The Token Economy of AI
Understanding how GenAI models process language into tokens.
This tool was built with Gemini 2.5 on Oct 13th, 2025.
What is a Token?
Tokens are the fundamental building blocks of communication for Large Language Models (LLMs). Before a model can "read" or "write" text, it converts human language into a sequence of numbers (tokens) using a **Tokenizer**.
- Sub-Word Units: Tokens are usually *not* whole words, but pieces of words (sub-words).
- Efficiency: This sub-word strategy allows the AI to handle rare words and proper nouns without needing an impossibly large vocabulary.
- Cost & Speed: LLM usage and processing speed are directly measured by the number of tokens in the input and output.
Live Tokenization Demo
Token Output:
Total Tokens: 0
Tokenizing Sub-Words
Tokens often break words in ways that have nothing to do with human syllables. This maximizes vocabulary coverage.
Original Word: unbelievably
AI Token Split (Simulated):
Tokens are 3, not 1, allowing the model to reuse the un- and -vably components.
Numbers are Text, Not Math
Large numbers are split into multiple tokens, losing their numerical value. The AI "reads" digits as characters, not a single quantity.
Original Number: 1,234,567,890
AI Token Split (Simulated):
A simple number becomes X tokens. This is why LLMs often struggle with precise arithmetic.