Tokens in AI: The Building Blocks Behind Every Prompt
Introduction
If you’ve spent any time using ChatGPT or other AI tools, you’ve probably come across the word “token.” You might have seen phrases like “you’ve used 500 tokens” or “this model can handle up to 4,000 tokens.” But what exactly is a token in the AI world? And why do they matter?
In this article we break tokens down into simple, clear terms.
What is a Token?
At its most basic level, a token is a piece of text - smaller than a sentence, often smaller than a word. In AI models like ChatGPT, tokens are the fundamental units used to process and generate language.
A token could be:
A whole word (e.g., "apple")
Part of a word (e.g., "unhapp" and "y" might be two tokens)
A punctuation mark (e.g., ".")
A space character or newline
For example, the sentence:
"Hello there! How are you?"
...might be broken into the following tokens:
["Hello", "there", "!", "How", "are", "you", "?"]
Depending on the tokenizer (more on that below), this would be about 7 tokens.
How Tokens Are Used in AI Models
Language models like ChatGPT or Claude don’t understand language the way humans do. Instead, they work with numerical representations of text, and this starts by breaking the text into tokens.
Once text is tokenized, each token is converted into a vector (a list of numbers) that the model can process. The model then predicts the next token, one at a time, using a complex mathematical framework known as a transformer architecture.
This prediction loop continues:
Tokenize input
Convert tokens into vectors
Predict next token(s)
Convert predicted tokens back into words
So when you enter a prompt, both your input and the model’s output count toward the total number of tokens used.
Tokens and Model Training
During training, an AI model is fed massive amounts of text such as books, articles, code and web pages. This data is broken into billions or even trillions of tokens. The model learns by guessing the next token in a sequence and adjusting its internal weights when it gets the answer wrong.
The more tokens a model sees during training:
The more context it understands
The more fluent and accurate its responses become
The larger (and more expensive) the model usually is
Models like GPT-4 were likely trained on datasets containing trillions of tokens, requiring thousands of high-performance GPUs (Graphics Processing Units).
What Hardware is Used?
To train on so many tokens, specialized hardware is used:
GPUs (like Nvidia A100 or H100): These are the workhorses for AI training. They can process huge amounts of data in parallel.
TPUs (Tensor Processing Units): Custom chips developed by Google, used for training and inference.
Data Centers: Large cloud providers (AWS, Azure, Google Cloud) host clusters of these GPUs and TPUs, connected by high-speed networks.
Training a large model can cost tens of millions of dollars, much of which is driven by how many tokens the model has to process.
Why Token Limits Matter
Each AI model has a token limit, which represents how much information it can handle in a single input + output cycle.
GPT-3.5 has a token limit of about 4,096 tokens
GPT-4 can handle up to 32,000 tokens
Claude 2.1 can handle 200,000+ tokens
If your input + output exceeds the token limit, the model will cut off the message or reject it.
Tokens and Cost: How They Translate to Money
Tokens aren’t just units of text, they’re also how usage is measured and billed when using AI services.
Most AI providers charge based on how many tokens you use. This includes both the input (your prompt) and the output (the AI's response). The more tokens involved, the more it costs.
For example:
GPT-3.5 might charge ~$0.002 per 1,000 tokens
GPT-4 might charge ~$0.03 to $0.06 per 1,000 tokens, depending on the context length
How it adds up: If your prompt is 500 tokens and the AI replies with 1,000 tokens, you’ve used 1,500 tokens. Multiply that by the per-1,000-token rate to get the cost.
Why it costs money:
Processing tokens uses expensive compute resources (GPUs, electricity, cooling)
Training and maintaining models requires huge data centers and infrastructure
Companies need to cover operating costs and generate revenue
How tokens are paid for:
Via API usage: Developers get billed monthly for token usage
Via subscription plans: Tools like ChatGPT offer subscription tiers that include token-based usage caps
Pay-as-you-go: For enterprise-grade services, you only pay for what you use based on token counts
Understanding token pricing is essential for developers, businesses, and even casual users who want to estimate cost or optimize usage.
Pros and Cons of Tokenization
Pros:
Enables efficient text processing by breaking language into manageable pieces
Reduces memory and computational requirements compared to processing whole words or sentences
Helps AI models generalize better, since they learn patterns across smaller units
Cons:
Tokenization can break words in awkward places (especially in non-English languages)
Token-based limits can cut off longer conversations or documents
Tokenization adds a layer of complexity that humans don’t naturally understand
Are There Alternatives to Token-Based Models?
Yes, but they’re still experimental.
Researchers are exploring:
Character-level models: These treat each character (not token or word) as a unit. They’re more flexible but computationally expensive.
Byte-level models: These go even lower than characters, using raw bytes. They're language-agnostic but require even more training data.
New encoding schemes: Projects like Mistral, Gemini, and others are working on more efficient tokenizers or alternative representations.
Still, token-based models are currently the industry standard, offering a good balance of speed, accuracy, and scalability.
In Summary
Tokens are the currency of communication in the AI world. Every word you type, and every word the AI replies with, gets counted and processed as tokens. Understanding tokens helps demystify how these powerful tools work behind the scenes.
They may be small, but tokens are at the heart of everything modern language models do, and the better we understand them, the better we can use AI responsibly and effectively.