reasoning benchmark
OpenRCA
OpenRCA is a benchmark for evaluating AI models on root cause analysis tasks. For each failure case, the model receives 1 point if all generated root-cause elements match the ground-truth ones, and 0 points if any mismatch is identified. The overall accuracy is the average score across all failure cases.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
OpenRCA Ranking
Higher score ranks better on this benchmark.
| 01 | AN | 34.9% | 100.0% | 1 | C |
OpenRCA Highlights
The leading models and scores on this benchmark.
What is OpenRCA?
What OpenRCA measures and how its scores work.
OpenRCA is a benchmark for evaluating AI models on root cause analysis tasks. For each failure case, the model receives 1 point if all generated root-cause elements match the ground-truth ones, and 0 points if any mismatch is identified. The overall accuracy is the average score across all failure cases.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- OpenRCA
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- openrca|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about OpenRCA.
Which model scores highest on OpenRCA?
Claude Opus 4.6 is currently ranked first with 34.9%.
What does OpenRCA measure?
OpenRCA is a benchmark for evaluating AI models on root cause analysis tasks. For each failure case, the model receives 1 point if all generated root-cause elements match the ground-truth ones, and 0 points if any mismatch is identified. The overall accuracy is the average score across all failure cases.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.