How many tokens is 1 word?

On average, one English word is about 1.33 tokens. This varies by word length and the specific tokenizer — short common words like "the" or "is" are usually one token, while longer or rarer words get split into multiple tokens.

How many tokens is 1 page?

A single-spaced page of text (about 500 words) is roughly 670 tokens. A double-spaced page (~250 words) is about 335 tokens.

Why do AI companies charge per token?

Tokens provide granular, usage-based billing. Instead of charging flat fees, per-token pricing means you only pay for what you actually use — whether that's a single sentence or an entire novel. It also reflects the real computational cost: processing more text requires more compute.

Are tokens the same across different AI models?

Similar, but not identical. Each model family (GPT, Claude, Gemini) uses its own tokenizer, so the same text can produce token counts that differ by up to 30%. However, the rule of thumb of 1 token ≈ 0.75 English words holds reasonably well across all major models.

What's the difference between input and output tokens?

Input tokens are what you send to the model — your prompt, instructions, and any context. Output tokens are what the model writes back — its response. Output tokens cost 3–6× more than input tokens because generating text is far more computationally expensive than reading it.

What Is a Token in AI? A Simple Explanation

A token is the basic unit AI models use to process text. One token is roughly ¾ of an English word, or about 4 characters. When AI companies charge "per million tokens," they're billing for approximately 750,000 words of text.

How Tokens Work

Before an AI model can read or write anything, it breaks text into small pieces called tokens. Think of it like how a spell-checker processes one word at a time — except tokenizers often split words into smaller chunks. The word "understanding" might become two tokens: "understand" and "ing."

This process is called tokenization. Every piece of text you send to an AI — your prompt, instructions, and any context — gets split into tokens before the model processes it. The model's response gets generated one token at a time, too.

Different AI models use different tokenizers, so the exact same text can produce slightly different token counts depending on the model. GPT, Claude, and Gemini each have their own tokenizer. However, the ratio of roughly 0.75 words per token (or 1.33 tokens per word) holds well across all of them for typical English text.

Numbers, code, and non-English text tend to use more tokens per word. A line of Python code averages about 10 tokens, and languages with longer words or non-Latin scripts can use 2–3× more tokens than English for the same meaning.

What Does 1 Million Tokens Look Like?

A million tokens is the standard billing unit for AI APIs. Here's what that actually looks like in terms you can picture:

1 spine = 1 novel

≈ 8 novels

average novels

A typical novel runs about 90,000 words (~120,000 tokens), so a million tokens holds roughly eight of them.

basis: 90,000 words per novel

1 block = 100 lines of code

≈ 100,000 lines of code

lines of source code

Code averages roughly 10 tokens per line, so a million tokens is about 100,000 lines — nearly twice the original Doom engine (~57,000 lines).

basis: ~10 tokens per line

1 block = 25 messages

≈ 75,000 chat messages

chat messages

A typical text message is about 10 words (~13 tokens). A million tokens is over six years of texting at 30 messages a day.

basis: ~13 tokens per message

1 block = 1 page

≈ 1,500 pages

single-spaced pages

At about 500 words per single-spaced page, 750,000 words fills roughly 1,500 pages — a stack of paper about 15 cm (6 in) tall.

basis: 500 words per page

the 7 books — filled part = 1M tokens

≈ 69% of Harry Potter

of the complete 7-book series

The full Harry Potter series is 1,084,170 words — about 1.45 million tokens. A million tokens gets you past Order of the Phoenix and into the early chapters of Half-Blood Prince.

basis: Series total 1,084,170 words

Every estimate shows its assumption. See all 16 comparisons on the main page →

How Much Do Tokens Cost?

AI companies charge separately for input tokens(what you send) and output tokens (what the model writes back). Output always costs more — typically 3–6× more — because generating text requires far more computation than reading it.

Here's what a few popular models charge per million tokens:

Model	Input / 1M tokens	Output / 1M tokens
Claude Sonnet 5	$2	$10
GPT-5.5	$5	$30
Claude Haiku 4.5	$1	$5
Gemini 3 Flash	$0.50	$3

Prices range from $0.50 to $180 per million tokens — a huge spread. See all model prices →

Token Calculator

Want to estimate how much your specific use case will cost? Use our calculator to pick a model, set a budget, and see exactly how many words, pages, or lines of code that buys.

Open the token calculator →

Frequently Asked Questions

How many tokens is 1 word?: On average, one English word is about 1.33 tokens. This varies by word length and the specific tokenizer — short common words like "the" or "is" are usually one token, while longer or rarer words get split into multiple tokens.
How many tokens is 1 page?: A single-spaced page of text (about 500 words) is roughly 670 tokens. A double-spaced page (~250 words) is about 335 tokens.
Why do AI companies charge per token?: Tokens provide granular, usage-based billing. Instead of charging flat fees, per-token pricing means you only pay for what you actually use — whether that's a single sentence or an entire novel. It also reflects the real computational cost: processing more text requires more compute.
Are tokens the same across different AI models?: Similar, but not identical. Each model family (GPT, Claude, Gemini) uses its own tokenizer, so the same text can produce token counts that differ by up to 30%. However, the rule of thumb of 1 token ≈ 0.75 English words holds reasonably well across all major models.
What's the difference between input and output tokens?: Input tokens are what you send to the model — your prompt, instructions, and any context. Output tokens are what the model writes back — its response. Output tokens cost 3–6× more than input tokens because generating text is far more computationally expensive than reading it.