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ofirpress
gwd
They're not saying "Don't use SWE-bench Verified because it's saturated".
They're saying:
1. A large number of the tests are inaccurate; so correct solutions will be marked as incorrect.
2. Frontier models have already read and memorized the PR's the problems are based on.
3. In fact, many problems are essentially impossible to get right if you haven't memorized the solution: for example, the test cases will fail if you didn't happen to expose a helper function with a specific name. That name isn't mentioned in the problem; but frontier models are passing that test anyway because they remember that such a helper function is necessary.
If the next stage of benchmarks don't address these issues, they'll continue to have the same problems, saturated or not.
energy123
> 93.6% (congrats Anthropic)
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?
cjsaltlake
I suggest reading the Mythos report's discussion on SWE-bench and contamination. I think it's fairly convincing that you can account for contamination and still trust SWE-bench numbers on models that aren't over-optimized for it.
stingraycharles
You can trust that a model that scores 40% vs a model that scores 90% is indeed worse.
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.
kator
> models that aren't over-optimized for it.
But how do you know the model was over-optimized for it or just really good?
MagicMoonlight
This article says anthropic models can write out the entire benchmark solution set word for word from memory
fulafel
there's more details under the Too narrow and too wide tests heading.
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.
kator
Those who fail to study history (or live through it) are doomed to repeat it.
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.
wtallis
That's a slightly different problem. There's no thing as saturation for a performance benchmark like SPEC; we can always conceive of a faster processor (even if we don't know how to build one). Saturation is the problem that once you are at (or near) 100% pass rate on a test of pass/fail questions, there's no room for the score to keep going up and the test has lost any power to discriminate between competing options.
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.
fibonacci112358
Maybe OP was thinking about compilers "cracking" certain SPEC benchmarks: implementing exactly the optimization needed to boost a benchmark quite a lot, but that opt. probably won't apply to any other code out there (usually it's so targeted and risky with general C/C++ code that intentionally it doesn't work on anything else). That happened a couple of times over the years, I know about the Intel compiler cases for ex. I can certainly see LLM providers adding tricks that help a certain class of benchmarks, but doesn't help much for anything else.
mrothroc
From a verification-topology angle, what makes algotune.io contamination-resistant? Is it because the correctness oracle is a performance metric (which can't be memorized) rather than a fixed test that can?
davidheineman
SWE-bench is fantastic! IMO, the scrutiny is a byproduct of the adoption and success of the benchmark.
akavel
Also, in meantime, there's https://SWE-rebench.com as a nice riff on SWE-bench, as far as I understand.
EnPissant
> 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.
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.
Bombthecat
Both of them look pretty old?
cjsaltlake
code clash I think would be quite hard to game or contaminate unintentionally; considering that models need to compete against one another
gertlabs
https://gertlabs.com already does this at scale.
An industry-standard benchmark shouldn't be hosted or designed by a lab producing the models, regardless.
Bombthecat
I mean the data / benchmarks
Jcampuzano2
Its pretty clear that any benchmark that comes out will be outdated and exist within the training data with short measure. There will always be an incentive to optimize specifically for these benchmarks even if just for marketing material. Sure there is a training cutoff, but its usually only 3-6 months off of the public release dates.
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.
mnky9800n
This is why I made Zork bench. Zork, the text adventure game, is in the training data for LLMs. It’s also deterministic. Therefore it should be easy for an LLM to play and complete. Yet they don’t. Understanding why is the goal of Zork bench.
kqr
I have worked on similar problems. See e.g. [1].
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)
malfist
I'm going to ignore all that and tell my developers working in complicated codebases that they have to use AI. I'm sure comprehending side effects in a world building text adventure is completely different that understanding spaghetti code
seanmcdirmid
You can code your prompts to read and write an external world model on the side. This is what most people do who are seriously doing games with LLMs.
mnky9800n
we should talk. i sent you an email.
WarmWash
The open models only give the SOTA models a run for their money on gameable benchmarks. On the semi-private ARC-AGI 2 sets they do absolutely awfully (<10% while SOTA is at ~80%)
It might be too expensive, but I would be interested in the benchmarks for the current crop of SOTA models.
roenxi
Have the open models been tried? When I look at the leaderboard [0] the only qwen model I see is 235B-A22B. I wouldn't expect an MoE model to do particularly well, from what I've seen (thinking mainly of a leaderboard trying to measure EQ [1]) MoE models are at a distinct disadvantage to regular models when it comes to complex tasks that aren't software benchmark targets.
CamperBob2
Actually the Zorks weren't deterministic, especially Zork II. The Wizard could F you over pretty badly if he appeared at an inopportune time.
mnky9800n
I feel like you are being pedantic. There are very few parts of Zork that are not static to the game. Yes the thief shows up randomly but that’s not the main point of the game.
