What Are Tokens in AI? Simple Explanation with Examples

When you type “Build me an app” into ChatGPT, you probably think the AI is reading those 5 words. It’s not. AI doesn’t even have the concept of words — it reads something else entirely.

AI doesn’t read words. Words don’t exist for it. It reads something else entirely — tokens.

And these tokens decide everything:

• 🧠 How smart your AI is
• 📦 How much it can remember
• 💰 How big your bill is going to be

This is the most fundamental concept in AI engineering. Without understanding tokens, nothing else makes sense.

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What Are Tokens?

Let’s look at it directly. OpenAI has a tokenizer tool — whatever text you type in, it shows you exactly how AI breaks it down.

Example: “Hello, how are you?”

5 words. 6 tokens. Simple so far.

Now try “I am building an application” — depending on the model, “application” might be 1 or 2 tokens. “Artificial Intelligence” — 2 tokens. Easy.

Here’s Where It Gets Interesting

Type the same thing in Hindi: “मुझे एक ऐप बनाना है”

Same meaning. But count the tokens — almost double or triple compared to English.

Bengali? Even more. Japanese? Almost every character becomes its own token.

Why non-English costs more

The tokenizer was trained on English text. English words are in its vocabulary. Hindi, Bengali, Japanese — the tokenizer has to break these down character by character. That’s why non-English languages use significantly more tokens for the same meaning.

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How Was the Tokenizer Built? (BPE)

The algorithm behind tokenization is called BPE — Byte Pair Encoding. Sounds complex, but the concept is simple.

Step 1: Start with individual characters

Every letter is one token. h-e-l-l-o = 5 tokens.

Step 2: Find the most common pair

Look at which 2 characters appear together most often in the training text. For example, t and h appear together constantly — “the”, “that”, “this”, “they”, “with”. So merge th into a single token.

Step 3: Repeat

th and e appear together a lot? Merge them. Now the is one token.

Step 4: Keep going

• ing → one token
• tion → one token
• Hello → one token

This process ran on billions of English text. That’s why common English words = 1 token. But Hindi words? The tokenizer barely saw any Hindi text during training. So it breaks बनाना down character by character.

GPT-4o’s vocabulary

GPT-4o has a vocabulary of ~200,000 tokens — 200,000 common subwords that it learned from training data. Mostly English. This is why English is inherently cheaper and more efficient to process.