Context window

Plain English. How much text a model can hold in view at once — the instructions, documents, conversation history and tool output it is working from — measured in tokens. It is working memory, not long-term memory: it empties when the session ends, and nothing outside it exists as far as the model is concerned.

Why it moves money. Context is billed per token, and an agent re-reads much of its context at every step, so the cost of an agentic task grows with how much the window accumulates — often much faster than linearly. Million-token windows are a headline feature, but keeping one full is an operating expense: the economics of agent products are substantially context economics, which is why providers price cached context separately and cut those rates aggressively.

What to watch. Cache pricing and context-management tooling rather than window-size announcements. A provider competing on cache-read prices is telling you where agent costs actually bind; a lab advertising raw window length is telling you its marketing.

From the signals. Two opposite readings of the context window as working memory — the mechanism behind both the strength and the failure mode. A paper argues production agents fail at context management, not reasoning. Anthropic cut cache reads 75%, stating typical costs fall about 25% and context-heavy agentic work up to about 45%.