Posted by kmdupree 15 hours ago
1. SWE-bench Verified is now saturated at 93.9% (congrats Anthropic), but anyone who hasn't reached that number yet still has more room for growth.
2. SWE-bench Multilingual and SWE-bench Multimodal (which we'll open source in the next month) are still unsatured.
3. All benchmarks and benchmark paradigms eventually become saturated. That's why the SWE-bench team has worked hard on building the next stage of benchmarks, and we have a few that are already out, for example https://codeclash.ai/ or https://algotune.io/ . And we'll have more to say soon :)
But the article says "We audited a 27.6% subset of the dataset that models often failed to solve [which is 19.1% of the problems at time of publication] and found that at least 59.4% of the audited problems have flawed test cases that reject functionally correct submission"
0.191 * 0.594 > 1 - 0.936
Does this mean that the audited subset wasn't representative? Or that Anthropic is getting high answers through some shady means?
It would be interesting to see a deeper investigation, into how the models are dealing with this and whether the successful ones seemed to be trained on the benchmark.
But how do you know the model was over-optimized for it or just really good?
You can’t trust it that a model that scores 93% is better at software engineering than a model that scores 90%, because at that point it’s impossible to distinguish between recall and reasoning.
SPECint and SPECfp went through this exact movie: benchmark, saturate, retire, replace, repeat. The treadmill is the product.
I don't have the solution just noticing the pattern.
However, both kinds of tests are susceptible to over-fitting: an LLM can be trained on the exact test questions, and a CPU can be designed with eg. branch predictors and cache sizes tuned specifically to handle a particular benchmark or workload.
An industry-standard benchmark shouldn't be hosted or designed by a lab producing the models, regardless.
But if some or all players are bench-maxing it, then it becomes a much less useful metric for comparison.
Also, this doesn't address what OpenAI says about the test suite disallowing valid solutions.
The problem with coding benchmarks then becomes creating novel benchmarks that are guaranteed to not already be in the training data, and not borrow anything from previous benchmarks.
In this regard I don't think any benchmark that was created before a given model is released should ever be considered valid or representative of model performance. The potential financial gain for including the data just to be able to market a minor improvement is too swaying. With that in mind they should honestly just stop including benchmarks altogether in marketing material
Let the model speak for itself and let the community decide, but of course that will never slide with corporate types with so much money on the line.
The LLMs I have tested have terrible world models and intuitions for how actions change the environment. They're also not great at discerning and pursuing the right goals. They're like an infinitely patient five-year old with amazing vocabulary.
[1]: https://entropicthoughts.com/updated-llm-benchmark
(more descriptions available in earlier evaluations referenced from there)
If you doing an RPG, which I guess is where this is more obvious, you track the play and enemy positions, their health, their moods and perhaps top thoughts, the state of important inanimate objects. if you break down the door, you update the door's state in the document. This is in contrast to just giving the LLM the previous turns and hoping it realizes the door is broken down later (just by statistical completion).
It might be too expensive, but I would be interested in the benchmarks for the current crop of SOTA models.
Which community are we talking about? The professionals with 10+ years experience using LLMs, the vibe coders that have no experience writing code and everyone in between? If you read some of the online communities the experiences with the models all over the place, some compare GPT 5.5 to the second coming of JC while others think it's stupider than 5.4.
I personally don't have time to build a set of private benchmarks to compare the models that are coming out so I'm mostly relying on private and semi-private benchmarks to get a feel for how models are improving before I subscribe to a service and start using it myself. At least it's something a bit more reliable than the vibes of random people and bots on reddit.
In the end all it does is affirm what you're saying though. Benchmarks are essentially obsolete the moment they become recognized. I suppose it's just another iteration of Goodhart's Law.
These benchmarks are always greenfield, but people want a model that can deal with a rotted context.
The only real way to evaluate a model is to test it yourself but that's exhausting for each new model and not comprehensive anyway.
I also find it increasingly difficult to evaluate the models I actually do use. Sometimes each new release seems identical or only marginally better than the previous version, but when I then go back two or three version, I suddenly find that oder model to be dramatically worse. But was that older model always that quality, or am I now being served a different model under the same version name?
