Comment by eli
4 days ago
That's a plausible explanation but I'm not seeing evidence for it.
I have a personal benchmark suite of 14 real, non-public tasks. Opus 5 and Fable tied on 10, Opus won on 3, and Fable won on 1. It's a really strong model.
4 days ago
That's a plausible explanation but I'm not seeing evidence for it.
I have a personal benchmark suite of 14 real, non-public tasks. Opus 5 and Fable tied on 10, Opus won on 3, and Fable won on 1. It's a really strong model.
This is an interesting idea, without giving away your benchmarks specifically what kind of stuff do you test? I might try to assemble something myself, it's so hard to determine model quality from the system card these days.
I have actually been planning to open source the framework. Only benchmarks I care about are the ones that look like real work I do. So it makes it easy to trawl your own repos looking for benchmark task candidates from real bug fixes or features. Then as a bonus I can compare the agent’s solution to my own as a reference.
So most look like that but I did include a few one-shot “build an app that solves this problem” and some qualitative design tasks and a tough algorithmic optimization one.
Also working on a product to build tasks from your own work for testing coding agents. Main thing I would offer is to look carefully at the agent trajectories - they love to figure out ways to cheat. Additionally, consider what "winning" means. If just using test pass rate, consider that tests might not encode what good means in your repo. I have been having success using "equivalence with merged PR" as judged by an LLM as a signal.
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Would open sourcing it make you feel like the results might be compromised? I'd rather keep it private so it's specific to my use cases and not included in any training date (no matter now small the signal in the overall data).
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