[jmathai]

You may also be getting a worse result for higher cost.

For a medical use case, we tested multiple Anthropic and OpenAI models as well as MedGemma. Pleasantly surprised when the LLM as Judge scored gpt5-mini as the clear winner. I don't think I would have considered using it for the specific use cases - assuming higher reasoning was necessary.

Still waiting on human evaluation to confirm the LLM Judge was correct.

[lorey]

That's interesting. Similarly, we found out that for very simple tasks the older Haiku models are interesting as they're cheaper than the latest Haiku models and often perform equally well.

[andy99]

You obviously know what you’re looking for better than me, but personally I’d want to see a narrative that made sense before accepting that a smaller model somehow just performs better, even if the benchmarks say so. There may be such an explanation, it feels very dicey without one.

[vercaemert]

You just need a robust benchmark. As long as you understand your benchmark, you can trust the results.

We have a hard OCR problem.

It's very easy to make high-confidence benchmarks for OCR problems (just type out the ground truth by hand), so it's easy to trust the benchmark. Think accuracy and token F1. I'm talking about highly complex OCR that requires a heavyweight model.

Scout (Meta), a very small/weak model, is outperforming Gemini Flash. This is highly unexpected and a huge cost savings.

Some problems aren't so easily benchmarked.

[jmathai]

Volume and statistical significance? I'm not sure what kind of narrative I would trust beyond the actual data.

It's the hard part of using LLMs and a mistake I think many people make. The only way to really understand or know is to have repeatable and consistent frameworks to validate your hypothesis (or in my case, have my hypothesis be proved wrong).

You can't get to 100% confidence with LLMs.