Side by side
OpenCompass vs Pydantic Evals
Both stand on ai llm evaluation tools. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Free plan APILinuxSelf-hosted+1
OpenCompass offers broad model and dataset coverage with configurable evaluation methods and distributed workflows. Check dataset dependencies and the pyext limitation before planning code-generation evaluations.
Visit OpenCompass Free plan Linux
Pydantic Evals provides a code-first way to check AI outputs and agent behavior, including against live traffic. Deterministic checks avoid the stated cost of LLM judges, which can be slower and non-deterministic.
Visit Pydantic EvalsThe facts
| OpenCompass | Pydantic Evals | |
|---|---|---|
| Free plan | Yes | Yes |
| Open source | Not published | Not published |
| Runs on | API, Linux, Self-hosted, Windows | Linux |
| Lowest paid plan | Not published | Not published |
| Plans published | None | None |
| Maker | Not published | Not published |
| Website | opencompass.org.cn | pydantic.dev |
| On ai llm evaluation tools | #23 of 30 | #27 of 30 |
OpenCompass: what it does well
- More than 100 supported datasets
- Zero-shot, few-shot, and chain-of-thought modes
- Supports API, Hugging Face, and custom models
- CSV and TXT result output
- Safety evaluation dimensions included
What to know first
- Requires PyTorch 1.13 or later
- Some datasets need extra setup
- Several code benchmarks require pyext
- Recommended Python 3.10 for those datasets
Pydantic Evals: what it does well
- Free and self-hosted.
- Evaluates final outputs and tool-call trajectories.
- Supports deterministic checks, custom evaluators, and LLM judges.
- Can grade every production call or a sampled subset.
What to know first
- LLM judges can take seconds and cost money.
- LLM judge results can be non-deterministic.
- Ragas and DeepEval adapters require optional dependencies.
Alternatives to OpenCompass Alternatives to Pydantic Evals The whole list