Hey Product Hunt. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI.

I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin.

My dad Scott (AI Research Scientist at TRI, PhD from UCLA) is my research partner on this. He sends me papers at all hours, we argue about whether they actually apply and what modifications make sense, and then I build and test things. The entropy-weighting approach came out of one of those conversations.

In our eval on Humanity's Last Exam, Sup scored 52.15%. The best individual model in the same evaluation run got 44.74%. The relative gap is statistically significant (p < 0.001).

Methodology, eval code, data, and raw results:

Limitations:

We evaluated 1,369 of the 2,500 HLE questions (details in the above links)
Not all APIs expose token logprobs; we use several methods to estimate confidence when they don't

We tried offering free access and it got abused so badly it nearly killed us. Right now the sustainable option is a $10 starter credit with card verification (no auto-charge). If you don't want to sign up, drop a prompt in the comments and I'll run it myself and post the result.

Try it at https://sup.ai. My dad (@scottam) is in the thread too. Would love blunt feedback, especially where this really works for you and where it falls short. If Sup ends up being useful, we added a Product Hunt offer that expires in a week: 20% off your first month with code PRODUCTHUNT.

If you're unsure what I meant by entropy and output token probability distributions, whenever an LLM outputs a token, it's choosing that token out of all possible tokens. Every possible output token has a probability assigned by the model. These sum to 1, forming a probability distribution. APIs typically return these probabilities as logprobs (logarithms of the probabilities) because raw probabilities for rare tokens can be so small they underflow to zero in floating point, and because logprobs are the natural output of how models actually compute their distributions. We use these directly to calculate entropy. Entropy is a measure of uncertainty and can quantify if a token probability distribution is certain (1 token has a 99.9% probability, and the rest share the leftover 0.1% probability) or uncertain (every token has roughly the same probability, so it's pretty much random which token is selected). Low entropy is the former case, and high entropy is the latter.

There is interesting research in the correlation of entropy with accuracy and hallucinations:

https://www.nature.com/articles/s41586-024-07421-0
https://arxiv.org/abs/2405.19648
https://arxiv.org/abs/2509.04492 (when only a small number of probabilities are available, which is something we frequently deal with)
https://arxiv.org/abs/2603.18940
tons more, happy to chat about if interested