FireWorldBench (Rendered Images): leaderboard

FireWorldBench (Chen et al., 2026) tests understanding of coupled-field fire dynamics on 494 simulated fire worlds: models forecast evolution, ground observations in physical fields, relate coupled fields, attribute causal mechanisms and predict intervention outcomes. Observations are given as physics-rendered field images and 3D scene renderings; the score is completion accuracy in percent averaged over the five capability dimensions and over matched multiple-choice and open-report questions and scored deterministically; higher is better.

Metric: Completion accuracy (%; equal-weight mean of the five physical-capability dimensions P1-P5 on the 8,360-item 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 January 2028. 10 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 2.5 Flash44.83#452
2GPT-5.6 Luna42.99#117
3Qwen 3 VL 32B Instruct42.55#582
4Claude Haiku 4.539.51#568
5Qwen 3.5 Plus (2026-02-15)39.4#178
6InternVL3-78B38.72#733
7InternVL3-38B34.81#905
8Qwen 3 VL 235B A22B Instruct34.02#521
9Qwen 3 VL 8B Instruct29.41#982
10InternVL3-8B27.77#1695

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

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