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OpenCompass

github.com

Free plan LinuxAPISelf-hosted
Visit github.com
Spec score 7.6 #15 of 29 in LLM Evaluation Tools

What it is

OpenCompass is a free, open-source Python platform for evaluating large language models across knowledge, reasoning, coding, science, language, long-context and safety datasets. It describes support for models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek and other providers, along with more than 100 datasets. Evaluations can use zero-shot, few-shot or chain-of-thought methods with standard or dialogue prompt templates. The system supports task division, distributed evaluation and data-parallel work across multiple GPUs. Configuration files record experiments, and results can be reported in real time. Its modular design can be extended with models, datasets, task-division strategies and cluster-management systems. OpenCompass evaluates API models as well as open-source models; installation documentation lists LMDeploy and vLLM inference backends and an API option. A VLMEvalKit integration supports multimodal dataset loading, OpenAI-compatible inference and VLMEvalKit metrics. It is intended for self-hosted use under the Apache-2.0 license. The README recommends Python 3.12, while pyext-backed code-evaluation datasets require Python 3.10. Some datasets and agent evaluations need extra dependencies that may conflict.

Spec sheet

Purpose
OpenCompass is an LLM evaluation platform for assessing models across datasets covering knowledge, reasoning, coding, science, language, long context, and safety.Read 7 Oct 2026 · github.com
Intended users
The project describes CompassHub and CompassKit as tools for researchers and practitioners evaluating large language and large vision-language models.Read 7 Oct 2026 · github.com
License
The GitHub repository identifies its license as Apache-2.0.Read 7 Oct 2026 · github.com
Founded
2020Read 28 Sep 2026 · github.com
Community resources
The project links to CompassHub for benchmark browsing, CompassRank for leaderboards, documentation, Discord, and WeChat.Read 7 Oct 2026 · github.com
Dataset caveat
The documentation says some datasets need extra dependencies, and some agent evaluations may require many dependencies that can conflict with existing environments.Read 7 Oct 2026 · opencompass.readthedocs.io
Distributed evaluation
It supports task division and distributed evaluation, including data-parallel evaluation across multiple GPUs.Read 7 Oct 2026 · github.com
Evaluation methods
It supports zero-shot, few-shot, and chain-of-thought evaluations with standard or dialogue prompt templates.Read 7 Oct 2026 · github.com
Experiment tracking
Configuration files record experiments, and the project supports real-time reporting of results.Read 7 Oct 2026 · github.com
Extensibility
Users can extend the modular system with models, datasets, task division strategies, and cluster management systems.Read 7 Oct 2026 · github.com
Inference integrations
The installation documentation lists LMDeploy and vLLM as inference backends and provides an API installation option.Read 7 Oct 2026 · opencompass.readthedocs.io
Installation requirements
The README recommends Python 3.12, while users relying on pyext-backed code evaluation datasets are instructed to use Python 3.10.Read 7 Oct 2026 · github.com
Model APIs
OpenCompass supports evaluating API models alongside open-source models and lists API integrations including OpenAI, Gemini, Claude, Qwen, and others.Read 7 Oct 2026 · github.com
Models and datasets
The project describes support for models from providers including OpenAI, Anthropic, Gemini, Qwen, GLM, and DeepSeek, and more than 100 datasets.Read 7 Oct 2026 · github.com
Multimodal support
The project says its VLMEvalKit integration enables multimodal dataset loading, inference through OpenAI-compatible APIs, and evaluation with VLMEvalKit metrics.Read 7 Oct 2026 · github.com

Plans and prices

OpenCompass Free Open-source software · Python package · Some datasets and inference backends require optional dependencies Read 7 Oct 2026 · github.com

Every OpenCompass plan and price →

Strong points and limits

Strong points

  • More than 100 datasets
  • Distributed multi-GPU evaluation
  • API and open-source model support
  • Experiment tracking and live reporting
  • Multimodal evaluation integration

Limits

  • Optional dependencies can conflict
  • Python requirements vary by dataset
  • Self-hosted setup is required

Who it suits

It suits researchers and practitioners evaluating language and vision-language models. Self-hosting users who need extensible datasets, distributed runs or API-model evaluation are the clearest fit.

Verdict

OpenCompass provides broad, extensible evaluation coverage with distributed execution and multimodal support. Installation requirements and optional dependency conflicts should be checked against each planned evaluation.

Where Specifiction places it

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