So... it failed to solve the puzzle? That seems distinctly unimpressive, especially for a puzzle with a fixed start state and a limited set of possible moves.
I cannot understate how impressive this is to me, having been involved in ai research projects and robotics in years gone by.
This is a general purpose model, given an image and human written request that then step by step analyses the image, iterates through various options, tries to write code to solve the problem and then searches the internet for help. It reads multiple results and finds an answer, checks to validate it and then comes back to the user.
I had a robot that took ages to learn to plan tic tac toe by example and if the robot moved originally there was a solid chance it thought the entire world had changed and would freak out because it thought it might punch through the table.
This is also a chess puzzle marked as very hard that a person who is good at chess should give themselves fifteen minutes to solve. The author of the chess.com blog containing this puzzle only solved about half of them!
This is not an image analysis bot, it's not a chess bot, it's a general system I can throw bad english at.
I am human and I solved this before opening the blog post, because I've seen this problem 100 times before with this exact description. I don't understand why an LLM wouldn't have done the same, because pattern matching off things you saw on the internet is IIUC the main way LLMs work.
(I am good at chess, but not world class. This is not a difficult mate in 2 problem: if I hadn't seen it, it would take a minute or so to solve, some composed 2-movers might take me 5 minutes).
The obvious moves dont work, you can see whites pawn moving forward is mate, and you can see black is essentially trapped and has very limited moves, so immediately I thought first move is a waiting move and theres only two options there. Block the black pawn moving and if bishop moves, rook takes is mate. So rook has to block, and you can see bishop either moves or captures and pawn moving forward is mate
https://www.chess.com/blog/ThePawnSlayer/checkmate-in-two-pu...
Although perhaps this is missing the point - the process and chain here in response to an image and a sentence is extremely impressive. You can argue it's not useful, or not useful for specific use cases but it's impressive.
If you just paste the image into a search engine (without needing to include the text prompt) the first result contains the solution. We live in a world where Sam Altman claims that usage of words like "please" and "thank you" in prompts have cost OpenAI "tens of millions of dollars"[0]. In this case, OpenAI's "most powerful reasoning model"[1] spends 7m 51s churning through expensive output tokens spinning its wheels before ultimately giving up and searching the internet. This strikes me as incredibly wasteful. It feels like the LLM equivalent of "punch[ing] through the table". The most impressive thing to me here is that OpenAI is getting people to pay for all this nonsense.
[0] https://www.usatoday.com/story/tech/2025/04/22/please-thank-...
Is it, though? I play at around 1000 Elo – I have a long-standing interest in chess, but my brain invariably turns on fog of war that makes me not notice threats to my queen or something – and I solved it in something like one minute. It has very little moving parts, so the solution, while beautifully unobvious, can be easily brute-forced by a human.
I haven't played chess in decades and was never any good at it. I'm basically now at the level that I know most of the basic rules of the game. And it took me maybe 5 minutes.
Clever Hans at web-scale, so to say.
So if you're impressed by a model that spent 10 minutes and single-digit dollars to not solve a problem that has been solved before, then I guess their model is working exactly as expected.
"Well, it's not a chess engine so its impressive it-" No. Stop. At best what we have here is an extremely computationally expensive way to just google a problem. We've been googling things since I was literally a child. We've had voice search with google for, idk, a decade+. A computer that can't even solve its own chess problems is an expensive regression.
from the article:
"3. Attempt to Use Python When pure reasoning was not enough, o3 tried programming its way out of the situation.
“I should probably check using something like a chess engine to confirm.” (tries to import chess module, but fails: “ModuleNotFoundError”).
It wanted to run a simulation, but of course, it had no real chess engine installed."
this strategy failed, but if OpenAI were to add "pip install python-chess" to the environment, it very well might have worked. in any case, the machine did exactly the thing you claim it should have done.
possibly scrolling down to read the full article makes you a rube though.
This is a bad thing because it means they gave up on solving actual problems and entered the snake oil business.
Suppose we removed its ability to google and it conceded to doing the tedium of writing a chess engine to simulate the steps. Is that “better” for you?