HomeAli's Black Horse Model Happyhorse-1.0 Claims Victory in LM Arena Video Editing Contest
On April 15, the globally recognized AI model evaluation platform LM Arena released its latest rankings for the Video Editing category. The happyhorse-1.0 model from Alibaba's ATH business unit achieved a score of 1299 Elo points, securing the top global position and surpassing leading international models like Grok. This achievement marks the first time a Chinese AI team has claimed the number one spot in this evaluation category, signaling that domestic video generation and editing technology has entered the world's top tier.
LM Arena is widely regarded by the global AI community as a benchmark for blind evaluation. It employs an anonymous comparative voting system, gathering authentic user preferences on outputs from unidentified models and generating rankings using the Elo rating system. The listed happyhorse-1.0 model belongs to Alibaba's newly established ATH (Alibaba Token Hub) business unit, internally referred to as Alibaba-ATH. This model specializes in high-fidelity portrait expression, natural motion control, and precise video editing, allowing for seamless modification and reshaping of video content based on user instructions.
Previously, the HappyHorse model series had also made a notable appearance on another authoritative platform, Artificial Analysis's Video Arena ranking. During its anonymous testing phase, the series led both the text-to-video and image-to-video categories, with Elo scores exceeding those of top domestic and international models such as ByteDance's Seedance 2.0, Kuaishou's Kelin 3.0, and Google's Veo3Fast.
Industry experts suggest that happyhorse-1.0's success not only showcases Alibaba's core innovative capabilities in multimodal large models but also indicates that AI video technology is evolving from basic "content generation" to "precise editing and controllable production." As Chinese models increasingly lead international authoritative rankings, the global AI video industry's competitive focus is rapidly shifting toward more practical, vertical application scenarios.
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On April 15, the globally recognized AI model evaluation platform LM Arena released its latest rankings for the Video Editing category. The happyhorse-1.0 model from Alibaba's ATH business unit achieved a score of 1299 Elo points, securing the top global position and surpassing leading international models like Grok. This achievement marks the first time a Chinese AI team has claimed the number one spot in this evaluation category, signaling that domestic video generation and editing technology has entered the world's top tier.
LM Arena is widely regarded by the global AI community as a benchmark for blind evaluation. It employs an anonymous comparative voting system, gathering authentic user preferences on outputs from unidentified models and generating rankings using the Elo rating system. The listed happyhorse-1.0 model belongs to Alibaba's newly established ATH (Alibaba Token Hub) business unit, internally referred to as Alibaba-ATH. This model specializes in high-fidelity portrait expression, natural motion control, and precise video editing, allowing for seamless modification and reshaping of video content based on user instructions.
Previously, the HappyHorse model series had also made a notable appearance on another authoritative platform, Artificial Analysis's Video Arena ranking. During its anonymous testing phase, the series led both the text-to-video and image-to-video categories, with Elo scores exceeding those of top domestic and international models such as ByteDance's Seedance 2.0, Kuaishou's Kelin 3.0, and Google's Veo3Fast.
Industry experts suggest that happyhorse-1.0's success not only showcases Alibaba's core innovative capabilities in multimodal large models but also indicates that AI video technology is evolving from basic "content generation" to "precise editing and controllable production." As Chinese models increasingly lead international authoritative rankings, the global AI video industry's competitive focus is rapidly shifting toward more practical, vertical application scenarios.
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