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Tool Comparison
HELM
Stanford's holistic LLM benchmark — evaluate accuracy, fairness, bias, and efficiency together.
VS
At a Glance
| Attribute | HELM | DeepEval |
|---|---|---|
| License / Pricing | Free | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 6 listed |
| Categories | LLM Evaluation | LLM Evaluation |
Key Features
HELM
- Holistic metrics: accuracy, calibration, robustness, fairness, bias, efficiency
- 42 scenarios covering NLP, coding, reasoning, and knowledge tasks
- Standardized prompting methodology for fair model comparison
- Public leaderboard comparing GPT-4, Claude, Llama, Gemini, and more
- Modular scenario and metric system for custom evaluations
- Supports local models via HuggingFace and API models
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
HELM
Compare models for a regulated industry use case
Select HELM scenarios relevant to your domain (e.g. medical QA, legal reasoning)
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
HELM
huggingfaceopenaianthropicwandb
DeepEval
openaianthropiclangchainllamaindexragaslangfuse
🏆 Which should you choose?
Choose HELM if…
- → you want a managed or commercial offering with enterprise support and SLAs
Choose DeepEval if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in