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What's there not to understand?

If it matches the latest GPT O-N model in performance - or is just close, even, at a fraction of the compute (50x less?) and it is free, then that's huge news.

They just upended the current LLM/AI/ML dominance, or at least the perceived dominance. Billions and billions have been pumped into the race, where investors are betting on the winner - and here comes a Chinese hedge fund side-project on shoestring budget, matching those billion dollar behemoths. And they'll continue to release their work.

They just made the OpenAI et. al. secret sauce a lot less valuable.



New theory: this is a short-long play by the fund. They shorted NV, now they're hoovering up stock. In the process of making their billions from a small 50$mm investment!


Bullseye


Damn Matt Levine to hell! His latest newsletter has an entire section devoted to the entire topic!


In my tests it does not come close to O1-Pro. Still huge news but it does not quite make it.


The results i did get from deepseek-r1 on their webpage did not match the results i did get from o1-pro. I did ask it go to a github repo, find the part where the logic of the “export” button is and explain why it doesn’t work (the whole logic is actually missing, won’t work at all). O1 pro did get it right in the first try while deepseek r1 was heavily hallucinating. Maybe i am using the wrong model?


No, you’re not. They explicitly mention in the R1 paper (in the last paragraph before the bibliography) that R1 isn’t a “huge” improvement over DeepSeek-V3 in coding - where “huge” is an academic weasel word.

It’s just a lot of hype. In my coding tests it significantly underperforms o1 (haven’t tried o1-pro), often getting stuck in a reasoning loop because I underspecified something (that I don’t have to with o1).


Same anecdotal experience. Its definitely an improvement and they have made operational improvements at runtime but I am still concerned they are have over fit for the tests.




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