FireWorldBench - Temporal Evolution Forecasting (Rendered Images): leaderboard

The temporal evolution forecasting dimension of FireWorldBench (Chen et al., 2026): forecasting how the temperature, smoke, visibility and flow fields of a simulated fire will evolve after an observation cutoff. 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 (%; P1, temporal evolution forecasting 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 February 2027. 10 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.6 Luna60.74#117
2Qwen 3 VL 32B Instruct58.28#582
3Claude Haiku 4.556.04#568
4Gemini 2.5 Flash52.84#452
5InternVL3-78B45.96#733
6Qwen 3.5 Plus (2026-02-15)44.92#178
7InternVL3-38B41.94#905
8Qwen 3 VL 235B A22B Instruct38.02#521
9Qwen 3 VL 8B Instruct31.92#982
10InternVL3-8B26.86#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-temporal-evolution-forecasting-rendered-images · How It Works · Data refreshed daily, snapshot 2026-11-12.