13d • AI
What Are Tokens?
1. What Exactly Is a Token?
Before diving into costs, you need a baseline. An AI model doesn't read words the way humans do; it reads "tokens."
- A token is roughly ¾ of a typical English word.
- The word "apple" is one token.
- The word "unbelievable" might be broken into two or three tokens (un, believ, able).
Every time you send data to an AI model and receive a response back, a meter is running based on how many tokens were processed. But not all tokens are billed equally.
2. The Core Difference: Reading vs. Writing
The biggest trap for developers and automation builders is treating all tokens the same. They function completely differently under the hood:
Input Tokens (The Context)
Input tokens are the data you feed into the model. This includes your system prompts, the PDFs you upload, the Zapier triggers you pass through, and the user's actual question.
• How it works: AI models "read" input tokens in parallel. They can process a massive chunk of text almost instantly.
- The Cost: Because reading context is highly efficient for the hardware, input tokens are incredibly cheap.
Output Tokens (The Generation)
Output tokens are the words the AI generates and sends back to you.
- How it works: AI cannot write in parallel. It has to generate text sequentially, predicting the next token, then the next, one by one. This is computationally heavy and causes latency (lag) in your workflows.
- The Cost: Because of the heavy lifting required to write text step-by-step, output tokens typically cost 3x to 5x MORE than input tokens.
3. Visualize the Pricing Disconnect
To see how quickly this scales when running thousands of automated tasks a month, you can model out the cost difference between reading heavy context and generating heavy outputs.
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What Are Tokens?
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