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0 pointsUncleOxidant1mo ago0 comments

In the past I've usually found that Gemini (pro, flash) would get stuck on a problem and then seemingly start to do some kind of random search trying this and that just burning through tokens. When this would happen I'd switch (in antigravity) to Claude sonnet 4.6 and it would cut right to the chase and find the problem quickly. But the other day I was out of Claude tokens so I went back to Gemini 3.1 Pro and asked about a verilog simulation problem that Claude had been stuck on - and it figured it out in a few minutes.

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unethical_ban1mo ago· 2 in thread

Pardon my lack of depth on TFA here but in my experience with work, Gemini is far less accurate on queries about technical commands that Claude or OpenAI. Like, I don't trust it at all. Maybe it has its place but not as a general advisor.

seanhunter1mo ago

I think what you’re seeing here is a difference in the amount of “world knowledge “ encoded in the perceptron parts of the model as opposed to how good the model is at the “transformer” part which you could think of as pure token prediction using only what’s in the context window.

If true that would suggest gemini/gemma would be great in a RAG situation where world model isn’t needed as it’s being spoonfed all the relevant information and less good at green field tasks.

That’s interesting to me because I have been struggling to understand how gemma4 is so good in my local use and how notebookLM does such a great job does when I give it project docs and yet gemini has always seemed behind claude when I use it cold for stuff.

k__1mo ago

That matches my experience.

GPT and Claude would work much better than Gemini, even if the direct feedback was sparse or diffuse.

However, the moment I gave Gemini a fast testing framework that gave it instant feedback, it would mill through all kind of problems.

Claude and GPT are seniors.

Gemini is a very motivated mid level.

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