Data at https://gertlabs.com/rankings
I think it's interesting, I think other people would find it useful, but I don't want to spend a bunch of money running it against all the frontier models.
What's the best way to reach out to labs like yours to collaborate on something like that? Are there any labs that are more open to submissions from internet randos?
I agree that Opus 5 is not a great model, despite being clearly intelligent. It seems like a personality problem in user-driven agentic coding workflows, not a real capability issue. Not incorporating unspoken user intent, going off topic, incorporating some of the pedantry you find in GPT 5.x models, etc.
That's also likely why Opus 5 ranks low on our "Social Intelligence" benchmark (https://gertlabs.com/rankings?mode=decision), although sample sizes on this one are still low.
So just adding a language or tag filter can result in some pretty small sample sizes. You can see how many samples survived in the box plot, but that's probably bad UX that most people never see. There's a reason no other benchmark provides this type of data (even for our sample sizes it runs almost 10K USD/month to keep up to date with new releases).
Might be a good idea to reduce the ability to apply filters into a cohort with less than ~20 samples -- not the first time we've gotten that feedback. Seems like adding too many options to see individual sample variation is just misdirecting. I'm a nerd who loves data so I hate removing access, especially since the aggregate performance is very interesting (averaged across all languages, we see consistent and interesting performance data across models, like models outperforming with strongly typed languages). But tbh I think you're right and we'll try limiting filters to where we actually have statistically significant data.
Oh! I got my name on a paper! I don't think there is much reward for it these days.
Benchmarks are handy when they’re new, novel, and constantly changing. The second you let even a single aspect of it stagnate, it becomes a gameable score rather than a useful metric. In PC Gaming, we saw vendors optimize for specific titles, benchmark tools, and scenarios at the expense of general performance, and eventually the industry had a “come to Jesus” moment where we had to collectively decide how to move forward from an industry built on thoroughly gamed benchmarks, with entities like Gamers’ Nexus and Digital Foundry being the end results of that falling out.
LLMs were always going to end up the same way, because the people building the benchmarks - well-intentioned as they were - ultimately fell into the exact same traps with fixed scoring rubrics, known test questions, and believing in some form of “completeness” that could be attained or achieved. The net result are models consistently scoring better on benchmarks but also seeing diminishing returns and rising vulnerabilities, because actual improvement or utility isn’t what they’re being optimized for so much as bragging rights. It’s why there’s so much growing interest in things like MoE execution on unified memory platforms as a means of porting larger models to consumer kit, or ternary models (shoutout to Bonsai) as a means of reducing overall size: both take leading edge, benchmark-saturating models and show that with minimal score loss, they function about as well as frontier models might.
Building a new benchmark won’t solve the problem, either. To move forward, we must evaluate LLMs objectively and with continuously evolving workloads. More “pelican on a bicycle” stuff, but from varying perspectives and use cases. Radiologists putting models through their paces with usable sample data they don’t share with AI labs, or IT folks tasking agents with bootstrapping specific, real-world workloads. To prove general intelligence, we need more specialists evaluating them specifically and generally in ways that are transparent to consumers but difficult or impossible for AI companies to prepare against.
Only then will scoring values matter.
It will if the benchmark is proprietary. If you can't train on it, then it's extremely difficult to game, and if it's hard enough, then it's economically more efficient to just...make the model smarter
This results in a lot of "oh wow it can do math I dont care about" and "it can't code a lot, but not well" outcomes instead of the core needs:
1) Cheaper faster and real time 2) Long walk capable without losing attention while rescoring goals over updated enviroment 3) Specific domain knowledge that can be trained quickly into the model (how we do work in this specific case)
> We find that nearly half of the our bench- marks exhibit saturation
If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence.
I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm
“The seeker after truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration and not the sayings of human beings whose nature is fraught with all kinds of imperfection and deficiency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of of its content, attack it from every side. he should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.” - ibn al-Haytham
This is an amazingly ignorant thing to say given the current pace of progress.
We all learned more from the prior comment than we did from this one.
