OpenRCA
Agents find the root cause of 335 software failures from enterprise telemetry logs, metrics and traces. Scored on component, start time and reason.
Published at ICLR 2025. The data covers telecom, banking and online marketplace systems.
- Made by
- Microsoft, CUHK-Shenzhen and Tsinghua University (Xu et al.)
- Kind
- Data science and SQL
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- Claude Opus 4.6 #37 on the capability index
- Last in a system card
- February 2026
- Last model added
- February 2026
Claude Opus 4.6 leads with 34.9%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Claude Opus 4.6 34.9%
- 2 Claude Sonnet 4.6 27.9%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.6 | 34.9% | Feb 2026 | Lab model card | |
| 2 | Claude Sonnet 4.6 | 27.9% | Feb 2026 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
2 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Feb 17, 2026 | Claude Sonnet 4.6 system card | 27.9 | OpenRCA overall, adaptive thinking, high effort, 3-run average; 26.4 at max effort (Table 2.4.A) | Capabilities |
| Feb 5, 2026 | Claude Opus 4.6 system card | 34.9 | OpenRCA overall, 3-run average, author's agent harness (Table 2.6.A); Market 33.6, Banking 37.3, Telecom 32.7 | Capabilities |
More in Coding
- SWE-bench Verified In use 500 human-validated GitHub issues from 12 Python repos. Agent writes a patch that must pass hidden tests. Claude Opus 4.7 83.5%
- LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests.
DeepSeek-V4-Pro 93.5%
- SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Fable 5.2 96.6%
- DeepSWE In use 113 original long-horizon engineering tasks across 91 repos and five languages, written from scratch. Hand-written verifiers accept alternative solutions. GPT-6 Astra 74.1%
- SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases.
Seed2.1 Pro 59.8%
- Codeforces rating In use Model solves recent Codeforces contest problems. An Elo-style rating is computed from simulated contest performance.
DeepSeek V4.1 Flash 3,471