Getting Started with Python Tiktoken Library: A Comprehensive Guide

In this guide, you'll learn how to use the Tiktoken library in Python, which is a lightweight and efficient library for tokenizing text, counting tokens, and working with tokenized data in various natural language processing (NLP) scenarios.

Table of Contents

  1. Introduction to Tiktoken
  2. Installation
  3. Tokenizing Text
  4. Counting Tokens
  5. Working with Tokenized Data
  6. Conclusion

Introduction to Tiktoken

Tiktoken is a Python library developed by Explosion AI, the same team behind the popular NLP library spaCy. Tiktoken is designed to be fast, efficient, and easy to use when it comes to tokenizing text and managing tokenized data. It's particularly useful for scenarios where you need to count tokens without allocating memory for the actual token strings.


To install Tiktoken, you can use pip:

pip install tiktoken

Tokenizing Text

To tokenize text with Tiktoken, first, import the Tokenizer class from the library, and then create an instance of it. After that, you can use the tokenize method to tokenize a given text:

from tiktoken import Tokenizer

tokenizer = Tokenizer()
text = "This is an example sentence."

# Tokenize the text
tokens = tokenizer.tokenize(text)

# Print the tokens
for token in tokens:

Counting Tokens

Tiktoken provides a TokenCount class that helps you count tokens without creating token strings. This can be useful for tasks like estimating the cost of processing large documents with an API that charges per token.

from tiktoken import Tokenizer, TokenCount

tokenizer = Tokenizer()
text = "This is an example sentence."
token_count = TokenCount()

# Count the tokens in the text
for token in tokenizer.tokenize(text):
    token_count[token] += 1

# Print the token frequency
for token, count in token_count.items():
    print(f"{token}: {count}")

Working with Tokenized Data

Tiktoken also provides a TokenRegistry class that allows you to work with tokenized data in a more efficient way. You can use it to register tokens and their corresponding IDs, and then use the IDs to access the tokens without creating token strings.

from tiktoken import Tokenizer, TokenRegistry

tokenizer = Tokenizer()
token_registry = TokenRegistry()
text = "This is an example sentence."

# Register tokens and get their IDs
token_ids = [token_registry.add(token) for token in tokenizer.tokenize(text)]

# Access tokens using their IDs
for token_id in token_ids:
    token = token_registry[token_id]


In this guide, you learned how to use the Tiktoken library in Python for tokenizing text, counting tokens, and efficiently working with tokenized data. Tiktoken is a powerful tool that can help you simplify your NLP workflows, especially when dealing with large-scale text processing tasks.

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