[State of Code Evals] After SWE-bench, Code Clash & SOTA Coding Benchmarks recap — John Yang

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Latent Space: The AI Engineer Podcast 17 min 8 chapters transcribed 28 days ago

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How did SWE‑bench originate and become the industry standard?

Swyx 0:00

So Light and space

John Yang 0:04

that one

Swyx 0:05

to find Rec up. Night and space night. We're here at Neurups with John Yang of Suibench and many other things. But welcome. Thanks so much for having me. Yeah, really happy to be here. Uh last year I talked to Othir and uh I think Carlos as well, one of your co-authors. How's Sweetbench doing? Like just generally, the project is like one and a half years old.

John Yang 0:31

Yeah, yeah. I think one and a half years old in terms of when it was actually useful. Yeah. We put it out October twenty twenty twenty-three and then people didn't really touch it too much. And then of course like cognition came on the scene and Devon was an amazing release. And I think after that it kind of kicked off the arms rate. Did they tell you beforehand? And they just showed up. It was you know, I got an email about like two weeks ago. I think it was from I think it was from Walden. It was like Hey, you know, we have a good number on it. I was like, Wow, congrats, you know, thanks for using it. Yeah. And then the release was like mind blown.

Swyx 1:04

Guys did an excellent job. Yeah. Amazing. And then Sweet Bench Verified was like maybe last year. Yes, that's right. Yeah. Um catch us up this year. Like you have uh other languages, you have uh there's like a whole bunch of varieties of sweet bench now.

John Yang 1:18

Yeah. So

Swyx 1:18

what should people know?

John Yang 1:19

Yeah, for sure. Um I think there's a couple extensions that have happened. One is like more sweet benches, sweet bench pro, sweet bench live. Um

Swyx 1:27

Superbench Pro, was that with you guys? Because it looks independent. It's like different authors. It's completely independent.

John Yang 1:32

Yeah.

What role did Cognition’s Devin launch play in the SWE‑bench arms race?

Swyx 1:32

So they just call themselves Superbench Pro without your blessing?

1:35

Yeah.

John Yang 1:36

I think uh I think we're we're we're we're okay with it. Uh when we came out we were like, Oh cool, interesting. It would have been, you know, fun to be part of it. But you know, I mean congrats to them. It's a great benchmark. Yeah. Right. Uh but yeah, uh multimodal. Yeah. We did multimodal and multilingual. Um and I think like those have multilingual seems to be the is it like JavaScript, what else? Yeah, yeah. Multilingual is like it's like nine languages across like forty repos. But yeah, you Got him like JavaScript, Rust, Java, C, you know, Ruby. Yeah, yeah. Yeah.

Swyx 2:07

And then Course New Bench itself, a lot of people like they they talk about the the Django focus. Yes. Django. Is there is there is there like I I don't know, how do we how do we move past Django?

John Yang 2:18

Yeah, for sure. I mean it it's cool to see um a lot of the newer benchmarks like really try to d diversify the repos. Like in the two follow-ups we did with multimodal and multilingual, we made it a point to do that. So I think But you can also just put out C Bench twenty twenty five and just That is true and do a new distribution. Yeah, yeah. So it's been cool to see the follow ups. I think quietly and and it's a open question for me. I'm excited to see how people curate the next sets. Like it's kind of interesting to see in the literature or in their blog posts like how they're justifying why they're creating their separate split. The easier ones were like, oh, more languages, more repos. And then I think now people are like, well, ours is more difficult because of this curation technique.

John Yang 3:00

And I'm yeah, I'm excited to see how how long that lasts and you know where we're gonna like guide the evaluations towards.

Swyx 3:08

Yeah. And more recently you're working on Code Crash. Yes, that's right. Uh so let's give people uh you've already done other episodes uh other podcasts about it. Yeah. So refer people to to that with uh your chat with Andy. Uh but just give like a people like a one two sentence. Yeah, no. Yeah, no,

John Yang 3:21

happy to do it, especially on your podcast, it's on or um yeah, so basically the idea is I don't like unit tests as a form of verification.

Which new variants (Verified, Pro, Multimodal, Multilingual) expanded SWE‑bench’s scope?

John Yang 3:29

And I also think there's the issue with Sweetbench where all of the task instances are independent of each other. So the moment you have the model kind of submit it, oh, it's done, you know, and and and that's the end of the story, the end of the episode, you know.

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Chapters

8 chapters

1 How did SWE‑bench originate and become the industry standard? 0:00–1:32 2 What role did Cognition’s Devin launch play in the SWE‑bench arms race? 1:32–3:29 3 Which new variants (Verified, Pro, Multimodal, Multilingual) expanded SWE‑bench’s scope? 3:29–5:41 4 Why is moving beyond a Django‑heavy repo set important for code evals? 5:41–7:20 5 What is CodeClash and how does it test long‑horizon agent development? 7:20–9:15 6 How do programming‑game arenas like Halite and economic tasks shape CodeClash competitions? 9:15–11:36 7 Why are ‘impossible tasks’ in Tau‑bench considered a feature, not a bug? 11:36–13:38 8 What future does the host see for human‑AI collaboration in long‑running code benchmarks? 13:38–17:30

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Topics in this episode

Cursor 1 Jane Street 1 Cognition 1 Silas 1 Two Sigma 1 Ophir 1 John Yang 1 Dee Young 1 Code Clash 1 Jeffrey Ma 1 Code Crash 1 Sweet Bench Verified 1

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Recorded31 Dec 2025

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Show notes

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just "more repos," why Tau-bench's "impossible tasks" controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning. We discuss: John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: "we have a good number") SWE-bench Verified: the curated, high-quality split that became the standard for serious evals SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution The SWE-bench Pro controversy: independent authors used the "SWE-bench" name without John's blessing, but he's okay with it ("congrats to them, it's a great benchmark") CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization) SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations) AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation) The Tau-bench "impossible tasks" debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%) Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents) The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve — John Yang SWE-bench: https://www.swebench.com X: https://x.com/jyangballin Chapters 00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations 00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race 00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants 00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories 00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments 00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas 00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing 00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation 00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity 00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration 00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research

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