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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 | OpenAI Evals |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.2/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 4 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
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
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)
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
LM Evaluation Harness
huggingfaceopenaiwandbmlflow
OpenAI Evals
openailangsmithwandbdeepeval
🏆 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 OpenAI Evals if…
- → you're already in the LLM Evaluation ecosystem and prefer OpenAI Evals's workflow