Introduction to the Model Evaluation Frameworks

CSGHub provides four mainstream model evaluation frameworks: lm-evaluation-harness, OpenCompass, EvalScope, and ClawEval, each with its own characteristics and application scenarios.

lm-evaluation-harness

1. Introduction

   • A Python tool provided by EleutherAI for unified evaluation of language model performance.
   • Supports various evaluation tasks, such as Language Modeling, Zero-Shot, and Few-Shot tasks.

2. Features

   • Flexibility: Supports integration with various models, including Hugging Face's Transformers models, OpenAI's API models, etc.
   • Task Diversity: Covers multiple NLP tasks, such as text generation, cloze tests, question answering, translation, etc.
   • Standardized Evaluation: Provides a fair performance comparison for models through a unified interface.

3. Supported Models

   • Models from the Hugging Face Transformers library, such as GPT-2, GPT-3, T5, BERT, etc.
   • OpenAI API models (e.g., GPT-3.5, GPT-4).
   • Custom models (requires users to provide suitable API interfaces or loading logic).

4. Applicable Scenarios

   • Performance evaluation of language models in research projects.
   • Benchmarking against existing models, especially for comparative experiments in LLM research.

OpenCompass

1. Introduction

   • OpenCompass is an open-source evaluation framework designed specifically for the performance evaluation needs of domestic and international large language models.
   • Maintained by domestic developers and the open-source community, it supports in-depth evaluation of Chinese corpora and tasks.

2. Features

   • Localization Support: Especially suitable for evaluation tasks in Chinese NLP, supporting a large number of Chinese benchmark datasets (e.g., CLUE).
   • Multi-Model Compatibility: Supports Hugging Face models, open-source LLMs, and commercial models via API (e.g., Baidu Wenxin, Alibaba Tongyi).
   • Scalability: Users can easily add custom tasks or datasets.

3. Supported Models

   • Hugging Face's Transformer models.
   • Open-source LLMs from both domestic and international sources, such as ChatGLM, MOSS, LLaMA, Falcon.
   • Commercial models providing APIs, such as Baidu Wenxin, Alibaba Tongyi, iFlytek Xinghuo, etc.

4. Applicable Scenarios

   • Evaluation of Chinese tasks (e.g., text classification, question generation).
   • Comparing the performance differences of domestic and international large language models on Chinese tasks.

Evalscope

1. Introduction

   • EvalScope is a model evaluation and performance benchmarking framework developed by the ModelScope community, providing a one-stop solution for your model evaluation needs.
   • Maintained by domestic developers and the open-source community, it supports in-depth evaluation of Chinese corpora and tasks.

2. Features

   • Localization Support: Built-in multiple industry-recognized testing benchmarks and evaluation metrics: MMLU, CMMLU, C-Eval, GSM8K, etc.
   • Multi-Model Compatibility: Supports Hugging Face models, open-source LLMs, and commercial models via API (e.g., Baidu Wenxin, Alibaba Tongyi).
   • Scalability: Users can easily add custom tasks or datasets.

3. Supported Models

   • Large language models.
   • Embedding models.
   • Multi-modal models.
   • AIGC models.

ClawEval

1. Introduction
   • Claw-Eval is an open-source evaluation benchmark and test suite specifically designed to evaluate the general capabilities of LLM Agents (Evaluation Harness for LLM Agents).
   • Dedicated to providing rigorously reproducible, human-verified high-standard benchmarks to comprehensively measure models' overall planning and execution capabilities in general agentic tasks.
2. Features
   • Human-Verified Task Set: Covers 300 realistic, human-verified Agent tasks across 9 core dimensions including perception, reasoning, creation, and delivery.
   • Strict Pass^3 Metric: Requires a model to succeed across all 3 independent trials to be counted as passed, eliminating single "lucky runs" and ensuring evaluation robustness and authenticity.
   • Deep Integration with EvalScope: Natively integrated with the EvalScope framework, enabling end-to-end evaluation of OpenAI-compatible model endpoints via CLI or Python.
   • Real Web Agent Interaction Support: Provides unified environments and tools for Agent tasks requiring web browsing and information retrieval, ensuring stable task execution.
3. Supported Models
   • Models providing OpenAI-compatible APIs (including dedicated inference endpoints and Serverless APIs deployed on CSGHub).
   • Mainstream open-source foundation models (such as Qwen, DeepSeek, Llama, etc.) and commercial LLM APIs.
4. Applicable Scenarios
   • Evaluating LLMs' end-to-end execution capabilities in real multi-step Agent tasks, complex reasoning/planning, and tool usage.
   • Measuring the reliability and task completion rates of models serving as LLM Agent foundations.

Comparison of Frameworks

Features	lm-evaluation-harness	OpenCompass	Evalscope	ClawEval
Task Scope	Global tasks (mainly English)	Focus on Chinese tasks	Comprehensive multimodal and Chinese benchmarks	General Agent capabilities (300 real tasks across 9 domains)
Model Support	Open-source + API models	Open-source + domestic commercial models	Open-source + domestic commercial models	OpenAI-compatible APIs + CSGHub Endpoints + Open-source models
Applicable Scenarios	Academic research, English-focused model performance comparison	Chinese task evaluation, domestic and international model performance comparison	Performance benchmarking for Chinese and multimodal models	LLM Agent general capabilities, planning & execution, and Pass^3 robustness evaluation
Evaluation Features / Metrics	Standard NLP Benchmark	Rich localized Chinese task sets	Full-stack evaluation for LLM / Multimodal / AIGC	300 real tasks, strict Pass^3 evaluation, and native EvalScope integration

Applicable Scenarios

• If your evaluation task is primarily in English or needs to benchmark against global LLMs, it is recommended to use lm-evaluation-harness.
• If your evaluation task is primarily in Chinese or involves domestic commercial models, it is recommended to use OpenCompass.
• If your evaluation models are diverse (including Multimodal, Embedding, AIGC, etc.), it is recommended to use Evalscope.
• If you need to systematically evaluate LLMs' agentic capabilities, multi-step planning & execution, and real-world task completion reliability, it is recommended to use ClawEval.
• The model evaluation systems of the above four frameworks can be used in combination to comprehensively cover the performance comparison needs of models across different languages, modalities, and complex tasks.