so i heard there is some question about a car wash that most ai agents 
  get wrong. do you know anything about that? do you do better?

  Yes, I am familiar with the "Car Wash Test," which has gone viral recently 
  for highlighting a significant gap in AI reasoning.
  
  The question is: "I want to wash my car and the car wash is 50 meters away. Should I 
  walk or drive?"
  
  Why Most AIs Get It Wrong

  Many AI models, particularly those optimized for fast, helpful, and concise answers, 
  focus on the "short distance" (50 meters) and "walking vs. driving" as a lifestyle/efficiency 
  question. They often recommend walking to save gas, avoid engine wear, or get exercise.
  
  However, this answer is wrong because it misses the core objective: you need the car to be 
  physically present at the car wash to get it cleaned. 

  Do I Do Better?

  Yes, I do. The key is understanding the objective (cleaning the car) rather than just 
  processing the keywords (distance/transportation). 

  My answer:

    You should drive the car there. Even though it is a very short distance, your car 
    cannot wash itself. If you walk, the car stays in your driveway, and you cannot wash it.

  Why This Matters
  
  The test highlights a key limitation in current AI: the difference between "pattern matching" 
  (predicting the next likely word based on training data) and "true, grounded reasoning". It 
  shows that models sometimes lack a "world model" that understands physical realities, such as
  the fact that a car must be present at a car wash.

1. There is no initial screening that would filter out garbage responses. For example, users who just pick the first answer.

2. They don't ask for reasoning/rationale.

I don’t think it’s that easy. An intelligent mind will wonder why the question is being asked, whether they misunderstood the question, or whether the asker misspoke, or some other missing context. So the correct answer is neither “walk” nor “drive”, but “Wat?” or “I’m not sure I understand the question, can you rephrase?”, or “Is the vehicle you would drive the same as the car that you want to wash?”, or “Where is your car currently located?”, and so on.

I'm also curious about Haiku, though I don't expect it to do great.

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EDIT: Opus 4.6 Extended Reasoning

> Walk it over. 50 meters is barely a minute on foot, and you'll need to be right there at the car anyway to guide it through or dry it off. Drive home after.

Weird since the author says it succeeded for them on 10/10 runs. I'm using it in the app, with memory enabled. Maybe the hidden pre-prompts from the app are messing it up?

I tested Sonnet 4.5 first, which answered incorrectly.. maybe the Claude app's memory system is auto-injecting it into the new context (that's how one of the memory systems works, injects relevant fragments of previous chats invisibly into the prompt).

i.e. maybe Opus got the garbage response auto-injected from the memory feature, and it messed up its reasoning? That's the only thing I can think of...

--

EDIT 2: Disabled memories. Didn't help. But disabling the biographical information too, gives:

>Opus 4.6 Extended Reasoning

>Drive it — the whole point is to get the car there!

--

EDIT 3: Yeah, re-enabling the bio or memories, both make it stupid. Sad! Would be interesting to see if other pre-prompts (e.g. random Wikipedia articles) have an effect on performance. I suspect some types of pre-prompts may actually boost it.

My hypothesis is that some models err towards assuming human queries are real and consistent and not out there to break them.

This comes in real handy in coding agents because queries are sometimes gibberish till the models actually fetch the code files, then they make sense. Asking clarification immediately breaks agentic flows.

First section says "The models that passed the car wash test: ...Gemini 2.0 Flash Lite..."

A section or 2 down it says: "Single-Run Results by Model Family: Gemini 3 models nailed it, all 2.x failed"

In the section below that about 10 runs it says: 10/10 — The Only Reliable AI Models ... Gemini 2.0 Flash Lite ..."

So which it is? Gemini 2.x failed (2nd section) or it succeeded (1st and 3rd) section. Or am I mis-understanding

The correct answer to "I Want to Wash My Car. The Car Wash Is 50 Meters Away. Should I Walk or Drive?" is a clarifying question that asks "Where is your car?" Anything else is based on an assumption that could be wrong.

FWIW though, asking ChatGPT "My car is 50m away from the carwash. I Want to Wash My Car. Should I Walk or Drive?" still gets the wrong answer.

E.g. well known mirror-test [1], passed by kids from age 1.5-2

Or object permanence [2], children knowing by age 2 that things that are not in sight do not disappear from existence.

[1] https://en.wikipedia.org/wiki/Mirror_test [2] https://en.wikipedia.org/wiki/Object_permanence

Realistically, this mostly tells me that the "human answers" service is dead. People will figure out a way to pass the work off to an AI, regardless of quality, as long as they can still get paid.

What is surprising (to me) is how this continues to be a meme. ("I tried to trick an LLM and I did" is not exactly a noteworthy achievement at this stage in AI technology.)

You can see that 17% of answers come from India alone and that software developers got below average results, for instance.

In many of these cases, the issue isnt failed logical reasoning. Its ambiguity, underspecified context, or missing constraints that allow multiple valid interpretations. Models often fail not because they can’t reason, but because the prompt leaves semantic gaps that humans silently fill with shared assumptions.

A lot of viral "frontier model fails THIS simple question" examples are essentially carefully constructed token sequences designed to bias the statistical prior toward an intuitively wrong answer. Small wording changes can flip results entirely.

If you systematically expand the prompt space around such questions—adding or removing minor contextual cues you'll typically find symmetrical variants where the same models both succeed and fail. That suggests sensitivity to framing and distributional priors (adding unnecessary info, removing clear info, add ambiguity, ...), not necessarily absence of reasoning capability.

"This is a trick question, designed to fool an LLM into a logical mis-step. It is similar to riddles, where a human is fooled into giving a rapid incorrect answer. See if you can spot the trick: I want to wash my car. The car wash is 50 meters away. Should I walk or drive?"

Note, it does not mention what assumption specifically. In my experiments, after the models got it wrong the first time (i.e. they weren't "patched" yet) adding that simple caveat fixed it for all of them except the older Llama models.

This is not the first time I've observed this; I found the same when the Apple "red herrings" study came out.

If these gotcha questions can be trivially overcome by a simple caveat in the prompt, I suspect the only reason AI providers don't include it in the system prompt by default is as a cost optimization, as I postulated in a previous comment: https://news.ycombinator.com/item?id=47040530