⚡️The End of SWE-Bench Verified — Mia Glaese & Olivia Watkins, OpenAI Frontier Evals & Human Data
episodePreviously titled “⚡️SWE-Bench-Dead: The End of SWE-Bench Verified — Mia Glaese & Olivia Watkins, OpenAI Frontier Evals & Human Data” — renamed by the publisher on Aug 2, 2026
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What is SWE‑Bench Verified and why was it considered a “North Star” for coding benchmarks?
Okay. Hi. We're here in the Open EI studio with Mia Olivia from the Frontier EBLs team or however you want to introduce yourself. Maybe maybe you want to introduce name what you do at OpenEI and we can get it started.
Sure. Hi, I'm Olivia. I'm on the
Frontier
EBELS team.
Hi, I'm Mia. I am a VP of research at OpenEI and my team are the Codex team, the Shumidata team, and the Alignment team. And we work a lot with Olivia's team on Frontier.
Yeah, very exciting. And as by my understanding, you were part of the original team that worked on C Batch Verified as well.
Yeah, Olivia's team, the frontier evads team and the human data team collaborated on creating Street Venge Verified.
So you've you've seen the evolution of coding benchmarks over time. And I I think it was round about sort of mid to late 2024 when you first covered Street Vengeance Verified. Things have evolved a lot since then. What's the blog post that you have worked on that you're that we're releasing today? Like what what is the sort of content that what's the main thesis that you're pushing out?
So the main thesis is that Sue Bench Verified has been one of the North Star coding benchmarks that the field has looked at to measure coding progress. But recently we've seen that progress is kind of stalled. And this we realize that this is because the eval is effectively saturated and also highly contaminated. So at this point we think that it's not really measuring coding performance improvements well anymore. And we think that the field should move away from this towards other benchmarks.
Like Sue Bench Pro.
Like Super Red Pro.
Yeah. Amazing. Yeah. I d uh one of the jokes I always have is like there's a group chat with all the labs and everyone just takes turns to increment like 0.1 on trucks and then it's like, okay, well, you have the best coding model, I guess, because you're 0.1% higher, but it's not super convincing at this point. No. Yeah. So cool. I think the let's let's sort of reset on like what was the original work that you guys did for Sequence Verified, which I think was pretty substantial. Like it was a like a very significant investment from Open AI, which like people still don't appreciate. And then what were the satisfactions that we that we found over time, right? So like what what was sweet bench verified or should should that people should know about?
Sweetbench Verified was a kind of a cleanup of original bench academic benchmark from a lab at Princeton called Suitebench. And the agent is basically given a code base and a task that was sourced from a real world repository and GitHub issue and was asked to solve the task and is graded on whether some tests pass. And at the time, this was quickly became a popular benchmark because at the time the field didn't really have good real world coding benchmarks, but then when OpenAI took a look at the benchmark as part of one of the evals we wanted to track in our preparedness framework. Folks started realizing that some of the cases where agents were failing were due to bad problem setups rather than just to models being dumb.
So folks at OpenAI did a pretty extensive human data campaign, hiring like almost a hundred real-world software engineers to go through the problems. And figure out like, are the tasks well specified? Are the tests actually fair? And kind of created a curated set of like 500 tasks that we thought were much better.
It's just it's maybe it's hard to to overstate like the amount of effort that it took to like create that benchmark.
Why did progress on SWE‑Bench Verified stall and what signs of saturation were observed?
It was literally like many expert software engineers reviewing the problems, like differentially multiple times and to, you know, basically like three different experts independently decided. Yeah, you
didn't have to do that. You just tripled your costs for just
I mean we had to do it we had to do it actually because it's quite a hard task to like look at something like a a problem and and the patch and then like it's not just the problem and the patch, right? You have to like understand it in the context of the code base that the human or or the motors and to solve the task.
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Chapters
8 chapters
1
What is SWE‑Bench Verified and why was it considered a “North Star” for coding benchmarks?
0:04–3:13
2
Why did progress on SWE‑Bench Verified stall and what signs of saturation were observed?
3:13–6:52
3
How was SWE‑Bench Verified built and what human‑review effort went into curating the tasks?
6:52–10:06
4
What evidence of contamination was found in SWE‑Bench Verified and how was it detected?
10:06–13:23
5
Why are some tests in SWE‑Bench Verified considered unfair or overly narrow?
13:23–16:32
6
When and why is OpenAI switching from SWE‑Bench Verified to SWE‑Bench Pro?
16:32–19:45
7
What does a great coding evaluation measure beyond simple pass/fail tests?
19:45–22:39
8
How should future coding benchmarks capture long‑horizon tasks, design decisions, and real‑world impact?
22:39–25:58
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