And there are benchmarks that cleanly separate the SOTA models:
https://epoch.ai/MirrorCode
Saturation of benchmarks is a property of benchmarks just as much as of the models.
Even if both aren't true, your evidence was people saying two opposing things. The truth (if there is a single objective truth on a given thing) has little bearing on whether or not different people agree on it.
Something being "non-deterministic" is orthogonal to whether or not skill plays a role.
We absolutely don't know enough about LLMs and intelligence to make such a bold (and ridiculous) claim. If anything, all evidence point to the contrary, with new scientific breakthrough achieved across a variety of fields via LLMs.
I've been really struggling to understand how the HN community can so boldly claim that LLMs are going to stop improving or not really smart. I just read it as the "denial" stage of the stages of grief that a good portion of this community is in right now (which is understandable).
We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.
If we would know that, there would be no need for interpretability research.
I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?
Claiming that you understand LLMs is similar to saying that you understand how our biology work because you understand evolution. No - you understand the mechanism behind evolution, but not the complexity it produces.
> maths are internally coherent and entirely theoretical
Nope. This kind of wish-washy thinking is not what we mean by understanding.
https://iep.utm.edu/math-inc/
This is like saying we don‘t know how a car works because a car can beat the best human athletes in 100 meter dash.
But I would bet that pretty much all of the improvement we’ve seen over the last year with coding has come from RL, not from the models becoming particularly stronger. And this makes sense, if models grow sublinearly with compute. And it seems like they do.
As a civilization, we are currently brushing up against the physics of efficiency. In many areas we have achieved close to what is theoretically possible, based on physics.
Such was not the case for the majority of human existence.
The body of research a.k.a. "writings of the ancients" is now insurmountably higher than it would have been during the time of ibn al-Haytham, when any kind of writing at all was scarce and literacy was low.
It's just inadequate benchmarks. Anyone who has used Fable for anything particularly difficult will have seen that it's miles ahead of Opus 5.0, yet the majority of benchmarks are completely unable to capture this.
I have seen people claim the exact opposite. If there was so much progress, then you wouldn't have endless disagreements with people championing their own favourite model as the strongest.
We already know from testing humans that test scores don't correlate that well with how effective a person is at work. Same applies here, we just aren't that great at making good tests.
What were seeing is all models failing to ace these tests.
"Benchmark Saturation" is term that promotes lowering the bar.
I think Epoch has the best analysis I’ve seen on evaluation trends; they use IRT to basically model a variety of benchmark difficulties, and then model a capability parameter for each model. This is as robust a sort of “meta-study” of evaluations as I’ve seen and the trend in capabilities show no sign of slowing down.
So I think people’s feelings clash with reality, and that’s because releases are more frequent and the jumps between releases are smaller, but the growth in capabilities _over time_ has not changed for the better or worse over a very very long period of time.
This is not "Acing" a test, this is hitting a wall.
If models can't get more than 90% of the benchmark right I think it's a strong indication that they were not trained on the answers and that benchmark itself is messy enough that <10% desired answers might be wrong or misleading.
Also to respond to the parent comment: benchmarks have a variety of difficulty levels. Humanity’s Last Exam, though now hitting the beginning of a saturation phase with Fable, was long unsaturated while other benchmarks saturated awhile ago. So that’s what I meant by Epoch capability index: using IRT models this effect so that you gather robust signals from variety of benchmark difficulties and can track progress over time as model capabilities have evolved (and so benchmarks have had to evolve to keep up).
But yes like I was saying: all benchmarks are problematic, some are useful. Benchmark quality problems abound, so 90% being the true ceiling is not surprising. There may be other factors at play here too, I haven’t studied this problem that deeply to have a good thorough answer to this. But keep in mind there are probably 50,000 benchmarks in the literature and that is not a joke number. A crapload of noise in that signal but it’s not all noise.
Most "AI" is really an optimization algorithm in software tools, same as its always been. This really isnt anything new, aside from adding a chatbot / MCP interface to the same tools.
Frontier labs have categorically different & better set ups for evaluation, they're fine. It's work but it's not a crisis.