A professional evaluation framework designed to rigorously test Large Language Models across Chinese comprehension, coding, reasoning, and tool-calling dimensions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install model-benchmark
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install model-benchmark using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Model Benchmark Framework?
The Model Benchmark skill is a specialized framework built for the Openclaw ecosystem to provide deep insights into how different AI models perform in real-world scenarios. It moves beyond generic benchmarks by focusing on the practical execution of tasks within the Matrix Agent environment, ensuring developers can select the optimal provider for their specific needs.
By utilizing a multi-dimensional scoring system, this skill evaluates models based on weighted criteria including Chinese linguistic nuance, Python coding efficiency, complex multi-step reasoning, and the critical ability to invoke external tools. It provides a standardized environment to compare industry leaders like GLM-5, Qwen3, and Claude Opus 4 side-by-side.
Model Benchmark Framework Use Cases
- Comparing different LLM providers to determine the most cost-effective or high-performance model for a specific project.
- Validating the coding proficiency of new model releases before integrating them into production Openclaw Skills.
- Testing the tool-calling reliability of agents to ensure complex workflows won't fail due to poor instruction following.
- Benchmarking response latency and reasoning depth for time-sensitive or logic-heavy applications.
How Model Benchmark Framework Works
- Configuration: The user defines the target models and API keys within the Openclaw models.json configuration file.
- Environment Setup: The benchmark initializes the Matrix Agent environment with specific context windows and proxy settings.
- Test Execution: The framework runs a battery of standardized tests covering Chinese comprehension, Python coding, tool-calling theory, and logical reasoning.
- Performance Monitoring: It tracks the response speed and accuracy for every prompt submitted to the model pool.
- Scoring and Analysis: Each response is graded against predefined criteria (e.g., word count limits, logic correctness, code runnability).
- Report Generation: A comprehensive markdown report is generated, providing a final score out of 10 and a breakdown of strengths and weaknesses.
Model Benchmark Framework Setup
To begin benchmarking with Openclaw Skills, you must first configure your providers in the local environment:
# Navigate to your Openclaw configuration
cd ~/.openclaw/configs
# Edit models.json to add your API keys for GLM-5, Qwen, or Claude
nano models.json
# Run the benchmark skill within the Matrix Agent
openclaw run model-benchmark
Model Benchmark Framework Data Schema & Taxonomy
The skill organizes evaluation data into a structured format to ensure longitudinal tracking of model improvements over time:
| Data Type | Description | Format |
|---|---|---|
| Test Library | Standardized prompts for consistent measurement | Markdown/JSON |
| Scoring Rubric | Weighted criteria (25% Code, 20% Reasoning, etc.) | Internal Logic |
| Performance Metrics | Latency and token usage statistics | Table |
| Evaluation Report | Final analysis including total score and highlights | Markdown (MD) |
Model Benchmark Framework Advanced Features
- Multi-Provider Support: Compare models from MiniMax, Zhipu, Alibaba, and Anthropic simultaneously.
- Weighted Dimension Analysis: Customize weights for different evaluation pillars like Chinese understanding (25%) or response speed (10%).
- Thinking Mode Toggle: Test models with internal chain-of-thought (Thinking) enabled or disabled to measure the impact on accuracy.
- Standardized Report Templates: Automatically generate structured feedback suitable for developer documentation or stakeholders.
SKILL.md
Loading
Related Openclaw Skills
Construction Meeting Minutes Generator
A professional documentation skill that converts unstructured site notes and voice transcripts into legally robust, structured construction meeting minutes.
Pretix
A comprehensive integration for managing event ticketing, attendee data, and invoices via the Pretix platform.
yidianzixun
A specialized automation skill for organizing public channel structures, topic entries, and distribution information from the Yidian Zixun news platform.
Trending Longboard Routes & Guides
A specialized tool for discovering trending longboard routes, providing detailed navigation, safety data, and photography spots for skating enthusiasts.
SuperSaaS
A professional AI integration that enables coding agents to programmatically manage SuperSaaS appointments, resources, and user data.
One Number Explains Video Generator
A specialized video generation skill for creating vertical data-stat hooks that use a single hero metric to explain complex trends through high-impact motion graphics.
Featured*
