What Is a Token in AI? The Key to Cost, Speed, and Accuracy

The growing interest in AI and machine learning has highlighted significant gaps in understanding how these technologies operate. In particular, the term 'token' frequently appears in discussions related to large language models and their performance. What is a token in AI? Why do they matter? How can understanding tokens help me optimize performance and costs? These questions plague many eager to leverage AI for their business or creative goals. In this blog, we’ll break down what a token is in AI. You’ll learn how tokens impact model performance and how to use this knowledge to write better prompts, control costs, and optimize model performance with confidence. But, What is Inference in Machine Learning?

Inference’s AI inference APIs can help you achieve these objectives by allowing you to test your prompts before running them on your model.

What is a Token in AI and Why is It So Important?

If you've ever used an AI service, you've probably seen the term "token." In the simplest terms, a token is a small piece of text. Think of it as a chunk of language, like a word, part of a word, or even just a few characters. For example, the sentence "AI is amazing" can be broken down into tokens as follows: "AI" (1 token), "is" (1 token), and "amazing" (1 token).

AI's Linguistic Building Blocks

For trickier words like "tokenization," the model might break them into smaller chunks (subwords) to make them easier to process. This helps AI handle even the most complex or unusual terms without difficulty. In a nutshell, tokens are the building blocks that let AI understand and generate language in a way that makes sense. Without them, AI would be lost in translation.

The Types of Tokens in AI

Depending on the task, these handy data units can take a whole variety of forms. Here’s a quick tour of the main types:

Word tokens

These are straightforward: each word is its token. For instance, in "AI simplifies life," the tokens are AI, simplifies, and life.

Subword tokens

Sometimes words get fancy, so they’re broken into smaller, meaningful pieces. For example, "unbreakable" might become "unbreakable." This helps AI deal with tricky words.

Character tokens

Each character stands on its own. For "Hello," the tokens are H, e, l, l, and o. This method is particularly suitable for languages or data that lack clear word boundaries.

Punctuation tokens

Even punctuation marks get their moment in the spotlight! In "AI rocks!", the tokens include !, because AI knows punctuation matters.

Image Tokens

Applied in models like DALL·E and Stable Diffusion, where images are broken down into token-like structures for AI-driven art generation.

Audio Tokens

Utilized in AI voice models, where spoken words are converted into tokenized representations for processing and generation. Handling more tokens enhances the capabilities of these models, allowing for more efficient processing and generation of human language by advanced AI systems.

Special tokens

Think of these as AI’s backstage crew. Tokens like (beginning of sequence) or (unknown word) help models structure data and handle the unexpected. Every token type contributes to the system's intelligence and adaptability.

Tokenization in AI: What is It and How Does It Work?

Tokenization in NLP is all about splitting text into smaller parts, known as tokens, whether they’re:

• Words
• Subwords
• Characters

Breaking Down Language

It’s the starting point for teaching AI to grasp human language. Here’s how it goes - when you feed text into a language model like GPT, the system splits it into smaller parts or tokens. Take the sentence "Tokenization is important" - it would be tokenized into “Tokenization,” “is,” and “important.”

AI's Numerical Understanding

These tokens are then converted into numbers (vectors) that AI uses for processing. The magic of tokenization comes from its flexibility. For simple tasks, it can treat every word as its token. But when things get trickier, like with unusual or invented words, it can split them into smaller parts (subwords).

Standardizing Text Interpretation

This way, the AI keeps things running smoothly, even with unfamiliar terms. Modern models, like GPT-4, work with massive vocabularies—around 50,000 tokens. Every piece of input text is tokenized into this predefined vocabulary before being processed. This step is crucial because it enables the AI model to standardize its interpretation and generation of text, ensuring everything flows as smoothly as possible.

By chopping language into smaller pieces, tokenization gives AI everything it needs to handle language tasks with precision and speed. Without it, modern AI wouldn't be able to work its magic.

Why Are Tokens Important in AI?

Tokens are more than just building blocks; they’re what make AI tick. Without them, AI couldn’t process language, understand nuances, or generate meaningful responses. So, let’s break it down and see why tokens are so essential to AI’s success:

Breaking down language for AI

When you type something into an AI model, like a chatbot, it doesn’t just take the whole sentence and run with it. Instead, it chops it up into bite-sized pieces called tokens. These tokens can be whole words, parts of words, or even single characters. Think of it as giving the AI smaller puzzle pieces to work with; it makes it much easier for the model to figure out what you’re trying to say and respond smartly.

