ChatGPT Tokens 101: What They Are and How They Work

One of the most fascinating aspects of AI is the use of tokens. Tokens are a fundamental building block of natural language processing, and they play a critical role in the functioning of many AI models, including ChatGPT. In this article, I will explain what tokens are and how they are used in ChatGPT. So what are ChatGPT tokens?

Tokens are the building blocks of natural language processing in AI, including ChatGPT. Tokenization converts raw text into a sequence of tokens that ChatGPT processes. Tokenization allows ChatGPT to analyze text more accurately and efficiently. 4096 is ChatGPTs token limit which is about 2,731 words.

At a basic level, tokens are simply a way of breaking down a piece of text into smaller, more manageable units. In the case of natural language processing, these units are typically individual words or phrases. By breaking down text into tokens, an AI model like ChatGPT can more easily analyze and understand the meaning of the text. This is because tokens provide a standardized way of representing language that the model can work with.

Artists’ Vision of Tokenization

However, the use of tokens goes beyond just breaking down text into individual units. Tokens can also be used to represent other aspects of language, such as grammar and syntax. By encoding these aspects of language into tokens, an AI model like ChatGPT can more accurately understand the structure and meaning of a piece of text. This allows the model to generate more coherent and accurate responses to user input.

Table of Contents

What are Tokens?

One of the most fundamental concepts in NLP systems is tokens. In this section, I will provide an overview of what tokens are and how they are used in ChatGPT.

Definition of Tokens

Tokens are a fundamental component of natural language processing (NLP) and are used in AI models such as ChatGPT to process and understand natural language. Tokens are simply the individual units of text that NLP models use to represent textual data.

Converting words into tokens involves breaking down words into their individual components, such as letters or syllables. For example, the word “ChatGPT” would be broken down into the tokens “Chat”, “G”, “P”, and “T”.

Here’s a table with examples to help illustrate this process:

OpenAI[Open, AI]
tokenization[token, ization]
converting[convert, ing]
ChatGPT[Chat, G, P, T]

As you can see in the table, some words are broken down into individual letters, while others are split into multiple tokens. This depends on the specific tokenization technique being used.

One common formula for converting words into tokens involves counting the number of characters in the word and adding one to account for spaces or punctuation. For example, the word “Hello” would be converted into two tokens using this formula: [Hello]. However, this formula is just an approximation and may not always be accurate.

In general, the process of converting words into tokens is dependent on the specific tokenization technique being used and can vary based on factors such as language and context. However, understanding the basics of tokenization can help improve your understanding of how AI models like ChatGPT process and understand natural language.

Types of Tokens

There are several types of tokens that can be used in natural language processing:

  • Word Tokens: These are tokens that represent individual words in a sentence.
  • Punctuation Tokens: These are tokens that represent punctuation marks such as periods, commas, and question marks.
  • Number Tokens: These are tokens that represent numbers, such as “1” or “100”.
  • Special Tokens: These are tokens that are used for special purposes, such as indicating the end of a sentence or marking the beginning of a new paragraph.

Each type of token serves a specific purpose in natural language processing and is used to help computers understand and process human language more effectively.

Tokenization in ChatGPT

In ChatGPT, tokenization is used to convert raw text into a sequence of tokens that can be processed by the model. This then allows the model to analyze the text.

Number of Tokens Per Word

Number of tokens = Number of words x Tokens per word

In the case of ChatGPT, the average number of tokens per word varies depending on the specific model being used. However, as a general estimate, we can assume an average of around 1.5 tokens per word for the gpt-3.5-turbo-0301 model.

Using this estimate, we can calculate the maximum number of words that can be entered into the ChatGPT text box while staying within the token limit of 4096 tokens:

Number of Tokens We Can Use In The ChatGPT Text Box

Number of tokens = 4096 Tokens per word = 1.5 (approximately)

Since, Number of words = Number of tokens / Tokens per word Number of words

and 4096 is the number of tokens we can input into the ChatGPT text box.

Number Of Words Max in The ChatGPT Textbox = 4096 tokens / 1.5 tokens/word= 2731 words (rounded to the nearest whole number)

So, using the gpt-3.5-turbo-0301 model in ChatGPT, we can estimate that the text box can hold around 2731 words while staying within the token limit. This is of course just an estimate because 1.5 is an average.

How Tokens are Used in ChatGPT

ChatGPT is a generative language model that uses tokens to generate text. The GPT family of models process text using tokens, which are common sequences of characters found in text. The models understand the statistical relationships between these tokens, and excel at producing the next token in a sequence of tokens. In ChatGPT, the tokenization process involves splitting the input text into individual words or tokens. This is often done by using white space or punctuation to identify word boundaries, but more advanced techniques, such as part-of-speech tagging and named entity recognition, can also be used.

Once the text has been tokenized, it is fed into the model as a sequence of tokens. The model then uses this sequence to predict the next token in the sequence. This process is repeated until the desired length of text has been generated. The output of the model is then post-processed to remove any unwanted tokens, such as punctuation or stop words.

