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OpenAI Evals
OpenAI's open-source framework for building and running custom LLM evaluations.
VS
At a Glance
| Attribute | OpenAI Evals | DeepEval |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.2/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 6 listed |
| Categories | LLM Evaluation | LLM Evaluation |
Key Features
OpenAI Evals
- Built-in eval types: match, includes, fuzzy match, model-graded
- Model-graded evals for open-ended responses
- Custom eval definition via YAML
- Eval registry with hundreds of community-contributed benchmarks
- Compare performance across model versions
- Integration with OpenAI API for automated scoring
DeepEval
- Pytest-compatible — run with deepeval test run or pytest
- 20+ metrics: correctness, hallucination, faithfulness, bias, toxicity
- RAG-specific metrics: context relevancy, contextual recall, RAGAS
- LLM-as-judge using GPT-4o or a custom evaluator model
- Confident AI platform for evaluation result dashboards
- Red teaming module for safety and jailbreak testing
Real-World Use Cases
OpenAI Evals
Measure quality before upgrading model versions
Define eval tasks from your real production use cases
Build a domain-specific benchmark
Collect 50–100 representative queries from your application logs
DeepEval
Write unit tests for your LLM application
Define test cases with input, actual_output, and expected_output
Red team your LLM for safety issues
Use DeepEval's red teaming module to generate adversarial prompts
Integrations
OpenAI Evals
openailangsmithwandbdeepeval
DeepEval
openaianthropiclangchainllamaindexragaslangfuse
🏆 Which should you choose?
Choose OpenAI Evals if…
- → you're already in the LLM Evaluation ecosystem and prefer OpenAI Evals's workflow
Choose DeepEval if…
- → you're already in the LLM Evaluation ecosystem and prefer DeepEval's workflow