PinchBench - Find the best AI model for your OpenClaw
by•
PinchBench is a benchmarking system for evaluating LLM models as OpenClaw coding agents. We run the same set of real-world tasks across different models and measure success rate, speed, and cost to help developers choose the right model for their use case.
PinchBench is made with 🦀 by Kilo Code, the makers of KiloClaw.
Replies
Features.Vote
the "focus on what your agent actually does, not keeping it alive" framing hits different when you've actually tried to self-host something like this. the infrastructure part isn't just tedious. it becomes the thing that distracts you from the whole reason you set it up
the pinchbench benchmarking layer is the underrated part here. most people pick a model based on vibes or generic leaderboards that aren't specific to their workflows. having real-world task data for openclaw use cases specifically changes what "best model" even means
Benchmarking for coding agents specifically is a gap that's needed filling for a while — general LLM leaderboards like MMLU or HumanEval don't really capture how a model behaves when it's autonomously navigating a codebase, handling multi-step edits, or recovering from its own mistakes.
Curious about the task selection methodology though — who decides what counts as a "real-world task" and how do you prevent the benchmark from quietly overfitting to patterns that favor certain models? That's usually where these evaluations lose credibility over time.
Also wondering if results are broken down by task type — refactoring vs. greenfield vs. debugging likely surfaces very different model strengths, and a single success rate flattens that nuance.