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HELM
Stanford's holistic LLM benchmark — evaluate accuracy, fairness, bias, and efficiency together.
VS
At a Glance
| Attribute | HELM | LM Evaluation Harness |
|---|---|---|
| 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 | 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
LM Evaluation Harness
- 60+ built-in benchmarks: MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag
- Evaluate any HuggingFace model, OpenAI API, or local model
- Few-shot prompting with configurable shot count
- Parallelized evaluation across GPUs
- Used by Hugging Face Open LLM Leaderboard
- Custom task support via YAML config
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)
LM Evaluation Harness
Benchmark a fine-tuned model before deployment
Install the harness: pip install lm-eval
Add LLM capability regression tests to CI
Select a fast subset of tasks (e.g. hellaswag with 100 samples)
Integrations
HELM
huggingfaceopenaianthropicwandb
LM Evaluation Harness
huggingfaceopenaiwandbmlflow
🏆 Which should you choose?
Choose HELM if…
- → you want a managed or commercial offering with enterprise support and SLAs
Choose LM Evaluation Harness if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in