OpenAI Retracts Recommendation for SWE-Bench Pro Due to Benchmark Flaws
OpenAI no longer recommends SWE-Bench Pro
We recently audited SWE-Bench Pro, a coding benchmark we previously endorsed, and discovered significant design flaws affecting over 30% of its tasks. Issues like overly strict tests and underspecified prompts mean the evaluation no longer provides a reliable signal of model capabilities. Consequently, we are retracting our earlier recommendation and urging the community to exercise caution when interpreting results from this dataset.
Ultimately, an eval should provide meaningful signal through benchmarks that are hard to game, easy to trust, and genuinely reflective of model capability or alignment.
- jjcm
I want a new bench - given $100 of api spend, how much can a model accomplish for a suite of benchmark tests?
Give us something that measures a combination of efficiency and intelligence.
I think this would allow for some interesting tactics for smaller models - eg they could do things like computer use to test their results and grind on problems for longer to verify the outputs, whereas larger models may not have budget to self-test.
- GodelNumbering
There are also a lot of fake results out there on Terminal Bench 2 for different reasons (although the great team behind it Ryan/Alex et al, recently cleaned up a lot of dodgy submissions). A lot of labs publish the results by modifying timeouts or hardware config which effectively bypasses what is being tested in certain tasks. Then there is harness level cheating, models reward hacking and more...
In fact, one thing that still bothers me after months is the gpt-5.5 official submission. This task in particular https://www.tbench.ai/leaderboard/terminal-bench/2.0/codex/0...
The task has the following timeouts (https://github.com/harbor-framework/terminal-bench-2/blob/ma...).
[verifier]
timeout_sec = 1200.0
[agent]
timeout_sec = 1200.0
[environment]
build_timeout_sec = 600.0
Which means no agent should take more than 3000 seconds doing it. Two out of five attempts in the link above took well over 3000 seconds (75min and 80 min respectively). Even though they failed, the fact that they ran that long is sus.
Goodhart’s Law at work
- mlhpdx
Fundamentally aren’t they concluding that tasks assigned to software developers (human or otherwise) are often incomplete, self contradictory or worse? This is the world in which their tool must play. I’m unsympathetic.
- janalsncm
Based on the numbers here it seems there’s less than 800 tasks in the entire benchmark. That is enough for a handful of engineers to comb through in a week (which is what OpenAI eventually did here).
On the one hand, kudos to them for actually doing that work.
On the other hand, garbage in, garbage out. It’s a bit embarrassing for the original authors to have not actually checked, and it’s embarrassing for everyone downstream to have not checked either.
Also if you check the article, although an LLM did find issues, it tended to underestimate issues that professional software engineers found.
- bellowsgulch
Seems like depending on your field these days, the hot thing to do is build your own private benchmarks.
In my own testing, no frontier model knows how to replicate an original 1990s Super Soaker prototype design, which for the most part, should be almost completely possible with Home Depot parts.
They just don't understand PVC parts, triggers, etc.