SciIR-Bench - Text: leaderboard
SciIR-Bench (2026) tests text-to-image models on 800 scientific illustration prompts drawn from open-access publications; a Gemini 3 Pro reviewer answers atomic yes or no checks, and an image counts for a track only if it passes every check of that track. This board is accuracy in percent on the Text track, the spelling and placement of required labels; higher is better.
Metric: Accuracy (%) on the Text track (spelling and position of required text); a gemini-3-pro-preview reviewer answers the atomic binary checks generated for each prompt with visual evidence retrieval; a sample counts only if it passes every check of the track; averaged over intrinsic-reasoning (abstract prompt) and instruction-following (dense scientific reasoning chain prompt) samples; higher is better. Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Nano Banana Pro | 90 | |
| 2 | Seedream 4.5 | 47 | |
| 3 | GPT-Image-1 | 41 | |
| 4 | Qwen-Image-2512 | 15 | |
| 5 | FLUX-Kontext-Max | 9 | |
| 6 | Show-o2-7B | 8 | |
| 7 | HiDream-I1-Full | 2 | |
| 8 | FLUX.1-dev | 1 | |
| 9 | Stable Diffusion 3.5 Large | 0 | |
| 10 | BAGEL-7B-MoT | 0 | #5470 |
Interactive version: theaggregate.ai/benchmark?slug=sciir-bench-text · How It Works · Data refreshed daily, snapshot 2026-11-13.