OpenCompass: A Universal Evaluation Platform for Large Language Models
Abstract
In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the rapid iteration of LLMs, objective, quantitative, and comprehensive evaluation of their capabilities has become a critical link in advancing technological development. Currently, the mainstream static benchmark dataset-based evaluation methods face challenges such as the diversity of task types, inconsistent evaluation criteria, and fragmentation of data and processing workflows, making it difficult to efficiently conduct cross-domain and large-scale model evaluation. To address the aforementioned issues, this paper proposes and open-sources OpenCompass, a one-stop, scalable, and high-concurrency-supported general-purpose LLM evaluation platform. Adhering to the design philosophy of modularization and component decoupling, the platform boasts three core advantages: high compatibility, flexibility, and high concurrency. The core architecture of OpenCompass comprises five key components: the Configuration System, Task Partitioning Module, Execution and Scheduling Module, Task Execution Unit, and Result Visualization Module. Its workflow provides rule-based, LLM-as-a-Judge, and cascaded evaluators to adapt to the requirements of different task scenarios. Supporting mainstream benchmark datasets across multiple domains, including knowledge, reasoning, computation, science, language, code, etc., the platform offers a unified and efficient LLM evaluation tool for both academia and industry, facilitating the accurate identification of strengths and weaknesses of LLMs as well as their subsequent optimization.
Cite
@article{arxiv.2605.19276,
title = {OpenCompass: A Universal Evaluation Platform for Large Language Models},
author = {Maosong Cao and Kai Chen and Haodong Duan and Yixiao Fang and Zhiwei Fei and Tong Gao and Ge Jiaye and Mo Li and Hongwei Liu and Junnan Liu and Yuan Liu and Chengqi Lyu and Han Lyu and Ningsheng Ma and Zerun Ma and Yu Sun and Zhiyong Wu and Linchen Xiao and Jun Xu and Haochen Ye and Zhaohui Yu and Yike Yuan and Songyang Zhang and Yufeng Zhao and Fengzhe Zhou and Peiheng Zhou and Dongsheng Zhu and Lin Zhu and Jingming Zhuo},
journal= {arXiv preprint arXiv:2605.19276},
year = {2026}
}