OpenCompass
OpenCompass is a free, open-source platform for evaluating large language and multimodal models.
What OpenCompass is
OpenCompass is a free, open-source platform for evaluating large language and multimodal models. It supports more than 100 datasets across knowledge, reasoning, coding, science, language, long-context, and safety areas. Evaluation modes include zero-shot, few-shot, and chain-of-thought with standard or dialogue-style templates. The project describes distributed evaluation using more than 70 datasets and about 400,000 questions. Documented integrations cover OpenAI, Gemini, Claude, Qwen, Hugging Face, API models, open-source models, and custom models. LMDeploy and vLLM are listed inference backends. Users configure runs, perform inference and evaluation, and save CSV or TXT results; Lark can provide status updates. Models and datasets can be added, with customizable task division and cluster management. The Apache-2.0 project is self-hosted for Linux, Windows, and API workflows. Installation uses pip or source and requires PyTorch 1.13 or later. Some datasets need extra setup, and APPS, TACO, and LiveCodeBench code evaluations require pyext.
Good
- Supports more than 100 evaluation datasets
- Zero-shot, few-shot, and chain-of-thought modes
- Integrates API, open-source, Hugging Face, and custom models
- Exports results as CSV and TXT
- Apache-2.0 licensed and self-hosted
Watch out
- Requires PyTorch 1.13 or later
- Some datasets need additional dependencies or manual setup
- Certain code evaluations require pyext
- Windows compatibility depends on optional OpenCC
Who it suits
It suits researchers and practitioners evaluating language and vision-language models across broad capability and safety dimensions. Teams needing self-hosted, distributed, or customizable evaluation workflows may find its scope relevant.
Verdict
OpenCompass provides broad, extensible model evaluation for self-hosted workflows. Installation requirements and dataset-specific setup should be checked before adoption.
The facts
Each one read from OpenCompass's own pages, with the date it was checked.
What it does
- API support
- The installation guide documents API evaluation and lists dependencies for OpenAI, Anthropic, Qwen, and ByteDance Volcano Engine APIs.checked 4 Oct 2026
- Benchmarks
- The project describes support for more than 100 datasets covering areas such as knowledge, reasoning, coding, science, language, long context, and safety.checked 4 Oct 2026
- Community support
- The README links to Discord and WeChat and directs users to GitHub for reporting issues.checked 8 Oct 2026
- Compatibility limit
- The README says APPS, TACO, and LiveCodeBench code-generation evaluations are unavailable without pyext, and recommends Python 3.10 for those datasets.checked 8 Oct 2026
- Dataset limitation
- The basic installation supports most fundamental datasets, while some datasets require additional dependencies or manual setup.checked 4 Oct 2026
- Datasets
- OpenCompass publishes recommended configurations for supported evaluation datasets.checked 8 Oct 2026
- Distributed evaluation
- The project says it supports task division and distributed evaluation.checked 8 Oct 2026
- Evaluation dimensions
- Its documentation describes evaluations across language, knowledge, understanding, reasoning, safety, and specialized capabilities.checked 8 Oct 2026
- Evaluation methods
- It supports objective and subjective evaluation, including model-assisted judging and human-feedback-based judging.checked 8 Oct 2026
- Extensibility
- The project says users can add models and datasets and customize task division strategies and cluster management support.checked 4 Oct 2026
- Inference backends
- The README gives LMDeploy and vLLM as accelerated inference backend options.checked 8 Oct 2026
- Installation
- The project documents installation with pip and from source, and its README recommends using conda to manage the Python environment.checked 8 Oct 2026
- Intended users
- The project describes CompassHub and CompassKit for researchers and practitioners evaluating language and vision-language models.checked 4 Oct 2026
- License
- The GitHub repository links an Apache-2.0 license.checked 4 Oct 2026
- Model integrations
- Documented model integrations include OpenAI, Gemini, Claude, Qwen, and other API models, as well as open-source models.checked 4 Oct 2026
- Model support
- The README describes support for Hugging Face models, API models, and custom models.checked 8 Oct 2026
- Purpose
- OpenCompass is a platform and toolkit for evaluating large language models and large vision-language models.checked 8 Oct 2026
- Reporting
- The quick start says users can enable Lark reporting for status updates in Lark clients.checked 4 Oct 2026
- Requirements
- The installation guide states that OpenCompass requires PyTorch 1.13 or later.checked 4 Oct 2026
- Safety evaluation
- Its evaluation dimensions include fairness, legality, harmlessness, ethical considerations, and privacy protection.checked 8 Oct 2026
- Scale
- The project describes distributed evaluation and a scheme with 70+ datasets and about 400,000 questions.checked 4 Oct 2026
- Workflow and outputs
- The quick start describes configuring, running inference and evaluation, and saving results as CSV and TXT files.checked 4 Oct 2026
Where it runs
- Windows compatibility
- A release note says OpenCC was made optional to restore Windows installation compatibility.checked 8 Oct 2026
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