I prefer not to due to privacy concerns. Perhaps you can try yourself?
I will say that after checking, I see that the model is set to "Auto", and as mentioned, used almost 8 minutes. The prompt I used was:
Solve the following problem from a competitive programming contest. Output only the exact code needed to get it to pass on the submission server.
It did a lot of thinking, including
I need to tackle a problem where no web-based help is available. The task involves checking if a given tree can be the result of inserting numbers 1 to n into an empty skew heap, following the described insertion algorithm. I have to figure out the minimal and maximal permutations that produce such a tree.
And I can see that it visited 13 webpages, including icpc, codeforces, geeksforgeeks, github, tehrantimes, arxiv, facebook, stackoverflow, etc.
A terse prompt and expecting a one-shot answer is really not how you'd get an LLM to solve complex problems.
I don't know what Deepmind and OpenAI did in this case, but to get an idea of the kind of scaffolding and prompting strategy that one might want, have a look at this paper where some floks used the normal generally available Gemini Pro 2.5 to solve 5/6 of the 2025 IMO problems: https://arxiv.org/pdf/2507.15855
The point of the GPT-5 model is that it is supposed to route between thinking/nonthinking smartly. Leveraging prompt hacks such as instructing it to "think carefully" to force routing to the thinking model go against OpenAI's claims.
Just select GPT5-thinking if you need anything done with competence. The regular gpt5 is nothing impressive and geared more towards regular daily life chatting.
I prefer not to due to privacy concerns. Perhaps you can try yourself?
I will say that after checking, I see that the model is set to "Auto", and as mentioned, used almost 8 minutes. The prompt I used was:
It did a lot of thinking, including And I can see that it visited 13 webpages, including icpc, codeforces, geeksforgeeks, github, tehrantimes, arxiv, facebook, stackoverflow, etc.A terse prompt and expecting a one-shot answer is really not how you'd get an LLM to solve complex problems.
I don't know what Deepmind and OpenAI did in this case, but to get an idea of the kind of scaffolding and prompting strategy that one might want, have a look at this paper where some floks used the normal generally available Gemini Pro 2.5 to solve 5/6 of the 2025 IMO problems: https://arxiv.org/pdf/2507.15855
The point of the GPT-5 model is that it is supposed to route between thinking/nonthinking smartly. Leveraging prompt hacks such as instructing it to "think carefully" to force routing to the thinking model go against OpenAI's claims.
Just select GPT5-thinking if you need anything done with competence. The regular gpt5 is nothing impressive and geared more towards regular daily life chatting.
Are you sure? I thought you can only specify reasoning_effort and that's it.