FireWorldBench - Cross-Field Coupling Understanding (Rendered Images): leaderboard

The cross-field coupling understanding dimension of FireWorldBench (Chen et al., 2026): relating coupled fields of a simulated fire, such as plume shape and flow, to predict smoke transport. Observations are given as physics-rendered field images and 3D scene renderings; the score is completion accuracy in percent, averaged over matched multiple-choice and open-report questions and scored deterministically; higher is better.

Metric: Completion accuracy (%; P3, cross-field coupling understanding items of the controlled-simulation subset; image-observation track (physics-rendered field visualizations and 3D scene renderings); each cell is the mean of the matched choice-format score (Jaccard overlap of selected and gold option sets) and open-report score (exact agreement of required structured slots); deterministic evaluator, no LLM judge; failed or unparseable responses score 0). Source: arxiv.org. Saturation forecast: Around August 2028. 10 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 2.5 Flash45.86#452
2GPT-5.6 Luna44.09#117
3Qwen 3 VL 32B Instruct41.5#582
4Qwen 3.5 Plus (2026-02-15)40.72#178
5Claude Haiku 4.540.49#568
6InternVL3-38B37.95#905
7Qwen 3 VL 235B A22B Instruct36.66#521
8InternVL3-78B35.84#733
9InternVL3-8B34.08#1695
10Qwen 3 VL 8B Instruct32.45#982

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=fireworldbench-cross-field-coupling-understanding-rendered-images · How It Works · Data refreshed daily, snapshot 2026-11-12.