open-compass/opencompass

What it solves

OpenCompass provides a unified, one-stop platform to evaluate the quality and effectiveness of large language models (LLMs) and vision-language models. It eliminates the need to manage disparate evaluation scripts by integrating numerous datasets and model configurations into a single workflow.

How it works

The platform manages a complete evaluation pipeline that includes dataset and model configuration, inference, evaluation, and result summarization. It supports both local open-source models and API-based models using various inference backends like vLLM, LMDeploy, and HuggingFace. The system can handle different evaluation strategies—such as zero-shot, few-shot, chain-of-thought, and LLM-as-judge—and supports distributed execution to handle large-scale workloads.

Who it’s for

It is designed for researchers and engineers who need to benchmark NLP and multimodal models against standardized datasets to assess capabilities in reasoning, coding, science, and safety.

Highlights

• Extensive Integration: Supports over 100 datasets and a wide array of models from providers like OpenAI, Anthropic, Gemini, DeepSeek, and Qwen.
• Multimodal Support: Integrates VLMEvalKit to evaluate both text-only and vision-language datasets in one workflow.
• Flexible Inference: Compatible with multiple backends (vLLM, LMDeploy, HuggingFace) and supports concurrent or distributed execution.
• Diverse Evaluation Methods: Includes rule-based evaluation, LLM-as-judge, and a CascadeEvaluator for sequential evaluation pipelines.