What is LM Evaluation Harness?
A Python-based evaluation framework that enables testing of language models against 60+ standard academic benchmarks with support for various model formats, APIs, and custom evaluation metrics.
LM Evaluation Harness pricing
We don't have LM Evaluation Harness's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Free and open source
What LM Evaluation Harness does
The capabilities that matter for ai evals testing, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source CLI
- LLM as a judge prompt grading framework
- -
- Specialized rag metrics faithfulness context relevance
- -
- Deterministic regex and json schema assertions
- -
- Synthetic test dataset generation from documents
- -
- Ci cd github actions pipeline blocking gates
- -
- Red teaming and adversarial vulnerability scanning
- -
- Multi model side by side ab regression testing
- ✓
- Human in the loop hitl annotation UI
- -
- Dashboard analytics for metric drift over time
- -
- SOC2 type ii
- -
- Mit or apache permissive oss license
- -
- Pricing model
- Free open source
Platform & deployment
Independently observed- CLI
- Self-hosted
Integrations (5)
Independently observed- Hugging Face
- PyTorch
- VLLM
- GitHub
- OpenAI-compliant APIs
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
LM Evaluation Harness alternatives
Other ai evals testing we track, ranked by the same independent score.
Compare LM Evaluation Harness
Side by side against other ai evals testing, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for LM Evaluation Harness, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 80 | 0.08 | 6.7 | ✓ |
| Price level | 100 | 0.05 | 5.2 | ✓ |
| Development activity | 54 | 0.09 | 5.1 | ✓ |
| Release cadence | 63 | 0.05 | 3.3 | ✓ |
| Dependent projects | 40 | 0.06 | 2.5 | ✓ |
| Capabilities | 29 | 0.08 | 2.4 | ✓ |
| Security score | 56 | 0.04 | 2.4 | ✓ |
| Integrations | 22 | 0.09 | 2.1 | ✓ |
| Stars | 78 | 0.03 | 2.0 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 50 | mediumsource · 2026-08-26 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: free_open_source · Architecture model: open_source_cli · Multi model side by side ab regression testing: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 5 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | MIT | highsource · 2026-08-26 · 95% |