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?
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