I agree that Gemini is overly enthusiastic, but at least in my limited testing, 2.5 Pro was also the only model that sometimes does say “no.”

Recently I tested both Claude and Gemini by discussing data modeling questions with them. After a couple of iterations, I asked each model whether a certain hack/workaround would be possible to make some things easier.

Claude’s response: “This is a great idea!”, followed by instructions on how to do it.

Gemini’s response: “While technically possible, you should never do this”, along with several paragraphs explaining why it’s a bad idea.

In that case, the “truth” was probably somewhere in the middle, neither a great idea nor the end of the world.

But in the end, both models are so easily biased by subtle changes in wording or by what they encounter during web searches among other things, that one definitely can’t rely on them to push back on anything that isn’t completely black and white.