Schlagbohrer
Was that using an RNG? Or is the entire game deterministic?
doingthehula
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cbg0
> let the community decide
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.
trueno
yea lol i think the community on this one is woefully unqualified to call any shots here. the goalposts are basically teleporting and everyone's aligning success with their own incredibly vague, personally created agentic non-deterministic workflows success. there's like no real answers coming from "the community" in this space at the moment, it's vividly similar to cryptocurrency cycles. most importantly, like you say, vibe coders are going to be the largest subset of the community and probably the most unqualified to assess performance because they're mostly clueless to how things work under the hood.
WarmWash
An easy way to make coding benchmarks viable again is to initialize the models with 200k of distracting or unrelated tokens in their context. Or even just run the tests sequentially in the same context and see how far the model gets before it unwinds.
These benchmarks are always greenfield, but people want a model that can deal with a rotted context.
adamandsteve
"The community" is astroturfed as hell though. Anthropic pays influencers to promote Claude Code and likely bots a ton as well, so it's hard to come to any kind of consensus online. Even if everyone was acting in good faith, some people will have a much better experience than others because of the domain they're working in (e.g. AI being much better at frontend and commonly used libraries).
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.
InsideOutSanta
Yeah, it's crazy that there is no trustworthy source for model reviews. I'd love to know how well the new Deepseek 4 actually performs, for example, but I don't want to spend the next week testing it out. Reddit used to be a somewhat useful gauge, but now there are posts on how 4 is useless right next to posts on how amazing it is. And I have no idea if this is astroturfing, or somebody using a quantized version, or different workloads, or what.
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.
rhdunn
One challenge is that model evaluation is typically domain/application specific. Model performance can also depend on the system prompt and the input/context.
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.
jayd16
Lets just base benchmarks on bounty rankings. To bench a model, you have it look at PRs on some open source projects. It has to complete a novel task or improve a previous task but no points for just re-doing a task with an existing PR. We rank the tasks by difficulty for the benchmark post-facto, once completed.
If an AI company wants to show off, it'll have to crush some OSS PRs. If another company wants to say their model remains supreme, it'll have to complete other tasks that were left on the table.
Of course, you would only bother the OSS project with new PRs once you were actually not embarrassed by what your model did.
In this way, rankings are created from jolly combat and one-ups-manship and we get some OSS work done.
(mostly joking but it would be a fun way to do things)
mtrifonov
Still downstream of the actual issue. The benchmarks measure capability and the bottleneck stopped being capability a while ago.
What you actually want to measure on these models is what they can SEE in production. Context shape, retrieval quality, tool use, ability to compose state across turns. None of that is in SWE-bench because SWE-bench is shaped like a one-shot problem set and frontier coding work isn't shaped like that anymore.
Even a perfectly contamination-free benchmark would mostly test the wrong axis. The model is already at human-grad-student level on isolated problems. The leverage is in how it operates inside a larger system. And that's almost like, a taste/preference issue, and virtually impossible to objectively measure.
jvuygbbkuurx
I think the solution is a bunch of private trusted benchmarks, and averaging their announced results.
zephen
> averaging their announced results.
Obligatory XKCD: https://xkcd.com/937/
Escapado
I agree with the sentiment but I wonder if a sufficiently large amount of sufficiently sophisticated benchmarks existed then I would be surprised if a model would only memorize those benchmarks while showing terrible real world performance. We are not there yet but maybe one day we will be.
cpard
Benchmarks/evals are really hard and they become harder when there’s huge incentive to game them at an industry scale.
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...
softwaredoug
It’s just hard to make them not part of the training data. We see this a bit with BrowseComp plus and other deep research datasets. Not because frontier labs are trying to cheat, but just from training on the full web.
You need new datasets perpetually.
cpard
That’s true. it also depends heavily on the type of task, not everything is equally represented on the web today and it remains to be seen if this is going to change or not.
fnordpiglet
Database benchmarks are another.
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.
operatingthetan
Would creating new benchmarks every month solve this problem?
preciousoo
Or create "blind" benchmarks.
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
jddj
For the most part I think we get the benchmarks we deserve.
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
threepts
Why don't they ask their premier model to generate a bench for them?
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.)
falcor84
They focus on minimizing the number of moves and don't allow any harness whatsoever, putting the bar extremely high. The current top verified contender (Claude Opus 4.6) is at only 0.45%. But with how new it is, I expect a lot of improvement in the next generation of models.
threepts
Optimal for judging actual reasoning ability rather than an LLM's ability to regurgitate knowledge from a necropost on HN/Reddit/Twitter from 2018.
knollimar
a small harness that stores text files and manages context could be useful, otherwise you lose all ability to measure that skill (and that's important because it represents real world use cases on large code bases)
jjmarr
I'm making an LLM agent that can play DS games. The biggest blocker is clicking on the right spot to move things around in space rather than reasoning abilities.
