One Million Tokens, visualized
www.1millioncontext.com
A visual essay on machine memory: one million WebGL tokens and the 2020-2026 race from 2,048-token context windows to ten million.

I found One Million Tokens. Its rough scale:

1M tokens
~ 750K words
~ 3,000 pages
~ 83 hours of conversation
~ 75K lines of code

🧠 The more interesting part is the timeline.

It starts with GPT-3 at 2,048 tokens in 2020, then walks through ChatGPT 4K, GPT-4 32K, Claude 100K, Gemini 1M, and the later multi-million-token era.

The visual change is kind of absurd when you see all the pages stacked together.

One caveat: maximum context is not the same as perfect memory/retrieval. A model accepting 1M tokens can still fail to use information buried inside that window.

The line-of-code/page conversions are approximations too. I am curious how people here design around 1M+ context in practice.

Do you actually feed giant source sets directly, or still prefer retrieval + smaller focused contexts for cost/attention reasons?