RHELM (Long Context) - Temporal: leaderboard
RHELM category (Zhang et al., Microsoft and Renmin University of China, 2026) that asks when events happened or in what order, identified through indirect markers or synthesized across long periods. The model reads the whole history, dialogue and external documents in chronological order, in a 1M-token context; accuracy in percent on 185 questions judged 0 or 1 by an LLM, higher is better.
Metric: Accuracy (%; 185 temporal questions, LLM judge, dialogue history plus external sources, full history in a 1M-token context). Source: arxiv.org. Saturation forecast: Estimated already saturated. 3 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-4.1 Mini | 27 | #775 |
| 2 | Gemini 2.5 Flash Lite | 14.1 | #958 |
| 3 | Qwen2.5-14B-Instruct-1M | 7 | #1417 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=rhelm-long-context-temporal · How It Works · Data refreshed daily, snapshot 2026-11-12.