For example, if you typed, "Chatbots are helpful," the AI would split it into three tokens:

• Chatbots
• Are
• Helpful

Breaking it down like this helps the AI focus on each part of your sentence, making sure it gets what you're saying and gives a spot-on response.

Understanding context and nuance

Tokens truly shine when advanced models like transformers step in. These models don’t just look at tokens individually; they analyze how the tokens relate to one another. This enables AI to grasp the basic meaning of words, as well as the subtleties and nuances behind them.

Imagine someone saying, "This is just perfect." Are they thrilled, or is it a sarcastic remark about a not-so-perfect situation? Token relationships enable AI to understand these subtleties, allowing it to provide accurate sentiment analysis, translations, or conversational replies.

Data representation through tokens

Once the text is tokenized, each token gets transformed into a numerical representation, also known as a vector, using something called embeddings. Since AI models only understand numbers (so, no room for raw text), this conversion lets them work with language in a way they can process.

These numerical representations capture the meaning of each token, enabling the AI to perform tasks such as:

• Identifying patterns
• Analyzing text
• Generating new content

The AI Translator

Without tokenization, AI would struggle to make sense of the text you type. Tokens serve as the translator, converting language into a form that AI can process, making all its impressive tasks possible.

Tokens’ role in memory and computation

Every AI model has a limit on how many tokens it can handle at once, and this is called the context window. You can think of it like the AI’s attention span - just like how we can only focus on a limited amount at a time. By understanding how tokens work within this window, developers can optimize the AI's processing of information, ensuring it remains accurate and effective.

If the input text becomes too long or complex, the model prioritizes the most critical tokens, ensuring it can still deliver quick and accurate responses. This helps keep the AI running smoothly, even when dealing with large amounts of data.

Optimizing AI models with token granularity

One of the best things about tokens is how flexible they are. Developers can adjust the size of the tokens to accommodate different types of text, providing them with more control over how the AI handles language.

Adaptive Tokenization for Diverse AI Tasks

For example, using word-level tokens is ideal for tasks such as translation or summarization, while breaking down text into smaller subwords enables the AI to understand rare or newly coined words. This adaptability allows for AI models to be fine-tuned for various applications, making them more accurate and efficient in any task they're given.

Enhancing flexibility through tokenized structures

By breaking text into smaller, bite-sized chunks, AI can more easily navigate different languages, writing styles, and even brand-new words. This is especially helpful for multilingual models, as tokenization enables the AI to handle multiple languages without confusion. Even better, tokenization allows the AI to handle unfamiliar words with ease.

If it encounters a new term, it can break it down into smaller parts, allowing the model to make sense of it and adapt quickly. So, whether it’s tackling a tricky phrase or learning something new, tokenization helps AI stay sharp and on track.

Making AI faster and smarter

Tokens are more than just building blocks - how they're processed can make all the difference in how quickly and accurately AI responds. Tokenization breaks down language into digestible pieces, making it easier for AI to understand your input and generate the perfect response. Whether it's conversation or storytelling, efficient tokenization helps AI stay quick and clever.

Cost-effective AI

Tokens play a significant role in maintaining the cost-effectiveness of AI. The number of tokens processed by the model affects how much you pay - more tokens lead to higher costs. By using fewer tokens, you can get faster and more affordable results, but using too many can lead to slower processing and a higher price tag. Developers should be mindful of token use to get great results without blowing their budget.

Why Don’t AI Companies Use Words Instead of Tokens?

Why not just measure usage in words? Wouldn’t that be easier to understand? Here’s why AI companies prefer tokens:

Precision

Tokens allow for more granular measurements. A word can vary in length, but tokens break text into consistent chunks. For example, a single long word like “supercalifragilisticexpialidocious” might be split into multiple tokens, whereas short words like “AI” are just one token. This ensures fair usage tracking.

Multilingual Support

Tokens work better across different languages. Some languages, like Chinese or Japanese, don’t have spaces between words, making it harder to define what counts as a “word.” Tokens standardize text processing regardless of language.

Efficiency

AI models process tokens, not words, under the hood. Using tokens aligns the billing system with how the AI works. This avoids extra complexity and ensures smoother operation.