It's all just so opaque.
Regarding evaluation, I've found using tools like promptfoo (and in some cases custom tools built on top of that) are useful. These help when evaluating new models/versions and when modifying the system prompt to guide the model. Especially if you can define visualizations and assertions to accurately test what you are trying to achieve.
This can be difficult for tasks like summarization, code generation, or creative writing that don't have clear answers. Though having some basic evaluation metrics and test cases can still be useful, and being able to easily do side-by-side comparisons by hand.
Obligatory XKCD: https://xkcd.com/937/
By determining if model gets better or not on a given benchmark, OpenAI selects models against benchmarks, implicitly using them in the training.
as long as theres a test framework, you could gauge success deterministically.
ELT-Bench is another recent example. It was the first serious attempt at a benchmark for data engineering workloads, published about a year ago.
A few days ago, a follow-up paper from a group that includes one of the original authors audited the benchmark itself. The team gfound that the benchmark has structural issues that biased results.
Here’s the paper: https://arxiv.org/abs/2603.29399
None of these are new though, the industry has gone through all that before just in a smaller scale and there’s a lot to learn from that. Here’s a post I wrote on the parallels we see today to what happened with the benchmarketing wars of the database systems.
https://www.typedef.ai/blog/from-benchmarketing-to-benchmaxx...
You need new datasets perpetually.
I have empirical experience though building classifiers that can have no precision measurement because the classifier performs invariably better than humans. They become the state of the art benchmark themselves and can’t be benchmarked except against themselves. These are for tasks that are non trivial and complex, but less logical than coding and less sustained reasoning. There may come a day though, when there is no calibrated benchmark that is independent of the models it’s measuring.
10 groups of 3 researchers, all have their own benchmarks that they do not share (testing it without the authors knowing is a different problem, maybe they only run the benchmarks when the gen-pop has access to the models).
that's 10 different tests. Aggregate pass rates
Many SWE-bench passing PRs would not be merged: https://news.ycombinator.com/item?id=47341645
Top model SWE bench scores may be skewed by git history leaks: https://news.ycombinator.com/item?id=45214670
Jokes aside, a benchmark I look forward to is ARC-AGI-3. I tried out their human simulation, and it feels very reasoning heavy.
Leaderboard: https://arcprize.org/leaderboard
(Most premier models don't even pass 5 percent.)
Arc AGI seems to test that as well. Every game is a rectangular grid to make it as easy as possible yet the AIs still fail.
I'm fairly certain the way forward isn't through agents directly interfacing with UIs but through agents using scripts and other tools to interact with the interface. That's why harnesses are so critical to performance on tasks like this.
I would like a version of Arc AGI that tests the agent's ability to dynamically create these harnesses.
Meanwhile AI agents are expected to guess pixels and fail each time.
It's not a crazy idea. Have the older model interview the newer one and then ask both (or maybe a third referee model) which one they think is smarter. Repeat 100x with different seeds. The percentage of times both sides agree the newer model won is the score.
Hehe
Jan 2025 was Claude 3.5 Sonnet, Gemini 1.5 Pro and OpenAI had GPT-4o.
As someone who used all those models, as well as today's frontier models - today's models are a significant step up from those.
It will be interesting to see the implications of this. Tooling can only do so much in the long term.
That's what we designed at https://gertlabs.com. We put a lot of thought into it, and kept it mostly (not fully) related to problem solving through coding.
Opus otoh is overrated in terms of its technical ability. It is certainly a better designer/developer for beautiful user experiences, but I'll always lean on gpt 5.5 to check its work.
The biggest surprise in the benchmark is Xiao-Mi. I haven't tried it yet, but I will be after looking at this.
Grats on your team for putting together something meaningful to make sense of the ongoing AI speedrun! Great work!
Your comment makes it sound like they are miles apart, which the benchmark doesn't seem to support.
Edit: I looked at the data more and the two models are only basically equal when looking at the mean of all the tests. Gpt 5.5 significantly outperforms opus 4.7 in coding, while opus 4.7 significantly outperforms in "decision making." I'm not seeing details on what decision making explicitly means.
