alt Hacker NewsThis has happened to consistently across OpenAI and Anthropic models that I can't believe people are still denying it. Obviously they're silently quantising their models or making other efficiency changes. If they can save 40% RAM and only hurt intelligence by a few percent in their benchmarks, they're going to do that. Unfortunately their benchmarks are obviously not representative of the real world, and these downgrades are very noticeable for people who rely on these models for work.
Of all the reasons to look at local inference, this is the one which pushes me in that direction the most. I want to rely on a model for consistency in my workflows. If I wake up on Monday and suddenly it's refactoring things it built on Friday, that's a huge problem. That makes it unreliable.
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I guess the question really is: is this malice (trying to trick people into thinking new model is better by nerfing old one), or is this capacity constraints, where they obviously need to make room in their infra for inference of the new model by shoving the other models into a smaller box?
These machines are inherently unreliable.
People are denying it because it's simply not true and for sure has not happened consistently.
In my own work I have not once noticed a degradation where I could have said with any certainty that it's not within usual deviation or me having a bad day.
Conspicuously the model seems to be especially dumb and annoying when I did not get a lot of sleep the previous night.
When OpenAI and Anthropic engineers post here on HN that they do not quantize models and silently replace checkpoints in the API, I believe them.
I saw nothing that convinced me the people who are constantly making these complaints don't just mistake any suboptimal result for changes in the model.
> I can't believe people are still denying it
Probably because those "nerfs" do not show on sites that track model performance over time. Sometimes Anthropic/OpenAI mess stuff up in their harness, but usually not in the models themselves.