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HELM
Stanford's holistic LLM benchmark — evaluate accuracy, fairness, bias, and efficiency together.
VS
At a Glance
| Attribute | HELM | OpenAI Evals |
|---|---|---|
| License / Pricing | Free | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.2/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 4 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
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
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)
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
Integrations
HELM
huggingfaceopenaianthropicwandb
OpenAI Evals
openailangsmithwandbdeepeval
🏆 Which should you choose?
Choose HELM if…
- → you want a managed or commercial offering with enterprise support and SLAs
Choose OpenAI Evals if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in