Because GPT 5.5 just launched and those games take longer to accumulate data for, it just doesn't have enough samples yet. It will end up with a wider lead on Opus, I am sure. Coding evals always have large sample sizes on day 1. We should probably better adjust the weighting here for decision games with low match counts.
Either that, or Flash is truly a better architecture and the Pro variant is heavily benchmaxxed. It wouldn't be the first time we saw something like that in our benchmarking. We collect samples every week so it'll be interesting to see if it rebalances over time as new providers host the model. Flash is great though; it's so fast and cheap.
Is this saying a quarter* of the questions and answers were wrong, this whole time?!
If so, how was this ever, in any way, a valid measurement?
And what was the process for creating this benchmark and how did it end up with such an extraordinarily poor set of data? (There is a description later of how, which seems to be a high standard and I struggle to understand how it aligns with the other results they discuss.) Kudos to them for highlighting the issues, but I am left with questions.
[*] Not one in four, but one in six, thanks commenters for the correction; leaving the original since, eh, my bad, and it lets replies make sense. I feel the broad point still stands!
No, they're saying 59.4% of the 27.6% subset had flawed test cases I think.
> If so, how was this ever, in any way, a valid measurement?
Benchmarks essentially aren't, for practical concerns anyways. They don't represent your use case, and they don't represent any and all use cases, they're valid for measuring exactly what's included in the benchmarks, nothing more and nothing less.
I don't understand the ecosystems obsession with using public benchmarks, they hardly ever tell you anything of value. Ok, Qwen 3.5 is 50% better on Benchmark X than Qwen 2.5, does that mean it'll be 50% better for what you're using it for? Very unlikely.
I've been running my own private benchmarks, with test cases I never share anywhere, for the specific problems I'm using LLMs for. Some are based on real, actual cases where a LLM went wrong and I had to adjust the prompt, and over time I've built up a suite.
Most of the times when a new update comes out to a model, it moves maybe 2-3% in my own benchmarks, meanwhile they tout 30-40% increase or something ridiculous in public benchmarks, and we're supposed to believe the models' training data isn't contaminated...
The marketing departments touting each model do want to claim superiority on the basis of slivers of percentage points, and that's probably always a stronger claim than the test results can reasonably support. And the benchmarks are obviously susceptible to cheating and overfitting. But when the scores aren't saturated and do show a big discrepancy, that kind of result usually seems to align with what people report from actually trying to use the models in the relevant problem space.
That being said, they didn't audit the other 72.4%, right? So it's likely that there are way more flawed problems throughout the full set?
The answer is “it works because ML wants to work.” It’s surprising how far you can get with something flawed. It’s also why such huge breakthroughs are possible by noting flaws others haven’t.
I do these sort of breakthroughs at home all the time! My wife would say the computer is doing something strange, and instead of just randomly clicking around, I read the error messages slowly and out loud, then follow what they say. Anyone can do this, yet it seems like a magical ability every time you employ it to help people.
Most machine-learning benchmarks have a fairly large fraction of incorrect labels, but when you just want to distinguish between different models, the time you'd need to ensure perfect scoring would usually be better spent on collecting a larger benchmark dataset, even if it ends up having more errors.
So not one in four, but one in six problems have problems.
That is extraordinarily high and the point still stands: is this truly saying a [large proportion] of the questions and answers were wrong, this whole time, and if so how was it ever a valid measurement?
I.e. A panel comes up with a series of problems.
Like advent of code or project Euler but more complex and constricted.
Benchmark outcomes could be performance points and measure of cost, time to solution (well token count really).
A couple times per year it's run.
It avoids overfitting.
Overtime the tasks can become more complex if needed.
If they benchmax it into being able to complete full products from spec and robust implementations amazing.
Further, olympiad style benchmarks are arguably easier to contaminate / memorize unless you refresh it regularly; but that goes for SWE-bench too.
Simple enough that anyone could run it with a regular subscription.
Really unless we can get the providers to ditch the gameable benchmarks they won't.
But industries love nothing more than a benchmark they can manipulate.
this statement alone seems to invalidate the SWE-bench tests