Complexity of Text

Punctuation, spaces, and special characters also consume computational resources. Tokens account for all of these, providing a more accurate reflection of the AI's workload. While counting words might seem more straightforward, it wouldn’t capture the actual computational effort required, which could lead to unfair or inaccurate pricing.

Related Reading

• Neural Network vs. Machine Learning
• What is a Token in AI

What are the Applications of Tokens in AI?

Tokens are at the heart of how AI systems understand language. They help break down text into digestible pieces so models can analyze what they mean. The results are more accurate AI applications across the board. Let’s look at how tokens work in real-world AI applications, like:

• Text generation
• Semantic search
• Model training

AI-Powered Text Generation Starts With Tokens

Models like GPT or BERT don’t understand language in the same way we do. Instead, they break down text into smaller chunks called tokens that help them make sense of the words. With these tokens, AI can predict what word or phrase comes next, creating everything from simple replies to full-on essays.

The more seamlessly tokens are handled, the more natural and human-like the generated text becomes, whether it’s crafting blog posts, answering questions, or even writing stories.

AI Breaks Language Barriers With Tokens

Ever used Google Translate? Well, that’s tokenization at work. When AI translates text from one language to another, it first breaks it down into tokens. These tokens help the AI understand the meaning behind each word or phrase, making sure the translation isn’t just literal but also contextually accurate.

For example, translating from English to Japanese is more than just swapping words - it’s about capturing the correct meaning. Tokens help AI navigate through these language quirks, so when you get your translation, it sounds natural and makes sense in the new language.

Tokens Help AI Analyze and Classify Feelings in Text

Tokens are also good at reading the emotional pulse of text. With sentiment analysis, AI looks at how text makes us feel - whether it’s a glowing product review, critical feedback, or a neutral remark. By breaking the text down into tokens, AI can figure out if a piece of text is positive, negative, or neutral in tone.

Unlocking Emotional AI with Tokens

This is particularly helpful in marketing or customer service, where understanding how people feel about a product or service can shape future strategies. Tokens enable AI to pick up on subtle emotional cues in language, allowing businesses to act quickly on feedback or emerging trends.

How are Tokens Used During AI Training?

Training an AI model begins with tokenizing the training dataset. Based on the size of the training data, the number of tokens can number in the billions or trillions, and, per the pretraining scaling law, the more tokens used for training, the better the quality of the AI model. As an AI model is pretrained, it’s tested by being shown a sample set of tokens and asked to predict the next token.

Model Training and Convergence

Based on whether or not its prediction is correct, the model updates itself to improve its next guess. This process is repeated until the model learns from its mistakes and reaches a target level of accuracy, known as model convergence.

After pretraining, models are further improved by post-training, where they continue to learn on a subset of tokens relevant to the use case where they’ll be deployed.

Tailoring AI for Specific Tasks

These could be tokens with domain-specific information for an application in law, medicine, or business, or tokens that help tailor the model to a specific task, like reasoning, chat, or translation. The goal is to create a model that generates the correct tokens to deliver a response based on a user’s query, a skill commonly referred to as inference.

How are Tokens Used During AI Inference and Reasoning?

During inference, an AI receives a prompt—which, depending on the model, may be text, an image, an audio clip, a video, sensor data, or even a gene sequence—that it translates into a series of tokens. The model processes these input tokens, generates its response as tokens, and then translates it to the user’s expected format.

Multilingual and Multimodal AI Understanding

Input and output languages can differ, as seen in models that translate English to Japanese or convert text prompts into images. To understand a complete prompt, AI models must be able to process multiple tokens at once. Many models have a specified limit, referred to as a context window, and different use cases require different context window sizes.

A model that processes a few thousand tokens can handle a high-resolution image or several pages of text, while those with tens of thousands of tokens can summarize entire novels or long podcasts. Some advanced models offer million-token context lengths for analyzing massive data sources, and new reasoning AI models can tackle even more complex queries.

Reasoning Tokens and "Long Thinking" AI

These reasoning tokens enable more effective responses to complex questions, much like a person can formulate a better answer when given time to work through a problem. The corresponding increase in tokens per prompt can require over 100 times more compute compared with a single inference pass on a traditional LLM, an example of test-time scaling, also known as long thinking.

How Do Tokens Drive AI Economics?

