ScholScan (Image Input) - Measurement and Operationalization: leaderboard
Metric: Flaw-detection score (0-1, scaled to 0-100): per question, zero unless the model finds the annotated scientific error, otherwise the geometric mean of evidence-location Dice and reasoning-chain prefix match, times a penalty for unrelated errors listed, as extracted by a GPT-4.1 evaluator, averaged over the measurement and operationalization questions of ScholScan's 1,800 expert-reviewed questions that ask a model to scan a whole academic paper (ICLR 2024-2025 and Nature Communications, 715 papers) for a planted or reviewer-reported scientific flaw without being told where to look, with the paper given as page images; higher is better. Source: arxiv.org. Saturation forecast: Around April 2028. 9 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5 | 14.6 | #91 |
| 2 | Gemini 2.5 Pro | 12.3 | #145 |
| 3 | Doubao-Seed-1.6 (Thinking) | 7.5 | |
| 4 | Seed-1.6 | 4.9 | #257 |
| 5 | Llama 4 Maverick | 4.5 | #451 |
| 6 | Grok 4 | 3.2 | #169 |
| 7 | Gemma 3 27B | 1.7 | #596 |
| 8 | Mistral Small 3.1 | 1.5 | #600 |
No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.
Interactive version: theaggregate.ai/benchmark?slug=scholscan-image-input-measurement-and-operationalization · How It Works · Data refreshed daily, snapshot 2026-10-11.