The current generation of models all support pretty long context now - the Gemini family has had 1m tokens for over a year, GPT-4.1 is 1m, interestingly GPT-5 is back down to 400,000, Claude 4 is 200,000 but there's a mode of Claude Sonnet 4 that can do 1m as well.

The bigger question is how well they perform - there are needle-in-haystack benchmarks that test that, they're mostly scoring quite highly on those now.

https://cloud.google.com/blog/products/ai-machine-learning/t... talks about that for Gemini 1.5.

Here's a couple of relevant leaderboards: https://huggingface.co/spaces/RMT-team/babilong and https://longbench2.github.io/

sorry I should have been more clear, I meant around open source llms. and I guess the question is, how are closed source llm doing it so well. And if OS OpenNote is the best we have...