Benefits of Tokenization in ChatGPT

Tokenization is an important step in the text processing pipeline in ChatGPT. It allows the model to process text more efficiently and accurately. By breaking text into smaller units, the model can better understand the meaning of the text and generate more accurate responses. Tokenization also helps to reduce the amount of memory required to process text, which is important for models like ChatGPT that process large amounts of text.

Another benefit of tokenization is that it allows for more efficient training of the model. By breaking text into smaller units, the model can learn the statistical relationships between tokens more easily. This leads to better performance on tasks such as language modeling and text generation.

Token IDs – How ChatGPT Keeps Track of Words

Tokenization is the process of converting a text into a sequence of tokens, which are essentially numerical representations of individual words or subwords. Token IDs are unique numerical identifiers assigned to each token in a given vocabulary. These IDs are used by machine learning models like ChatGPT to process and understand natural language.

To better understand token IDs, let’s consider an example sentence: “I am a big fan of natural language processing.”

In the tokenization process, this sentence might be split into individual words, each of which is assigned a token ID based on the vocabulary. Here’s an example vocabulary with token IDs assigned to each word:

WordToken ID

Note that some of the words have been broken into parts, such as “process” being split into “process” and “ing”. This is a common practice in tokenization, as it allows the model to better understand the structure and meaning of words.

In this example, the token IDs are assigned based on the order of the words in the vocabulary. So, “I” is assigned token ID 143, “am” is assigned token ID 13, and so on. The token IDs are then used to represent the sentence in a machine-readable format, which can be fed into a machine-learning model like ChatGPT for processing.

Using token IDs instead of the original text allows for more efficient and effective processing by machine learning models, as numerical representations are easier for these models to work with. Additionally, tokenization allows for the handling of out-of-vocabulary words, as these can be split into subwords and assigned new token IDs.

Tokenization Challenges

As I mentioned earlier, tokenization is a critical step in natural language processing (NLP) and is used in many AI applications. However, tokenization is not without its challenges. In this section, I will discuss some of the common challenges that arise during tokenization and their solutions.

Common Challenges and Solutions

One of the most common challenges in tokenization is dealing with words that have multiple meanings. For example, the word “bank” can refer to a financial institution or the edge of a river. In such cases, the tokenization process needs to be able to differentiate between the two meanings and assign the appropriate token. One solution to this problem is to use part-of-speech (POS) tagging, which identifies the grammatical category of each word in a sentence and helps to disambiguate words with multiple meanings.

Another challenge in tokenization is dealing with words that are misspelled or not in the dictionary. This can happen due to typos, slang, or jargon. One solution to this problem is to use a spell checker or a language model that can recognize and correct misspelled words. Additionally, some tokenizers use a process called stemming, which reduces words to their root form, to handle variations of words.

Limits To the Number of Tokens in Context Vector

Another challenge in tokenization is the limited number of tokens that can be used in a context vector. A context vector is a mathematical representation of the words surrounding a target word in a sentence. The context vector is used to determine the meaning of the target word. However, the number of tokens that can be included in the context vector is limited by the memory capacity of the system. This can be a problem when dealing with long sentences or documents.

This memory capacity for ChatGPT is approximately 2,731 words for the context vector. I explained how to calculate in the section about tokenization in ChatGTP.

One solution to this problem is to use techniques such as subword tokenization or byte-pair encoding (BPE), which breaks in down words into smaller units that can be combined to form new words. This allows for a larger vocabulary without increasing the number of tokens in the context vector. Another solution is to use attention mechanisms, which allow the system to focus on the most relevant tokens in the context vector.


In conclusion, tokens play a crucial role in ChatGPT’s natural language processing capabilities. By breaking down input text into smaller, more manageable pieces, ChatGPT is able to generate more accurate and relevant responses to user queries. Tokenization also helps with language modeling, allowing ChatGPT to predict the likelihood of certain words or phrases appearing in a given context.

Token IDs are another important aspect of ChatGPT’s tokenization process. These unique numbers allow ChatGPT to keep track of each individual token and its associated properties, such as its position in the input text and its frequency of occurrence. This information is used to build a vocabulary of all possible tokens, which ChatGPT can then draw upon when generating responses.

Overall, understanding how tokens work in ChatGPT can help us appreciate the incredible complexity and sophistication of this AI-powered chatbot. As we continue to develop more advanced natural language processing technologies, it is likely that tokenization will continue to play a central role in helping machines understand and respond to human language in more nuanced and sophisticated ways.


Chris Chenault trained as a physicist at NMSU and did his doctoral work in biophysics at Emory. After studying medicine but deciding not to pursue an MD at Emory medical school Chris started a successful online business. In the past 10 years Chris's interests and studies have been focused on AI as applied to search engines, and LLM models. He has spent more than a thousand hours studying ChatGPT, GPT 3.5, and GPT4. He is currently working on a research paper on AI hallucinations and reducing their effects in large language models.

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