Show HN: Parsimony – an open-source benchmark for how much code AI models add
Existing benchmarks measure solve rate and token output/cost, but don't punish excess code, which makes a codebase harder to work with in the long term. I wanted to measure this since it matters most for long-term projects. Method: it takes the patches that benchmarks already publish (DeepSWE, SWE-bench Verified, SWE-bench Live) and counts "coding units": syntax elements like statements, calls, names and literals. I didn't use lines of code, so cramming code isn't rewarded. Deleted code is subtracted, so it measures net units. GPT-6 and Claude 5.5 have no public patches yet, so I ran them myself on 3–10 tasks on my own subscriptions (shown as faded circles, not ranked). Any feedback or model contributions are welcome, especially public patch exports for newer models. GitHub: https://github.com/La5u/Parsimony Not affiliated with any of the benchmarks.
Discussion Highlights (6 comments)
fastball
On the one hand, I absolutely agree that we should try to measure and reduce verbosity that LLMs are slopping into our codebases. On the other hand, the fact that Astra is "winning" this bench is not great signal for me, since imo Astra produces basically unreadable code. Astra plays code golf. My own revealed preference is that I'd rather have the excessive comments and tests of Claude rather than the inscrutability of Astra.
Vakaiser
Does anyone have ways to reduce the complexity and verbosity of code generated via agents? Anything that can increase code quality would help.
Xeoncross
At this point, I really want all AI benchmarks to use the best coding/writing model that normal people can run locally. For now that seems to be Qwen 3.8 27B that requires ~28GB of RAM (Q4) which is pretty doable for anyone with a beefy GPU or slightly-above-average mac. I want to know how close open weights (open models) are to all these leading commercial models. Yes, yes, local models aren't as good, but that distance is closing as local models are already better than what we had just over a year ago in many cases. Same with Text-to-Image generation (Flux 2, Z-Image-Turbo, etc..) or text-to-speech (Parakeet, Whisper, etc..).
smilekzs
On a related note: Would love to see cyclomatic complexity as a supporting metric for readability. I increasingly find AI-adjacent and AI-written code suffering from this.
chengjunsuger
Nice project! But I wonder does less coding actually means less work for whoever reviews it?
KasianFranks
Nobody is going to care about how much code an AI system generates in the very near future, some even now, once everyone understands the scale at which a vector space truly operates at. Complexity is not much of an issue for an AI system.