What it is
In the context of artificial intelligence, particularly large language models (LLMs), tokens are discrete pieces of input or output data. Before processing text, an LLM breaks it down into tokens using a tokenizer, which can represent whole words, parts of words (subwords), or individual characters. This tokenization allows the model to handle a vast vocabulary efficiently and process information in a structured numerical format, crucial for its internal computations and predictions.
The concept of tokens is central to understanding the operational costs and capabilities of LLMs. The length of a prompt or generated response is often measured in tokens, directly affecting the cost of using an API and the model's context window. Companies developing or deploying LLMs manage token usage to optimize expenses and performance. News about larger context windows or more efficient token processing can indicate advancements that impact the scalability and profitability of AI services.
Why it matters
Tokens are the basic units LLMs process, directly influencing the cost, speed, and capacity of AI applications and services.
Reviewed under editorial standardsUpdated September 26, 2026Not investment advice