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Comment by andy99 | Hacker News Reader

Depends on what you’re doing. Using the smaller / cheaper LLMs will generally make it way more fragile. The article appears to focus on creating a benchmark dataset with real examples. For lots of applications, especially if you’re worried about people messing with it, about weird behavior on edge cases, about stability, you’d have to do a bunch of robustness testing as well, and bigger models will be better.

Another big problem is it’s hard to set objectives is many cases, and for example maybe your customer service chat still passes but comes across worse for a smaller model.

Id be careful is all.

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One point in favor of smaller/self-hosted LLMs: more consistent performance, and you control your upgrade cadence, not the model providers.

I'd push everyone to self-host models (even if it's on a shared compute arrangement), as no enterprise I've worked with is prepared for the churn of keeping up with the hosted model release/deprecation cadence.

How much you value control is one part of the optimization problem. Obviously self hosting gives you more but it costs more, and re evals, I trust GPT, Gemini, and Claude a lot more than some smaller thing I self host, and would end up wanting to do way more evals if I self hosted a smaller model.

(Potentially interesting aside: I’d say I trust new GLM models similarly to the big 3, but they’re too big for most people to self host)

Where can I find information on self-hosting models success stories? All of it seems like throwing tens of thousands away on compute for it to work worse than the standard providers. The self-hosted models seem to get out of date, too. Or there ends up being good reasons (improved performance) to replace them

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.

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.

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.

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.

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.

You're right. We did a few use cases and I have to admit that while customer service is easiest to explain, its where I'd also not choose the cheapest model for said reasons.

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