slopbench

[DarkHater01]

🏷️ Discussion Type

Product Feedback

Body

SlopBench
Code benchmarks measure if a model can solve a task. None measure how much code it wastes.

I got tired of reviewing AI-generated PRs that were technically correct but 5x larger than they needed to be. 🤯

So I built SlopBench: an open-source benchmark that measures code inflation in LLM agents.

The premise is simple. Every model solves the same 5 Python tasks inside a sandboxed Docker environment. The metric is inflation factor = generated LOC ÷ LOC of a hand-written reference solution that passes the exact same tests.

The twist: every model runs twice.
→ Once with a "vanilla" system prompt.
→ Once with a "minimalism" system prompt.

The difference in LOC is how much a simple prompt change shaves off. It's the cheapest lever against AI bloat that any team can pull today, without swapping models.

Here's what nobody else is publishing: inflation is only counted for runs that pass the tests. A 3-line solution that fails 4 out of 5 tries doesn't qualify. See ADR-001 in the repo for the full rationale.

Results update weekly via GitHub Actions. Every raw run is committed. Reference solutions are in-repo and contestable via PR — the denominator is public on purpose.

If you use AI agents to write code, this number matters to you.
repo :



https://github.com/DarkHater01/slopbench

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[queenofcorgis]

Hi @DarkHater01, thanks for sharing with the community!

Unfortunately, we currently do not allow self-promotion, or asking for stars in Community Discussions. We want to make sure there is space for users to ask questions without overwhelming them with other conversations. Thank you for helping us maintain a productive and tidy community for all our members. If you want me to reopen this discussion, please edit it to remove the stars request and ask for some feedback!