-2 tokenizer for the same input text.

Q: Can I train my own tokenizer?

A: Yes, you can train your own tokenizer and restrict its token space by various parameters, including the size of the vocabulary.

Conclusion

Token estimation is a important when working with LLM services, both for technical feasibility and cost management. Accurately counting tokens before making API calls helps developers avoid errors and optimize costs.

Implement these token estimation practices and build more efficient and cost-effective AI applications.

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