Being charged per token feels like being charged per milliliter for a beer.

The word “token” comes from the Old English tacen, meaning “a sign.” Even our ancestors were working with tokens; they simply did not have to pay for them. Seriously, every time we say token, we’re literally talking about a sign that represents meaning.

If you’ve ever tried to understand AI pricing and ended up frustrated, you are not alone. There’s one tiny concept at the center of everything, yet almost nobody defines it properly.

This article answers five questions we hear the most:

1- “Ok… but why tokens?”
2. “So… what even is a token?”
3. “Why can the same text have different token counts in different systems?”
4. “How can you estimate tokens without any tools?”
5. “Should pricing differ for individuals vs companies?”

If you read until the end, you will finally understand the one idea that makes AI pricing look confusing, inconsistent, or mysterious.

1- “Ok… but why tokens?”

When we talk about AI translation or AI models in general, the question we hear most often is: 
 
“Why tokens? Why can’t you just price everything per word, per document?”

The short answer is this:
tokens reflect how much “thinking” the model has to do.
Words do not.

A single German word might explode into 10 pieces inside the model.
A Chinese sentence might compress into 4.
An emoji might secretly become 6 internal components.

Words belong to humans.
Tokens belong to machines.

And machines always bill you in their currency.

2- “So… what even is a token?”

A token is the smallest piece of text an AI model knows how to process.

Imagine breaking a picture into pixels:
Humans see the full image, AI sees the tiny dots.

Now a more technical version (in a single breath):

Before training, each model builds its own subword vocabulary by analyzing billions of sentences and identifying the fragments that best compress real language. This vocabulary typically contains tens of thousands of units and becomes the model’s internal alphabet.
Each item in that list, whether it’s a full word, a space, “ing”, “trans”, or “.” becomes one token.

Your text is broken into those pieces and turned into numbers.
For example:
“Translate this document carefully.”
might become:

[13492, 428, 4001, 17822, 13]
 Five numbers → five tokens.

You don’t see the pieces.
The model does.

Because each model constructs its vocabulary differently, GPT, Claude, and Llama do not tokenize text the same way. The same sentence can become more or fewer tokens depending on the model’s vocabulary and merge rules.

Why this determines cost?
Every token goes through every layer of the model. 
If your text has 50 tokens and the model has 96 layers, each token is processed 96 times.

This is why:
More tokens → more compute → higher cost.

This is also why two texts with the same number of words can have completely different prices.

3- “Why can the same text have different token counts in different systems?”

Each model speaks a different “accent” of tokenization.

GPT: ~50,000-piece vocabulary
Claude: ~100,000
Llama: ~32,000

Here is a calculator for you to compare different models online.

Different vocabularies mean different splits.
So the same sentence might be:
54 tokens in GPT
49 tokens in Claude
62 tokens in Llama

It’s not your text changing.
It’s the model’s perception of your text.

4- “How to estimate token count without any tools?”

Here are the only rules you need:

1. One token ≈ four English characters
A 2,000-character email ≈ 500 tokens.

2. One Word page = 750 to 900 tokens
12pt, single-spaced, normal English text.

3. 100 English words = 130–150 tokens

4. Translation doubles your tokens
One pass for input.
One pass for output.

That’s it.
With these four rules, you can estimate any AI cost.

5- “Should pricing differ for individuals vs companies?”

Yes! When the cost per token becomes significantly lower.

For individuals, token pricing feels unnatural.
People think in words, not machine fragments.

But for companies, tokens are powerful:
They can be forecasted, optimized, and automated.
They create transparency around real compute.

Some subscriptions already use “per word” or “per document.”
But behind the scenes, tokens will remain the real currency.