Benchmark Almanac

OpenRCA

Claude Opus 4.6 34.9% Only in system cards

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.) Data science and SQL All tasks public Last in a system card Feb 2026 Last model added Feb 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
microsoft/OpenRCA 448 stars Paper
2
Models scored in lab cards
2
System cards report it, from 1 lab

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. 34.9 Claude Opus 4.6
  2. 27.9 Claude Sonnet 4.6
  1. 1 Claude Opus 4.6 34.9%
  2. 2 Claude Sonnet 4.6 27.9%
#ModelScoreSettingDateSource
1 Claude Opus 4.6 34.9% Feb 2026 Model card
2 Claude Sonnet 4.6 27.9% Feb 2026 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 4.6 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 4.6

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 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.

DateDocumentScoreAs the document writes itSection
17 Feb 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
5 Feb 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

All 243 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%
  • SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Opus 5.5 93.9%
  • 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%
  • 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