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.
sowbug
Why don't they ask their premier model to generate a bench for them?
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.
alansaber
Very (reasoning) heavy benchmarks do seem like the way to go, being the hardest to game.
xtracto
Can AI write a problem so difficult that even AI cannot solve?
Hehe
therealdrag0
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gertlabs
A better benchmark needs to be objectively scored, have multi-disciplinary, breadth, and be scalable (no single correct answer).
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.
orangebread
Wow. This benchmark definitely feels more accurate than the other rankings I've seen. My experience with gpt 5.4/5.5 is that they are technically flawless and if there are any technical issues that is because the input didn't provide enough clarity; that's not to say that it doesn't autonomously react to any issues during bug fixes or implementations, but it'll tend to nail its tasks without leaving behind gaps.
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!
euleriancon
Are we looking at the same data? On that site I see that opus 4.7's and gpt 5.5's g scores are within each others confidence intervals, and both significantly ahead of the number 3 model.
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.
gertlabs
Decision making refers to the environments where the LLM is called on every tick (like games with social communication), examples here: https://gertlabs.com/spectate.
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. Good find, we should probably better adjust the weighting here for decision games with low match counts.
orangebread
Right, I'm including my own observations in what the leaderboard is showing. Could be confirmation bias, but I use both Opus and GPT extensively and since GPT 5.4 I have noticed that Opus doesn't even begin to touch GPT's level of technical depth. I was hoping Opus 4.7 would close that gap, but unfortunately it doesn't even compare to GPT 5.4 in that sense.
I'm not being a hater, I love Opus for different reasons, but I can't rely on it for its technical ability.
gertlabs
Much appreciated! MiMo V2.5 Pro is by far the most underrated recent release (probably because it wasn't open weights from the start).
yalok
amazing to see Claude Code top models still way above all other models for C++ & Java, while GPT 5.5 is higher in Python & JS and others. Shows the skew in the training data sets, and maybe the go-to-market focus - with Anthropic focusing on enterprise customers much more than OpenAI?
Matches with my experience with Opus for C++.
C# results are empty - @gertlabs - any ETA for those?
gertlabs
C# testing is a new feature added a few days ago from HN comment suggestions, samples will continue growing. Most C# data is currently for non-agentic workloads: https://gertlabs.com/?mode=oneshot_coding
monlockandkey
Your benchmark suggests Deepseek V4 pro performs worse than Deepseek V4 flash? That is in an interesting result. Any comments on that outcome?
gertlabs
It's a surprising result, and a lot of it stems from the Pro variant struggling with our custom harness in agentic tasks (whereas Flash does fine), as well as provider instability. Failed requests are not counted against the model in its score, but it's possible there are additional silent degradations even on successful requests.
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.
kqr
It was never that great, it seems. For all of 2025 there was virtually no improvement in the rate at which models produced quality code. They only got better at passing automated tests.
stevex
It's not true that there was no improvement in the rate at which models produced quality code.
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.
civvv
This is likely true. I think model quality has stagnated and that its likely a non-trivial task to find a new improvement vector. Scaling the width of the model (which has been the driving force behind the speed of improvement thus far) seems to have reached its limit.
It will be interesting to see the implications of this. Tooling can only do so much in the long term.
mxwsn
How do you know that width scaling has been the driving force of improvement?
civvv
I am no insider and have never even tried to build an LLM, so I can only guess. But the general sentiment seems to be that this is the case. If you are interested, I would recommend you read the MIT paper "Superposition Yields Robust Neural Scaling" [0]. It confirms an interesting trend: models represent more features/concepts than they have clean independent dimensions, so features overlap. Increasing model dimension reduces this geometric interference, which lowers loss in a predictable way, but with diminishing returns.
This has, in my opinion, likely been the primary vector in getting better models thus far, but MIT mathematically proves that it yields diminishing returns for each new dimension added. It will get more and more expensive and the cost-return will or probably already has made it infeasible.
Ilya appear to support sentiment this as well. [1]
[0] - https://openreview.net/forum?id=knPz7gtjPW [1] - https://www.businessinsider.com/openai-cofounder-ilya-sutske...
waterTanuki
I mean, it's not exactly a PhD level question. One can infer from the extreme demand of GPUs and DRAM + new data center construction that all the providers are banking on width.
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cjsaltlake
But, that's an enormous source of coding productivity, and it's why Anthropic is worth billions... The reason SWE-bench has been so successful and useful for coding is that software engineering has a ton of tradition and infrastructure for making and using automated tests.
greenchair
maybe this is why these companies pricing plans are getting more limited and expensive..
vintagedave
> We audited a 27.6% subset of the dataset that models often failed to solve and found that at least 59.4% of the audited problems have flawed test cases that reject functionally correct submissions, despite our best efforts in improving on this in the initial creation of SWE-bench Verified.