During pretraining and post-training, tokens represent an investment in intelligence, and during inference, they drive both cost and revenue. So, as AI applications proliferate, new principles of AI economics are emerging.

AI factories are designed to support high-volume inference, enabling the creation of manufacturing intelligence for users by transforming tokens into monetizable insights.

Token-Based AI Pricing

That’s why a growing number of AI services are measuring the value of their products based on the number of tokens consumed and generated, offering pricing plans based on a model’s rates of token input and output. Some token pricing plans offer users a set number of tokens shared between input and output.

Flexible Token Usage

Based on these token limits, a customer could use a short text prompt that uses just a few tokens for the input to generate a lengthy, AI-generated response that took thousands of tokens as the output. Or a user could spend the majority of their tokens on input, providing an AI model with a set of documents to summarize into a few bullet points.

To serve a high volume of concurrent users, some AI services also set token limits, which are the maximum number of tokens per minute that can be generated for an individual user.

Tokens and AI User Experience

Tokens also define the user experience for AI services. The time to first token, the latency between a user submitting a prompt and the AI model starting to respond, and inter-token latency—the rate at which subsequent output tokens are generated—determine how an end user experiences the output of an AI application.

Balancing Latency for Optimal Interaction

There are tradeoffs for each metric, and the right balance depends on the use case. For LLM-based chatbots, shortening the time to first token improves user engagement by maintaining a conversational pace without unnatural pauses. Optimizing inter-token latency enables text models to match average reading speed or video models to achieve a desired frame rate.

Quality vs. Speed in AI Output

For AI models engaging in long thinking and research, more emphasis is placed on generating high-quality tokens, even if it adds latency. Developers must strike a balance between these metrics to deliver high-quality user experiences with optimal throughput, which is the number of tokens an AI factory can generate.

Related Reading

• What is Quantization in Machine Learning
• Batch Learning vs. Online Learning
• Feature Scaling in Machine Learning

Complexity in Tokenization and Strategies to Optimize AI Token Usage

Breaking down language into neat tokens might seem easy, yet there are several challenges that tokenization has to overcome. Let’s take a closer look at the bumps along the way.

Ambiguous Words in Language

Language loves to throw curveballs, and sometimes, it’s downright confusing. Take the word "run," for instance; does it mean going for a jog, operating a software program, or managing a business?

For tokenization, these kinds of words create a puzzle. The tokenizers must determine the context and split the word in a way that makes sense. Without seeing the bigger picture, the tokenizer might miss the mark and create confusion.

Polysemy and the Power of Context

Some words act like chameleons; they change their meaning depending on how they’re used. Think of the word "bank." Is it a place where you keep your money, or is it the edge of a river? Tokenizers must be vigilant, interpreting words based on their surrounding context. Otherwise, they risk misunderstanding the meaning, which can lead to some hilarious misinterpretations.

Understanding Contractions and Combos

Contractions like "can’t" and "won’t" can trip up tokenizers. These words combine multiple elements, and breaking them into smaller pieces might lead to confusion. Imagine trying to separate "don’t" into "do" and "n’t" - the meaning would be lost entirely. To maintain the smooth flow of a sentence, tokenizers need to be cautious with these word combos.

Recognizing People, Places, and Things

Whether it’s a person’s name or a location, they’re treated as single units in language. But if the tokenizer breaks up a name like “Niagara Falls” or “Stephen King” into separate tokens, the meaning goes out the window. Getting these right is crucial for AI tasks, like recognizing specific entities, since misinterpretation could lead to embarrassing errors.

Tackling Out-of-Vocabulary Words

What happens when a word is new to the tokenizer? Whether it’s a jargon term from a specific field or a brand-new slang word, if it’s not in the tokenizer’s vocabulary, it can be tough to process. The AI might stumble over rare words or miss their meaning entirely. It’s like trying to read a book in a language you’ve never seen before.

Dealing with Punctuation and Special Characters

Punctuation isn’t always as straightforward as we think. A single comma can completely change the meaning of a sentence. For instance, compare "Let’s eat, grandma" with "Let’s eat grandma." The first invites grandma to join a meal, while the second sounds alarmingly like a call for cannibalism.

The Punctuation Predicament in Tokenization

Some languages also use punctuation marks in unique ways, adding another layer of complexity. So, when tokenizers break text into tokens, they need to decide whether punctuation is part of a token or acts as a separator. Get it wrong, and the meaning can take a confusing turn, especially in cases where context heavily depends on these tiny but crucial symbols.

