DisasterBench: leaderboard
Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage assessment, into coherent multi-step workflows. As LLMs increasingly serve as orchestrators of such pipelines, effective coordination requires more than selecting semantically plausible tools: LLMs must generate executable workflows with correct pa
Metric: Exact-match Accuracy (self-reported). Source: benchmarklist.com. 12 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3.1 Pro Preview | 73.39 | |
| 2 | Gemma-4-31B | 66.52 | |
| 3 | Qwen3-Max-Thinking | 66.52 | |
| 4 | Qwen3.5-9B | 65.67 | |
| 5 | GPT-5.4 | 65.67 | #79 |
| 6 | Qwen3-Max | 63.95 | |
| 7 | DeepSeek-R1 | 62.66 | |
| 8 | Qwen3.5-27B | 62.23 | |
| 9 | DeepSeek-V3.2 | 61.8 | |
| 10 | Llama-3.3-70B | 58.8 |
Interactive version: theaggregate.ai/benchmark?slug=disasterbench · How It Works · Data refreshed daily, snapshot 2026-10-20.