# HappyHorse Related Articles

HTX News Center provides the latest articles and in-depth analysis on "HappyHorse", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

The article discusses the rise of HappyHorse-1.0, an AI video generation model developed by Alibaba, which topped the Artificial Analysis leaderboard in both text-to-video and image-to-video categories in April 2026. The model was created under the leadership of Zhang Di, who returned to Alibaba in November 2025 after working at Kuaishou, where he led the development of the Kling model. HappyHorse is open-source and commercially available, similar to Alibaba's Qwen model. Zhang Di's background includes extensive experience in large-scale data systems and machine learning at Alibaba and Kuaishou, which contributed to the rapid development of HappyHorse within just five months. The model uses a 15-billion-parameter transformer architecture with native multimodal training, supporting multiple languages and lip-sync capabilities. It also focuses on reducing inference time and cost, making it practical for commercial use. The primary application of HappyHorse is in e-commerce, where it can generate product videos to enhance user engagement and conversion rates by creating contextual and personalized content. This aligns with Alibaba's strengths in commerce, advertising, and data feedback loops. The model's success with open-source approach contrasts with challenges faced by closed-source models like OpenAI's Sora (shut down due to high costs) and ByteDance's Seedance 2.0 (paused over copyright issues). HappyHorse represents a strategic move for Alibaba to integrate AI video generation into its core business ecosystems.

marsbit04/13 05:10

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

marsbit04/13 05:10

Claiming the "Happy Horse": Alibaba's AI Lays Out the "Eight Trigrams Formation"

Alibaba has officially claimed the "HappyHorse" (HappyHorse-1.0) AI video generation model, which recently topped the global benchmark on Artificial Analysis with an Elo score of 1357. Developed by Alibaba’s ATH (Alibaba Token Hub) innovation unit, the model is notable for its ability to generate high-definition video with synchronized audio and sound effects from text input, significantly improving motion coherence and reducing production time and cost. This launch is part of a broader acceleration in Alibaba’s AI strategy. In late March and early April, the company released three flagship models in quick succession: Qwen3.5-Omni, Wan2.7-Image, and Qwen3.6-Plus. The latter broke global daily call volume records with 1.4 trillion tokens processed shortly after release. Alibaba has also undergone significant organizational restructuring to support its AI ambitions. In March, it established the ATH business group, led by CEO Wu Yongming, to integrate AI development, cloud services, and application deployment. Further changes in April included forming a group-level technology committee and consolidating the Tongyi Lab into a dedicated AI model division. The company is investing heavily in AI, with plans to spend over 380 billion RMB on cloud and AI infrastructure over three years. Its self-developed GPUs have already seen mass production. While the market has responded positively to these moves, challenges remain in balancing centralized control with operational flexibility and maintaining team stability amid rapid changes.

marsbit04/11 04:07

Claiming the "Happy Horse": Alibaba's AI Lays Out the "Eight Trigrams Formation"

marsbit04/11 04:07

5.4 Billion Burned, Sora Dies: Anonymous Chinese Model Kicks Open the Next Door in 38 Seconds

In March-April 2026, two major events reshaped the AI video generation landscape. OpenAI shut down its flagship model Sora, citing unsustainable daily costs of $15 million and low user retention, effectively exiting the consumer video market. Shortly after, an anonymous Chinese model dubbed "HappyHorse-1.0" topped the blind-test leaderboard on Artificial Analysis with a score of 1357 in text-to-video (without audio), outperforming rivals like ByteDance’s Seedance 2.0. HappyHorse-1.4 seconds to generate 1080p video with audio on a single H100 GPU. Its unified Transformer architecture and distilled diffusion techniques significantly improved efficiency compared to Sora’s costly diffusion-based approach. The model is speculated to be developed by Alibaba or based on Sand.ai’s technology, though its anonymous release suggests strategic data collection and legal risk avoidance regarding copyright and deepfake regulations. Meanwhile, commercial leaders like ByteDance impose high barriers—including million-dollar API contracts and strict compliance checks—to mitigate legal risks, focusing on B2B applications rather than consumer use. Key emerging opportunities include automated e-commerce promo videos, AI-assisted short drama production, and localized ad creation for global markets, all driven by plunging generation costs and faster turnaround times. The competition has shifted from pure model performance to cost efficiency, workflow integration, and regulatory compliance.

marsbit04/10 00:19

5.4 Billion Burned, Sora Dies: Anonymous Chinese Model Kicks Open the Next Door in 38 Seconds

marsbit04/10 00:19

Mysterious Model HappyHorse Tops the Chart Overnight: Is the Video Generation Arena Welcoming a "Game Changer"?

A mysterious AI video generation model named "HappyHorse-1.0" has quietly topped the AI Video Arena leaderboard on Artificial Analysis, surpassing established models like Seedance 2.0 and others in Elo score—a user-blind-test-based ranking reflecting real perceived quality. The model’s origin was initially unknown, but technical analysis later linked it to the open-source model "daVinci-MagiHuman," jointly developed by Shanghai SII GAIR Lab and Beijing-based Sand.ai. HappyHorse-1.0, likely an optimized iteration by Sand.ai, uses a 15-billion-parameter transformer architecture for joint audio-video-text modeling. Its strong performance in human-centric scenes (e.g., portraits, narrations) helped it excel in blind tests, though it still lags in multi-character or complex motion scenarios. The achievement signals a potential shift: an open-source model rivaling closed-source alternatives in perceived quality, which could lower costs and increase flexibility for developers in vertical applications like virtual avatars. However, limitations remain, including high computational requirements (H100 GPU needed) and shorter generation lengths. While not yet threatening market leaders, HappyHorse represents progress toward open models reaching "production-ready" quality, potentially accelerating community-driven improvements in the video AI space.

marsbit04/08 07:57

Mysterious Model HappyHorse Tops the Chart Overnight: Is the Video Generation Arena Welcoming a "Game Changer"?