Handling a Multilingual World

Things become even more complex when tokenization must handle multiple languages, each with its unique structure and rules. Take Japanese, for example; tokenizing it is a whole different ball game compared to English. Tokenizers have to work overtime to make sense of these languages, so creating a tool that works across many of them means understanding the unique quirks of each one.

Tokenizing at a Subword Level

Thanks to subword tokenization, AI can tackle rare and unseen words like a pro. Nevertheless, it can also be somewhat challenging. Breaking down words into smaller parts increases the number of tokens to process, which can slow down the process. Imagine turning “unicorns” into “uni,” “corn,” and “s.”

Suddenly, a magical creature sounds like a farming term. Finding the sweet spot between efficiency and meaning is a real challenge here - too much breaking apart, and it might lose the context.

Tackling Noise and Errors

Typos, abbreviations, emojis, and special characters can confuse tokenizers. While it’s great to have tons of data, cleaning it up before tokenization is a must. But here’s the thing: no matter how thorough the cleanup, some noise just won’t go away, making tokenization feel like solving a puzzle with missing pieces.

The Trouble With Token Length Limitations

Now, let’s talk about token length. AI models have a max token limit, which means if the text is too long, it might get cut off or split in ways that mess with the meaning. This is especially challenging for long, complex sentences that require a thorough understanding. If the tokenizer isn't careful, it could miss some vital context, and that might make the AI's response feel a little off.

Strategies to Optimize AI Token Usage

Learn prompt engineering

Your “ask” from the AI should be concise and focused. Use as few words as possible to conserve tokens and get the best possible result. Large text blocks may introduce noise into the AI output and consume a significant number of tokens.

Don’t summarize previous conversations

Within the context of a chat, the AI already knows what you are talking about. Avoiding summarizing earlier parts of a conversation reduces time spent and tokens consumed, ensuring efficient communication.

Be concise and precise

Short prompts will not only result in a more beneficial number of tokens but also allow you to achieve more satisfactory answers. Therefore, think carefully about the construction of your prompts and decide how much context will be enough to achieve the best effects. Additionally, the fewer tokens you use in your input, the more will be left to generate output.

Pay attention to the language that you use

The grammar of different languages can differ fundamentally. Whether you decide to create prompts in English, German, or Polish will significantly affect how the system counts tokens. That's why you need to be aware of these differences and choose the option that will benefit you in terms of costs and suit your use case.

Also, remember that there are some unique tokens that ChatGPT doesn't include in the cost, for example, "<|endoftext|>" that signals the end of the text or a given fragment, but there are also some like "\n" that are subject to standard counting. What does this all mean? Namely, it's worth experimenting with tokens and looking for helpful advice online.

Choose the appropriate language model

The cost of tokens depends on the language model you want to use. OpenAI has already released a few versions of ChatGPT:

• ChatGPT Legacy
• ChatGPT-3.5 Turbo
• ChatGPT-4
• ChatGPT-4 Turbo

Quality vs. Cost

The choice of the appropriate model depends on the goals you try to achieve. If, for any reason, you don't need the bot responses to be of very high quality, then older versions of the language model may be enough for you. These models differ in the quality of generated content and the price you need to pay for one token.

Predicting Chatbot Costs

It's also worth knowing about tools that will help you predict the cost of chatbot use. For ChatGPT, it's the Tokenizer mentioned above and a handy Python library called Tiktoken. Thanks to these tools, you can estimate the cost of your input and decide whether a given conversation should be split into a few segments.

Optimizing Output for Token Efficiency

Request multiple outputs: With an efficiently worded prompt, you can request various outputs with one prompt. This consumes fewer output tokens.

Request more efficient output formats: An AI may often respond in paragraphs. But if you request short bullets or tables, you are likely to get a more efficient answer.

Related Reading

• LLM Embeddings
• Domain Adaptation

Start Building with $10 in Free API Credits Today!

Inference refers to the process of taking a trained AI model and applying it to new data to generate predictions or insights. OpenAI's Inference API offers serverless inference for large language models (LLMs), allowing developers to concentrate on building applications rather than managing infrastructure.

The Inference API is compatible with leading open-source LLMs like those in the GPT family.