PinchBench is a model evaluation and selection comparison platform whose core purpose is to help users find the most suitable AI model for OpenClaw agents.
Purpose PinchBench conducts evaluations around OpenClaw agents, targeting users who need to choose an underlying large language model for AI coding agents. It compares the performance of different models through real coding tasks rather than relying solely on general leaderboards, thereby making selection closer to actual agent usage scenarios.
Main Capabilities The platform covers more than 100 large language models, with comparison dimensions including success rate, speed, and cost. It provides multiple "best model for common use cases" leaderboards, specifically including: the model with the highest average score across multiple benchmark runs; the highest average scorer among open-weight models; the highest average scorer among open-weight models with no more than 125B total parameters; the shortest observed full benchmark runtime; the lowest observed non-zero benchmark run cost; the highest success rate per dollar; the highest scorer on coding and DevOps tasks; and the highest scorer on data analysis tasks.
The platform also provides "success rate by model" data, showing the percentage of tasks each model successfully completes in standardized OpenClaw agent tests. Scoring is completed jointly by automated checks and LLM review. The page provides a "How we benchmark" entry and a "View all tasks" entry, allowing further viewing of the evaluation methodology and all tasks.
How to Use Users can directly visit the PinchBench website to view various leaderboards and success rate data, and choose a model according to their own use case (such as general performance, open weights, parameter size limits, runtime, cost, coding and DevOps, data analysis, etc.). The platform itself is an evaluation and comparison site; the materials do not state whether registration or installation is required, nor do they provide specific operating steps.
Applicable Scenarios It is suitable for developers and teams that are using or planning to use OpenClaw agents and need to choose or change the underlying AI model; it is also suitable for users who care about success rate, runtime speed, and cost trade-offs of models on real coding tasks, as well as scenarios that require selecting models for specific tasks such as coding, DevOps, or data analysis.
Limitations The materials do not state whether PinchBench is paid, whether it provides an API, whether it supports agents other than OpenClaw, nor do they provide the specific number of evaluation tasks, model list, or update frequency. Scoring is completed through automated checks and LLM review, but specific evaluation details need to be viewed on its "How we benchmark" page. PinchBench's hosting and inference are sponsored by Kilo, and the page includes promotional content for Kilo.
