OpenCompass

At a glance

• Editor score
  7.3 / 10
• Pricing
  Open source
• Best for
  Researchers comparing models across benchmarks
• Paid from
  Free
• Safety evaluations
  Yes
• Facts checked
  22 Sep 2026

• Where it wins

  • Supports objective, subjective, and LLM-as-a-judge evaluations
  • Evaluates Hugging Face, API-based, and custom models
  • Combines distributed inference, metrics, visualization, and leaderboards

• Where it doesn't

  • Self-hosted deployment requires local or source-based installation
  • Configuration-driven workflows may require technical setup
  • Teams seeking a managed commercial service should consider another tool

Our verdict on OpenCompass

OpenCompass is an open-source evaluation platform for large language models and multimodal models. It is designed for researchers, engineers, and teams benchmarking models locally or through supported APIs. The toolkit covers language, knowledge, reasoning, coding, long-context understanding, and safety evaluations. It supports Hugging Face models, API-based models, custom models, and named model families including OpenAI, Anthropic, Gemini, Qwen, GLM, and DeepSeek. Its self-hosted deployment model suits teams that want control over evaluation workflows and execution environments.

Its strongest capability is evaluation depth. OpenCompass supports objective evaluation through discriminative and generative methods, alongside subjective evaluation using pairwise comparison and scoring modes. LLM-as-a-judge workflows add another way to assess model outputs. Configurable prompt templates support prompt engineering, while metrics can cover accuracy, BLEU, ROUGE, pass@k, toxicity, and other measures. Distributed evaluation and parallel inference are relevant for teams comparing multiple models or running larger benchmark workloads.

OpenCompass also extends beyond metric calculation with result visualization, report generation, and leaderboard publishing. That makes it suitable for structured benchmark projects where results need to be reviewed, presented, or compared over time. The trade-off is operational: the product is self-hosted and installed locally or from source, so teams remain responsible for setup and execution. Researchers and engineering teams that need broad, configurable model evaluation should consider OpenCompass. Teams looking for a managed commercial service or a narrowly focused evaluation workflow should choose another option.

Your product? Our editors can test OpenCompass in depth and publish a review, labelled as paid · from $149/mo →

OpenCompass pricing

OpenCompass fact sheet

Alternatives to OpenCompass

• DeepEvalA code-first toolkit for testing LLM apps, agents, tools, safety, and regressions.9.4
• Arize PhoenixA free, open-source toolkit covering traces, evaluations, prompts, datasets, and costs.9.3
• PromptfooA broad evaluation and security toolkit for teams testing AI systems in development.9.2

See all OpenCompass alternatives →

Featured on iTechGuides

OpenCompass is listed in our AI LLM Evaluation Tools directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update