FilmBench (Text-to-Video): leaderboard
FilmBench (2026) scores video-generation models on prompts reverse-engineered from clips of award-winning films in 20 genres, most scripting several shots, using an automatic evaluator built with film-academy directors on a three-level cinematic-language taxonomy. This board covers the 515 text-to-video prompts (text prompts); score is the equal-weighted mean of the instruction-following, temporal-continuity and aesthetic-quality axes on a 0-100 scale, higher is better.
Metric: Overall score (0-100), the equal-weighted mean of the three axes, on 515 text-to-video prompts; prompts reverse-engineered from clips of award-winning films in 20 genres, scored by the authors' expert-grade evaluation agent (Cinematic Language operators plus a judge model) on a three-level cinematic taxonomy, 1-5 per sub-metric mapped to 0-100. Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 9 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Seedance 2.0 | 88.9 | |
| 2 | HappyHorse 1.1 | 87.4 | |
| 3 | HappyHorse 1.0 | 87 | |
| 4 | Kling 3.0 Omni | 86.1 | |
| 5 | Kling 3.0 | 85.8 | |
| 6 | Vidu Q3 Pro | 80.9 | |
| 7 | Grok Imagine Video | 80.9 | |
| 8 | Veo 3.1 | 80.8 | |
| 9 | Hailuo 2.3 | 68.9 |
Interactive version: theaggregate.ai/benchmark?slug=filmbench-text-to-video · How It Works · Data refreshed daily, snapshot 2026-11-13.