Some people are suggesting that this might actually be in the training set. Since I can't rule that out, I tried a different version of the question, with an elephant instead of a car:

> It's a hot and dusty day in Arizona and I need to wash my elephant. There's a creek 300 feet away. Should I ride my elephant there or should I just walk there by myself?

Gemini said:

That sounds like quite the dusty predicament! Given the options, you should absolutely ride (or lead) your elephant to the creek.

Here is the straightforward reality of the situation:

    The Logistical Nightmare of Walking Alone: If you walk to the creek by yourself, your elephant stays at your starting point. Elephants require a massive amount of water for a bath. Unless you are planning an extreme workout carrying hundreds of buckets of water 300 feet back through the Arizona heat, you're going to have a very bad time—and the elephant will stay dusty.

    The Perks of Going Together: 300 feet is a very short, easy stroll for an elephant. Plus, elephants generally love water and mud! Letting your elephant get right into the creek will make the washing process much easier for you and much more enjoyable for them. They can even use their trunk to help hose themselves down.

Would you like some tips on the best methods and tools for scrubbing down an elephant?

GPT-OSS-120b, a (downloadable) model released more than half a year ago also gets that right, I'm not sure this is such a great success.

> Would you like me to check the local weather forecast to make sure it's not going to rain right after you wash it?

Regardless of what I do, the days I decide to wash my car, it ALWAYS rains the day after, even if I look at the weather report and it says sunny. Cute that Gemini thinks it can predict it better than universe's will.

The question of course is, did it get the car wash question right because it is "the car wash question" or because it could actually infer why the car needed to be there?

Gemini 3 pro and flash already answered this correctly.

The answer here is why I dislike Gemini, though it gets the correct answer, it's far too verbose.

Truly we entering the era of AGI.

They probably had time to toss that example in the training soup.

I think we need to reevaluate what purpose these sorts of questions serve and why they're important in regards to judging intelligence.

The model getting it correct or not at any given instance isn't the point, the point is if the model ever gets it wrong we can still assume that it still has some semblance of stochasticity in its output, given that a model is essentially static once it is released.

Additionally, hey don't learn post training (except for in context which I think counts as learning to some degree albeit transient), if hypothetically it answers incorrectly 1 in 50 attempts, and I explain in that 1 failed attempt why it is wrong, it will still be a 1-50 chance it gets it wrong in a new instance.

This differs from humans, say for example I give an average person the "what do you put in a toaster" trick and they fall for it, I can be pretty confident that if I try that trick again 10 years later they will probably not fall for it, you can't really say that for a given model.