Opencompass
Comprehensive open-source AI evaluation platform for LLM and VLM benchmarking.
OpenCompass is the go-to open-source evaluation suite for LLM and VLM benchmarking, with the broadest model and dataset support. It's a must-have for researchers and developers who need standardized comparisons, though it demands technical setup. A solid choice for anyone serious about model evaluation.
- LLM researchers conducting standardized model benchmarking
- AI developers evaluating model performance across diverse tasks
- Model vendors seeking objective, third-party evaluation
- Academic labs performing reproducible experiments
- Users seeking a production monitoring or real-time inference solution
- Non-technical users expecting a no-code interface
- Those needing private evaluation without any data sharing
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In short
Opencompass — Comprehensive open-source AI evaluation platform for LLM and VLM benchmarking. Best for LLM researchers conducting standardized model benchmarking, AI developers evaluating model performance across diverse tasks, Model vendors seeking objective, third-party evaluation. Free to use.
Viability Score
How likely is Opencompass to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Supports 100+ evaluation datasets
- Compatible with 50+ LLMs including GPT-5, DeepSeek, Claude, Qwen, Kimi
- Covers VLM evaluation with multimodal dimensions
- Standardized metrics (accuracy, BLEU, ROUGE, etc.)
- Automated report generation with visualization
- Online and offline evaluation modes
- Multi-task and multi-model comparison
- Safety and bias evaluation capabilities
- Custom dataset and model integration via plugin
- Community leaderboard for model rankings
- Instruction following and agent evaluation
- AI4S (AI for Science) evaluation dimension
- Spatial awareness and multimodal reasoning evaluation
- Open-source with active community development
About Opencompass
OpenCompass (司南) is an open-source evaluation platform designed to provide a standardized, reproducible, and scalable framework for benchmarking large language models (LLMs) and vision-language models (VLMs). It supports a wide range of models including GPT-5, DeepSeek-V4, Claude Opus 4.7, Qwen3, Kimi-K2.6, and many more. The platform covers diverse evaluation dimensions: language, reasoning, knowledge, mathematics, coding, instruction following, agents, multimodal perception, spatial awareness, and AI4S (AI for Science). With over 100 datasets and both online and offline evaluation modes, OpenCompass offers automated report generation, visualization, and a community leaderboard. It is tailored for AI enthusiasts, developers, model vendors, and academic institutions seeking objective, trustworthy model comparisons. The platform's modular design allows easy integration of new models and datasets, making it a flexible tool for comprehensive AI evaluation. OpenCompass is free and open-source, community-driven, and actively maintained.
Behind the Verdict
OpenCompass stands out for its breadth: it supports the latest models like GPT-5.4, DeepSeek-V4-Pro, and Claude Opus 4.7, and covers evaluation dimensions from basic language tasks to agents and scientific reasoning. The open-source nature means you can extend it with custom datasets and models. We'd reach for this when we need a standardized, reproducible benchmark for comparing multiple models in research or development. It's also great for model vendors who want to showcase performance on a trusted leaderboard. Where it bites: setup requires some technical chops – there's no no-code UI for casual users. It's not a production monitoring tool; it's built for offline evaluation, not real-time latency metrics. Compared to LMSys Chatbot Arena, OpenCompass offers more structured, objective benchmarks with multiple dimensions, while Arena focuses on human preference voting. For researchers needing granular, reproducible evaluation across many tasks, OpenCompass is the stronger pick. Casual users may prefer the simplicity of proprietary evaluation tools like those from OpenAI or Hugging Face, but OpenCompass's openness and community support give it an edge in flexibility.
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Use Cases
- Benchmark your fine-tuned Llama3 model against GPT-4 on 20 reasoning datasets
- Compare safety scores across multiple open-source LLMs using built-in adversarial prompts
- Automate weekly evaluation of your internal chatbot model with custom metrics
- Generate a comprehensive report for your enterprise to demonstrate model readiness
- Integrate OpenCompass into your CI/CD pipeline to catch performance regressions
- Train students on reproducible LLM evaluation methodology using standardized tasks
Models Under the Hood
Limitations
- As an open-source platform, OpenCompass lacks dedicated customer support and SLAs.
- The evaluation process can be resource-intensive, requiring significant compute for large models.
- Integration with proprietary models may require manual configuration.
- The platform focuses on offline evaluation and does not provide real-time monitoring.
12-month cost
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Integrations
Resources & Guides
- Documentationopencompass.org.cn
Get Started · Opencompass
Full product docs from opencompass.org.cn
- Quickstartopencompass.org.cn
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- Documentationopencompass.org.cn
Evaluation · Opencompass
Full product docs from opencompass.org.cn
- Documentationopencompass.org.cn
Custom Dataset · Opencompass
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