I'm still using https://lmarena.ai/leaderboard. Perhaps there is something better and someone will pipe up to tell me about it. But we use LLMs at work and have unexplainable variations between them.
And when we get a prompt working reliably on one model, we often have trouble porting it to another LLM - even straight "version upgrades" such as from GPT-4 to -5. Your prompt and your model become highly coupled quite easily.
I dunno what to do about it and am tending to just pick Gemini as a result.
Product testing (with traditional A/B tests) are kind of the best bet since you can measure what you care about _directly_ and at scale.
I would say there is of course “benchmarketing” but generally people do sincerely want to make good benchmarks it’s just hard or impossible. For many of these problems we’re hitting capabilities where we don’t even have a decent paradigm to use,
Human raters are exploitable, and you never know whether the B has a genuine performance advantage over A, or just found a meat exploit by an accident.
It's what fucked OpenAI over with 4o, and fucked over many other labs in more subtle ways.
Ultimately we are measuring extremely measurable things that have an objective ground truth. And yet:
- we completely fail at statistics (the MAJORITY of analysis is literally just "here's the delta in the mean of these two samples". If I ever do see people gesturing at actual proper analysis, if prompted they'll always admit "yeah, well, we do come up with a p-value or a confidence interval, but we're pretty sure the way we calculate it is bullshit")
- the benchmarks are almost never predictive of the performance of real world workloads anyway
- we can obviously always just experiment in prod but then the noise levels are so high that you can entirely miss million-dollar losses. And by the time you get prod data you've already invested at best several engineer-weeks of effort.
AND this is a field where the economic incentives for accurate predictions are enormous.
In AI, you are measuring weird and fuzzy stuff, and you kinda have an incentive to just measure some noise that looks good for your stock price anyway. AND then there's contamination.
Looking at it this way, it would be very surprising if the world of LLM benchmarks was anything but a complete and utter shitshow!
Even professional human evaluators are quite vulnerable to sycophancy and overconfident-and-wrong answers. And LMArena evaluators aren't professionals.
A lot of the sycophancy mess that seeps from this generation of LLM stems from reckless tuning based on human feedback. Tuning for good LMArena performance has similar effects - and not at all by a coincidence.
What changed to make "the inevitable AI bubble" the dominant narrative in last week or so?
When models figure out how to exploit an effect that every clever college student does, that should count as a win. That’s a much more human-like reasoning ability, than the ability to multiply large numbers or whatever (computers were already good at that, to the point that it has become a useless skill for humans to have). The point of these LLMs is to do things that computers were bad at.
However:
> Testing only on these problems would not predict performance on larger numbers, where LLMs struggle.
Since performance on large numbers is not what these exams are intended to test for, I don’t see this as a counterargument, unless the benchmarks are misrepresenting what is being tested for.
Personally, I’d say that LLM tools are more akin to a human using a calculator.
The way they’re being deployed it feels like the point of LLMs is largely to replace basic online search or to run your online customer support cheaply.
I’m a bit out on a limb here because this is not really my technical expertise by any stretch of the imagination, but it seems to me these benchmark tests don’t really tell us much about how LLM’s perform in the ways most people actually use them. Maybe I’m off base here though
AI (and humans!) aside, claiming that there was an oracle that could "answer all questions" is a solved problem. Such a thing cannot exist.
But this is going already too deep IMO.
When people start talking about percentages or benchmark scores, there has to be some denominator.
And there can be no bias-free such denominator for
- trivia questions
- mathematical questions (oh, maybe I'm wrong here, intuitively I'd say it's impossible for various reasons: varying "hardness", undecidable problems etc)
- historical or policital questions
I wanted to include "software development tasks", but it would be a distraction. Maybe there will be a good benchmark for this, I'm aware there are plenty already. Maybe AI will be capable to be a better software developer than me in some capacity, so I don't want to include this part here. That also maps pretty well to "the better the problem description, the better the output", which doesn't seem to work so neatly with the other categories of tasks and questions.
Even if the whole body of questions/tasks/prompts would be very constrained and cover only a single domain, it seems impossible to guarantee that such benchmark is "bias-free" (I know AGI folks love this word).
Maybe in some interesting special cases? For example, very constrained and clearly defined classes of questions, at which point, the "language" part of LLMs seems to become less important and more of a distraction. Sure, AI is not just LLMs, and LLMs are not just assistants, and Neural Networks are not just LLMs...
There the problem begins to be honest: I don't even know how to align the "benchmark" claims with the kind of AI they are examinin and the ones I know exist.
Sure it's possible to benchmark how well an AI decides whether, for example, a picture shows a rabbit. Even then: for some pictures, it's gotta be undecidable, no matter how good the training data is?
I'm just a complete layman and commenting about this; I'm not even fluent in the absolute basics of artificial neural networks like perceptrons, gradient descent, backpropagation and typical non-LLM CNNs that are used today, GANs etc.
I am and was impressed by AI and deep learning, but to this day I am thorougly disappointed by the hubris of snakeoil salespeople who think it's valuable and meaningful to "benchmark" machines on "general reasoning".
I mean, it's already a thing in humans. There are IQ tests for the non-trivia parts. And even these have plenty of discussion revolving around them, for good reason.
Is there some "AI benchmark" that exclusively focuses on doing recent IQ tests on models, preferably editions that were published after the particular knowledge cutoff of the respective models? I found (for example) this study [1], but to be honest, I'm not the kind of person who is able to get the core insights presented in such a paper by skimming through it.
Because I think there are impressive results, it's just becomimg very hard to see through the bullshit at as an average person.
I would also love to understand mroe about the current state of the research on the "LLMs as compression" topic [2][3].
[1] https://arxiv.org/pdf/2507.20208
Marshferm•2h ago