OpenCompass
OpenCompass is an open-source, one-stop platform for evaluating large language models and multimodal models. It aims to make evaluation objective, comprehensive, reproducible, scalable, and extensible through shared benchmarks, evaluation workflows, and public leaderboards.
At a Glance
- AI/ML researchers and universities
- Foundation-model and LLM developers
- Enterprise AI teams
- Open-source model communities
- +2 more
AI Tools by OpenCompass
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OpenCompass
Open Source LLM Evaluation Platform
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Latest News
OpenCompass v0.5.4 released with VLMEvalKit multimodal integration, multi-round inference, LiteLLM gateway support and expanded benchmarks
API model ecosystem expanded with OpenAI Responses API, LiteLLM AI Gateway, and updated Gemini and Anthropic SDK interfaces
Multi-round inference and Multi-IF support added for multi-turn instruction-following evaluation
OpenCompass v0.5.3 released with new benchmarks, RawPromptTemplate, and new model/API support
Products & Services
Open-source Python toolkit and workflow for configuring models and datasets, running inference, evaluating outputs, and summarizing results across LLM and multimodal benchmarks.
Native multimodal dataset loading, OpenAI-compatible inference, and official VLMEvalKit metrics within the OpenCompass workflow.
Public leaderboard for ranking public and API-accessible models using OpenCompass evaluations.
Benchmark-navigation and model-evaluation community service associated with the OpenCompass ecosystem.
Market Position
OpenCompass positions itself as a broad, modular, one-stop evaluation platform rather than a single benchmark. Its differentiation is the combination of 100+ datasets, support for both local and API models, multiple evaluators and inference backends, distributed/high-concurrency execution, multimodal integration through VLMEvalKit, and public CompassRank leaderboards. Comparable tools and ecosystems include EleutherAI's lm-evaluation-harness, Stanford CRFM's HELM, Hugging Face LightEval, and OpenAI's SimpleEvals.
Leadership
Founders
Songyang Zhang
Previously a Young Scientist at Shanghai AI Laboratory, Songyang Zhang says he initiated the OpenCompass project for foundation-model evaluation and analysis. He holds a PhD in Computer Science from the University of Chinese Academy of Sciences and previously worked as a postdoctoral researcher supervised by Dahua Lin.
Kai Chen
Kai Chen leads research and engineering groups at Shanghai AI Lab and lists OpenCompass among projects led by him. Before joining Shanghai AI Lab, he was a director at SenseTime leading computer-vision R&D teams; he earned his PhD at The Chinese University of Hong Kong.
Founding Story
The project was started to address fragmented, inconsistent, and difficult-to-scale evaluation of rapidly evolving foundation models. Its initial vision was a modular, open-source platform that could unify models, datasets, evaluators, inference backends, scheduling, result summarization, and public leaderboards.
Business Model
Revenue Model
Open-source software and research ecosystem distributed through GitHub and PyPI; the public materials describe free toolkit installation and community leaderboards rather than subscriptions, API usage fees, licenses, or other commercial monetization.
Target Markets
- AI/ML researchers and universities
- Foundation-model and LLM developers
- Enterprise AI teams
- Open-source model communities
- Benchmark and evaluation researchers
- Developers evaluating hosted model APIs
- Academic and industrial benchmarking of foundation models
- Regression testing and model release evaluation
- Comparing proprietary API models with local open-source models
- Evaluating LLM reasoning, coding, mathematics, science, safety and long-context abilities
- Vision-language and multimodal model evaluation
- Custom benchmark development and reproducible evaluation experiments