Ranked #1 on Artificial Analysis Leaderboard: Does Happy Horse 1.0 Actually Beat Seedance 2.0?

Alibaba’s Happy Horse 1.0 reached the top of Artificial Analysis’s text-to-video and image-to-video Elo rankings largely through its high single-frame visual quality, unified audio-video generation architecture, and broad seven-language lip-sync support, but a six-scenario comparison with ByteDance’s Seedance 2.0 found that the aggregate ranking obscures important capability differences. Using identical prompts, assets, settings, and a framework measuring prompt alignment, visual quality, motion and physics, temporal consistency, and multimodal performance, the evaluation found Happy Horse stronger for cinematic aesthetics, texture, localized dialogue, multilingual lip sync, and short mood-focused clips. Seedance performed more reliably on dense multi-shot instructions, narrative reversals, editing tasks, character identity preservation, timing cues, natural audio consistency, and genre-aware additions such as talk-show audience laughter. The results suggest that Happy Horse often follows surface-level details well but can miss deeper semantic intent, while Seedance is less visually polished in some cases but better at executing narrative structure and complex direction. The comparison also promotes Atlas Cloud’s One API as a common interface for testing models with differing endpoints and SDKs, arguing that model selection should rely on scenario-specific capability matrices rather than a single leaderboard score.

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