MiGUE-Bench - Cross-Document Temporal Relations: leaderboard
A cross-document relation task of MiGUE-Bench (2026), a benchmark of event analysis over news documents: accuracy in percent on 300 multiple-choice questions about temporal relations between events reported in different documents, with LLM-built distractors. Higher is better.
Metric: Accuracy (%) on 300 cross-document temporal relation multiple-choice questions with LLM-built distractors over news documents; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 12 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3 Pro | 79.67 | #137 |
| 2 | Claude Opus 4.5 | 77.81 | #156 |
| 3 | Qwen 3 Max | 70.67 | #403 |
| 4 | GLM-4.7 | 70.66 | #366 |
| 5 | GPT-5.2 Pro | 67.86 | #124 |
| 6 | DeepSeek V3.2 | 67.32 | #406 |
| 7 | Kimi K2 | 61.67 | #439 |
| 8 | Qwen 3 235B A22B | 55.67 | #623 |
| 9 | Qwen 3 30B A3B | 53.72 | #1122 |
| 10 | Qwen 3 8B | 49 | #1830 |
| 11 | Claude Haiku 4.5 | 46.33 | #568 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=migue-bench-cross-document-temporal-relations · How It Works · Data refreshed daily, snapshot 2026-11-13.