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!
embedding-shape
> Is this saying a quarter of the questions and answers were wrong, this whole time?!
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...
wtallis
I'm not sure people are really trying to interpret this kind of benchmark as being accurate in gauging the magnitude of improvement. It seems pretty obvious that doubling your score on some benchmark where 100% means "correctly answered all of these specific problems" doesn't translate directly to performing twice as well on all problems. I think what people want from these benchmarks—and what they do get to some extent—is answering the question of "is model A better than model B", especially the subset of "is this local model better than last year's frontier online model".
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.
avereveard
the ecosystem obsession with public benchmarks comes from the fact that running benchmark costs, and labs don't test on any given private benchmark
but yeah you're correct anyone optimizing for public-bench rank instead of their own task-distribution eval has been pointing at the wrong thing for a while
still I guess useful signal to know which one model to consider, negative signal is still signal, assuming everyone is gaming benchmark in certain ways, lack of performance do result in a real workload effect
wavemode
> No, they're saying 59.4% of the 27.6% subset had flawed test cases I think.
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?
sillysaurusx
Imagenet is one of the most popular datasets on the planet. Turns out, a significant fraction of its images are mislabeled. In the limit case the model would have to fit towards wrong answers to get higher than a certain percentage.
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.
embedding-shape
> 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.
cindyllm
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jmalicki
Has it been reasonably possible to overfit to the errors in ImageNet, or are they effectively random noise?
yorwba
To be useful for identifying which model is better, benchmark scores only need to correlate with true performance, for which it's enough that the majority of tasks are scored correctly. You could have a terrible benchmark where 49% of the labels are wrong and a model that always answers correctly gets a score of 51%, but as long as it's higher than the always-wrong model at 49%, it's still directionally correct.
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.
motoboi
It’s saying that 16% of the problems have well, problems.
vintagedave
You're right - I did not apply the math. (I won't edit, in order to let the parent comment still make sense, and thankyou for the correction.)
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?
motoboi
Wait until you discover how many wrong labeled images in imagenet and that it still kickstarted the deeplearning revolution.
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embedding-shape
> Curiously Opus 4.7 claims to have a 87.6% pass rate and Mythos claims to have a 93.9% pass rate... leading to the conclusion that it's actually possible to "solve" the problems that OpenAI claims are incorrect.
Huh, that is very curious and interesting indeed. If that's indeed true, that Anthropic claims that pass rate while OpenAI claims the test cases are flawed and broken, then clearly one of them aren't telling their whole side...
gpm
Oops, sorry, moved this portion of the comment to a top level comment simultaneously with you replying. Since the part of the comment that was replying to GP was addressed well in a simultaneous comment.
https://news.ycombinator.com/item?id=47911074
Citation for the claimed pass rates is: https://llm-stats.com/benchmarks/swe-bench-verified
rustyhancock
I think an Olympiad format is better. But the financial incentive is such that it might be near impossible to stop leaks.
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.
cjsaltlake
SWE-bench was created to replace olympiad coding benchmarks. I think past olympiad coding benchmarks were much worse representative of real-world coding than something like SWE-bench, which is derived from real units of labor.
Further, olympiad style benchmarks are arguably easier to contaminate / memorize unless you refresh it regularly; but that goes for SWE-bench too.
rustyhancock
I was picturing one-shot performance only for the benchmark, on novel real world tasks. I.e. the score on the March Olympiad you got in April isn't relevant.
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.
ripvanwinkle
>>In our analysis we found that all frontier models we tested were able to reproduce the original, human-written bug fix used as the ground-truth reference, known as the gold patch, or verbatim problem statement specifics for certain tasks, indicating that all of them have seen at least some of the problems and solutions during training
this statement alone seems to invalidate the SWE-bench tests
marlburrow
The "private benchmarks" suggestion comes up every time, but I think there's a more interesting axis: benchmarks built on top of already-public, already-stable test instruments. SWE-bench is fundamentally a corpus that lives on GitHub — once it ships, it leaks into training data automatically. Benchmarks built on contested qualitative instruments (psych tests, opinion surveys) have a different contamination profile because the correct answer doesn't exist in the training corpus to memorize — only the question does.
That doesn't help for measuring coding ability specifically (you fundamentally need a code-correctness oracle), but for capability axes where the "answer" is a stated position rather than a verifiable fact, public + stable can still be useful. The SWE-bench problem isn't really "public", it's "public + has a fixed correct answer".
pkoiralap
This was bound to happen either organically or inorganically. Make sure it performs well on the benchmarks. And it doesn't really matter if it doesn't generalize outside of it right? :D
Also similar: Graduate student descent. https://sciencedryad.wordpress.com/2014/01/25/grad-student-d...
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I'm a co-creator of SWE-bench:
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 :)