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LM Evaluation Harness
The industry-standard LLM benchmark suite — evaluate any model on 60+ tasks from one CLI.
VS
At a Glance
| Attribute | LM Evaluation Harness | DeepEval |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 6 listed |
| Categories | LLM Evaluation | LLM Evaluation |
Key Features
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
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
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)
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
LM Evaluation Harness
huggingfaceopenaiwandbmlflow
DeepEval
openaianthropiclangchainllamaindexragaslangfuse
🏆 Which should you choose?
Choose LM Evaluation Harness if…
- → you're already in the LLM Evaluation ecosystem and prefer LM Evaluation Harness's workflow
Choose DeepEval if…
- → you're already in the LLM Evaluation ecosystem and prefer DeepEval's workflow