How We Count Tokens - Sigrix Learn

How we count tokens

What a token is, how input and output costs add up, and where to read the estimate on any listing before you buy.

Sigrix Team FOUNDATIONS 6 MIN READ

Almost every AI model — OpenAI, Anthropic, Google — bills by the token,

not the word or the request. If you understand tokens, you can predict

what a listing will cost before you ever run it.

§ 01 What is a token?

A token is roughly 4 characters of English text — about ¾ of a

word. The sentence "Sigrix lists AI building blocks." is about 7

tokens. Models read and write in tokens, and providers bill per

million tokens consumed.

Punctuation, spaces, and rare words split into more tokens; common

words are a single token. Code and non-English text are usually

denser, so the same character count costs more.

Every run has two sides. Input is what you send the model: the

prompt, persona, attached skills, and any conversation history.

Output is what the model writes back. Output tokens usually cost

3–5× more than input tokens, so a chatty model can be expensive

even on a short prompt.

> cost = (input tokens × input rate) + (output tokens × output rate)

That's why a one-line prompt that produces a 2,000-word essay can

cost more than a long prompt that returns a single number.

Each of the five building blocks sizes differently. The estimate on a

listing reflects how that type tends to consume tokens:

Listing type	Token profile
Prompt / Persona	Small input (a few hundred tokens). Output depends entirely on the task.
Skill	The SKILL.md file is loaded into context once, then reused at near-zero marginal cost.
Assistant	Multi-turn, so input grows with conversation history. Expect 2–5× a single prompt.
Agent	Multi-step runs accumulate tokens across each step plus tool calls. Heaviest by an order of magnitude.
Product	Bundles all of the above, so the estimate is the sum of its parts.

Open any listing and check the Tokens tab. We show:

Estimates are seller-supplied averages. Actual usage depends on your

inputs, the model you pick, and how long the conversation runs.

When you compare two listings, look at the output estimate first

— it's the bigger lever on cost. A listing with a large prompt but a

tight, structured output is usually cheaper to run than a small

prompt that rambles.

And if a listing doesn't show a token estimate yet, treat that as a

question to ask the seller, not a reason to assume it's free.