openai/evals vs confident-ai/deepeval
confident-ai/deepeval scores higher overall: 80/100 (A-tier) against 68/100 (B-tier). openai/evals leads on adoption; confident-ai/deepeval leads on reliability, skill leverage, documentation. Scored 2026-07-07, methodology v1.0.
Dimension by dimension
| Dimension | openai-evals | deepeval |
|---|---|---|
| Reliability | 66 | 76 |
| Skill Leverage | 68 | 80 |
| Documentation | 72 | 82 |
| Maintenance | 45 | 90 |
| Safety / Governance | 62 | 66 |
| Evaluation Readiness | 84 | 92 |
| Composability | 68 | 80 |
| Adoption (capped) | 84 | 82 |
| Overall | 68 · B | 80 · A |
The entries
OpenAI's original eval framework and benchmark registry. Historically foundational; maintenance has visibly slowed as evaluation moved into…
A pytest-style LLM evaluation framework: metric classes for correctness, hallucination, RAG quality, and agent task completion, written as…
Frequently asked questions
openai/evals or confident-ai/deepeval?
confident-ai/deepeval scores higher overall (80 vs 68, methodology v1.0). But they are the same category, so the dimension table below is the real answer.
How is this comparison generated?
Both scorecards come from the same public rubric with evidence notes, scored by the same editorial process. This page presents them side by side; it adds no new judgments beyond the scores themselves.