AGI-Eval is a specialized evaluation community designed to benchmark the capabilities and performance of various AI large language models.

An advanced evaluation system by H2O.ai that utilizes Elo rating methodologies to benchmark and rank Large Language Models (LLMs).

A professional evaluation benchmark from Fudan University’s NLP Lab designed to measure the performance and reliability of large language models.

A standardized, holistic evaluation framework from Stanford University designed to measure the performance and safety of large language models.

OpenCompass is an open-source evaluation framework developed by the Shanghai AI Lab to provide standardized, comprehensive benchmarking for large language models.

An open-source evaluation framework developed by the Beijing Academy of Artificial Intelligence (BAAI) to standardize and scale LLM benchmarking.

MMLU is a comprehensive benchmark designed to evaluate the general knowledge and problem-solving capabilities of large language models across a vast array of disciplines.

A comprehensive evaluation suite designed to assess the knowledge and capabilities of large language models (LLMs) specifically in the Chinese language.

A professional evaluation framework providing standardized benchmarks to measure the intelligence and utility of Chinese-language AI models.

A comprehensive evaluation benchmark designed to measure the general knowledge and linguistic capabilities of Large Language Models in Chinese.