Context windows

It's a desk, not a memory

The instinct is to imagine the model remembering your conversation the way a person would: things happen, they go into memory, memory is consulted later. That's the wrong picture, and almost every confusing behaviour follows from the wrong picture.

A better one: the model has a desk. Everything it can take into account has to be physically on that desk, laid out flat, at the moment it answers. Not filed nearby. Not retrievable on request. On the desk. The context window is the size of the desk.

Here is the part that surprises people. The model has no memory of your previous messages at all. Each time you hit send, the entire conversation up to that point is re-sent, from scratch, and laid out on the desk again. The model reads the whole thing fresh, answers, and then forgets it completely. The continuity you experience is an illusion maintained by the software around the model, which keeps a transcript and re-sends it every single turn.

So why does it "forget"?

Because the desk fills up. Once the transcript grows past the window, something has to come off the desk to make room. Products handle this in different ways: some silently drop the oldest messages, some compress earlier parts into a summary, some warn you, and some just start behaving worse without saying anything.

That's why the classic failure looks the way it does. You have a long, productive session. You established an important constraint near the beginning. Forty messages later the model violates it, and when you point this out it apologises and agrees, sincerely, because you have just put the constraint back on the desk. It never forgot in the human sense. The instruction had simply been carried off the far edge.

Output shares the desk too

Input and output come out of the same budget. A long document you paste in does not just cost you space, it eats into the room available for the reply. This is why asking for an exhaustive analysis of an enormous file can produce an answer that stops mid-sentence. There's a separate, smaller cap on how long a single reply can be as well, which is the max output column on Model facts.

How big is it, in real terms?

Windows are measured in tokens, which are chunks of text a bit shorter than a word. To make that concrete: Anthropic gives a one-million-token window as roughly 555,000 words, or about 2.5 million characters. That's genuinely enormous, somewhere in the region of five or six long novels.

Current windows sit between a couple of hundred thousand tokens and a million, and that range keeps moving, which is exactly why the numbers live on their own dated page rather than in this paragraph.

Why bigger is not automatically better

A million-token window sounds like it makes this whole concern obsolete. It doesn't, for three reasons.

1. You pay for the whole desk, every turn

Since the entire transcript is re-sent on every message, cost scales with conversation length in a way people find counter-intuitive. Message fifty in a long session costs far more than message one, because you're paying to re-read everything each time. A large window is permission to spend a lot of money quickly, and the single biggest lever against this is caching, which is why it's on the concept list.

2. Attention thins out as it stretches

Having something in the window is not the same as the model weighing it properly. There is a well-documented tendency for material buried in the middle of a very long context to get less effective attention than material at the beginning or the end. Filling a huge window to the brim and assuming everything in it carries equal force is a mistake. Relevance beats volume: ten pages of the right document reliably outperforms three hundred pages containing it.

3. Long contexts are slower

More to read means more time before the first word comes back. On a long session this becomes noticeable, and on anything interactive it matters.

What to actually do about it

Start a new conversation more often than feels natural. People treat a long thread as accumulated value. Frequently it's accumulated noise that is costing you money and diluting attention. When you change tasks, start fresh and paste in just the conclusions that still matter.

Put the important thing near the end. If you have one critical constraint and a pile of background, the constraint goes last, closest to the question. Recency is doing real work here.

Paste the actual material. Describing a document from memory and asking the model to work from your description is strictly worse than putting the document on the desk. This is the highest-return habit change for most people, and it is entirely a context-window insight.

Re-state constraints in long sessions. Not because the model is being difficult, but because you're managing a desk. Restating is cheap; a violated constraint discovered late is not.

Treat "it forgot" as diagnostic. When a long session starts producing worse work, contradicting earlier decisions, or losing the plot, that's your signal, and the fix is almost never a better prompt. It's a fresh session with the relevant context carried over deliberately.

Sources

The 555,000-word figure for a one-million-token window is Anthropic's own, from its models overview, fetched 11 August 2026. The degradation of attention over long contexts is a widely reproduced research finding, and a specific citation should be added here before this page loses its provisional status.

Next → All concepts · Numbers → Model facts