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⚡️The End of SWE-Bench Verified — Mia Glaese & Olivia Watkins, OpenAI Frontier Evals & Human Data

26 min episode · 2 min read
·
Mia Glaese,Olivia Watkins

Episode

26 min

Read time

2 min

Topics

Investing, Fundraising & VC, Artificial Intelligence

AI-Generated Summary

Key Takeaways

  • Benchmark Saturation Signal: When frontier models cluster around 80%+ on SWE-Bench Verified and gains shrink to 0.1% increments, the benchmark stops measuring coding capability and starts measuring noise. Teams should treat any benchmark where top models score above 80% as a signal to find harder, less saturated alternatives rather than continuing to optimize against it.
  • Contamination Detection Method: OpenAI deployed a contamination auditor agent that presents target models with open-ended questions about task descriptions and patches, probing for memorized ground-truth solutions or task IDs. This method surfaced contamination across GPT, Claude Opus 4.5, and Gemini Flash — including a case where a model's chain-of-thought cited a historical repository argument never mentioned in the problem spec.
  • Test Fairness Audit Finding: A deep-dive human review of problems no frontier model could solve found that over half contained flawed tests — most commonly, tests requiring a specific function or argument name never specified in the problem description. Benchmark creators should audit failures by comparing model solutions against gold patches with domain-expert reviewers, not just checking pass/fail rates.
  • SWE-Bench Pro Advantages: SWE-Bench Pro, produced by Scale, addresses verified's weaknesses: tasks are estimated at one-to-four hours and four-plus hours for expert engineers, covers more repositories and multiple programming languages, and the contamination auditor found only marginal familiarity with one or two source repositories across all tested models — a substantially cleaner signal than its predecessor.
  • Next Benchmark Priorities: Olivia Watkins identifies three gaps the field should fill: tasks requiring top engineers months or teams weeks to complete with rubric-validated grading, end-to-end product creation benchmarks, and real-world usage metrics tracking how much AI is actually deployed in production workflows — moving beyond synthetic pass/fail rates toward measurable economic and labor impact.

What It Covers

OpenAI's Mia Glaese and Olivia Watkins explain why SWE-Bench Verified, the dominant coding benchmark since mid-2024, is now saturated and contaminated, why the field should migrate to SWE-Bench Pro, and what next-generation agentic coding evaluations need to measure.

Key Questions Answered

  • Benchmark Saturation Signal: When frontier models cluster around 80%+ on SWE-Bench Verified and gains shrink to 0.1% increments, the benchmark stops measuring coding capability and starts measuring noise. Teams should treat any benchmark where top models score above 80% as a signal to find harder, less saturated alternatives rather than continuing to optimize against it.
  • Contamination Detection Method: OpenAI deployed a contamination auditor agent that presents target models with open-ended questions about task descriptions and patches, probing for memorized ground-truth solutions or task IDs. This method surfaced contamination across GPT, Claude Opus 4.5, and Gemini Flash — including a case where a model's chain-of-thought cited a historical repository argument never mentioned in the problem spec.
  • Test Fairness Audit Finding: A deep-dive human review of problems no frontier model could solve found that over half contained flawed tests — most commonly, tests requiring a specific function or argument name never specified in the problem description. Benchmark creators should audit failures by comparing model solutions against gold patches with domain-expert reviewers, not just checking pass/fail rates.
  • SWE-Bench Pro Advantages: SWE-Bench Pro, produced by Scale, addresses verified's weaknesses: tasks are estimated at one-to-four hours and four-plus hours for expert engineers, covers more repositories and multiple programming languages, and the contamination auditor found only marginal familiarity with one or two source repositories across all tested models — a substantially cleaner signal than its predecessor.
  • Next Benchmark Priorities: Olivia Watkins identifies three gaps the field should fill: tasks requiring top engineers months or teams weeks to complete with rubric-validated grading, end-to-end product creation benchmarks, and real-world usage metrics tracking how much AI is actually deployed in production workflows — moving beyond synthetic pass/fail rates toward measurable economic and labor impact.

Notable Moment

During GPT-4.5.2 evaluation, the model's chain-of-thought spontaneously referenced a historical version of a repository containing a specific argument the test required — knowledge never provided in the problem prompt — revealing that passing certain SWE-Bench Verified tasks may be structurally impossible without prior training contamination.

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Episode Transcript

Okay. Hi. We're here in the OPI Studio with Mia and Olivia from the Frontier Evals team or however you want to introduce yourself. May maybe Mia want to introduce, name what you do at OpenAI, and we can get it started. Sure. Hi. I'm Olivia. I'm on the Frontier Evals team. Are you sure? And Pete? Hi. I'm Mia. I am a VP of research at OpenAI, and my teams are the codex team, the simulator team, and the alignment team. And we work a lot with Olivia's team on Frontera. Yeah. Very exciting. And as by my understanding, you were part of the original team that worked on Cventorify as well. Yeah. Olivia's team, the Frontera Evarts team, and the human data team collaborated on creating three benchmarks. So you've you've seen the evolution of coding benchmarks over time. And I I think it was roundabout to the mid to late twenty twenty four we first published three branch verified. Things have evolved a lot since then. What's the blog post that you have worked on a year that we're releasing today? Like, what what is the sort of content what's the main thesis that you're pushing out? So the main thesis is that Supervised 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 has kind of stalled. And this, we realized 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 Supermunch Pro? Like Supermunch Pro. Yeah. Amazing. Yeah. I'd 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 your original work that you guys did for CVS verified, which I think was pretty substantial. Like, it was, like, a very significant investment from OpenAI, which, like, people still don't appreciate. And then what were the dissatisfactions that we that we found over time? Right? So that like, what what was Speechbench Verified or should should that people should know about? SuiteBench 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 and was asked to solve the task and is graded on whether some tests pass. And at the time, this was it quickly became a popular benchmark because at the time, …

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Books, tools, and gear mentioned in this episode

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Tools

  • SWE-Bench Verified, the dominant coding benchmark since mid-2024, is now saturated and contaminated
  • SWE-Bench ProRecommended

    by Scale

    SWE-Bench Pro, produced by Scale, addresses verified's weaknesses: tasks are estimated at one-to-four hours and four-plus hours for expert engineers, covers more repositories and multiple programming languages

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