PinchBench is a benchmarking system for evaluating LLM models as OpenClaw coding agents, using real-world tasks to measure performance metrics.

How are tasks graded in PinchBench?

Tasks are graded automatically, by an LLM judge, or both, ensuring both objective and nuanced evaluation.

How can I submit results to the PinchBench leaderboard?

Run the benchmark with the provided commands, and results can be uploaded automatically or manually using an API token for official submissions.

Is PinchBench open source?

Yes, PinchBench is fully open source, with code available on GitHub for the benchmark runner, tasks, leaderboard, and API.

Who created PinchBench?

PinchBench was made by Kilo Code, the makers of KiloClaw, to help users choose from over 500+ AI models for their Claw agents.

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What is PinchBench

PinchBench is a benchmarking system designed to evaluate Large Language Models (LLMs) as coding agents for OpenClaw. It executes a standardized set of real-world tasks across various models and assesses them based on success rate, speed, and cost. This helps developers and organizations select the most suitable model for their specific use cases, ensuring optimal performance and efficiency.

Key Features

Benchmarks LLM models with real-world tasks instead of synthetic tests.

Measures multiple metrics: success rate, speed, and cost.

Features a public leaderboard for comparing results.

Open source with community contributions for tasks and improvements.

Supports automated and LLM-judged grading for nuanced evaluation.

Use Cases

Developers selecting the best LLM model for their OpenClaw-based AI agents.
AI researchers comparing model performance in practical coding scenarios.
Companies optimizing cost and efficiency of AI-driven automation tools.
Educators or trainers demonstrating real-world AI agent capabilities.
OpenClaw users making data-driven decisions when configuring agents.

Why do startups need this tool?

Startups need PinchBench to efficiently choose the right AI model for their OpenClaw agents, balancing cost, speed, and success rate to optimize resources. It enables data-driven decisions, helping startups avoid trial-and-error and select models that best fit their specific use cases, leading to improved productivity and reduced operational expenses.

FAQs

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