SciR - Causal Hard: leaderboard
Accuracy on the 200 hard causal-abduction tasks over the Sachs protein signalling network, written as plain natural-language premises, from SciR (2026), a scientific-reasoning benchmark generated from formal objects. The model reasons by chain of thought at temperature 0 and answers are checked against the generating object. Higher is better.
Metric: Accuracy (%) on the 200 hard-tier SciR causal tasks (causal abduction over the Sachs signalling network), rendered as plain natural-language premises (NL); the model reasons with chain-of-thought directly from the task text, temperature 0; answers are checked against the generating formal object; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | DeepSeek R1 | 88.5 | #473 |
| 2 | O3 Mini | 85.5 | #533 |
| 3 | GPT-4o | 46 | #741 |
| 4 | Qwen 3 30B A3B 2507 Instruct | 44.5 | #1089 |
| 5 | Llama 3.3 70B Instruct | 33 | #1357 |
| 6 | OLMo 3.1 32B Instruct | 4 | #2384 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=scir-causal-hard · How It Works · Data refreshed daily, snapshot 2026-11-13.