HappyHorse Opens for Testing: Topping the Charts but Failing to Deliver Surprises

The Highs and Lows After Topping the Charts

On April 27, the long-anticipated HappyHorse finally opened for testing. Unfortunately, it failed to make the same kind of splash as Seedance 2.0 did when it burst onto the scene. "No surprises" — for HappyHorse, this is a rather fair assessment.

HappyHorse is a video model developed by the Innovation Division under Alibaba's ATH Business Group. It officially launched gray testing on April 27 and was integrated into the Qwen App for users to experience.

Two Major Halos, Maximum Expectations

The video model attracted enormous attention before testing, for two reasons.

First, it dominated the blind-test leaderboard. Before the open testing, HappyHorse — without any vendor identification — topped the Artificial Analysis AI Video Arena leaderboard, an authoritative AI evaluation platform primarily based on blind testing. With a higher Elo score, it surpassed a roster of star video models including ByteDance's Seedance 2.0, Kuaishou's Kling AI, and Google Veo 3 Fast, instantly making a name for itself.

Subsequently, heated discussions about its origin and capabilities continued, with several fake official websites even appearing to impersonate it, attracting countless unsuspecting onlookers. This "mystique" itself constituted an extremely successful case of viral marketing.

Second, it has Alibaba backing it. Three days after topping the leaderboard, on April 10, Alibaba's ATH Innovation Division officially claimed HappyHorse. Alibaba's accumulated resources and technical depth in the AI field filled the industry with high expectations for this "happy horse."

Both HappyHorse and its parent ATH Business Group are very young. The latter was established in March this year, driven by Alibaba CEO Eddie Wu (Wu Yongming), and is regarded as a key strategic move by Alibaba in the AI innovation track. A new team, a new model, a leaderboard triumph — all narrative elements pointed toward a classic "dark horse upset" script.

The Gulf Between Leaderboards and Real Experience

However, when HappyHorse actually opened for user testing, a noticeable gap emerged between reality and expectations.

Based on industry feedback, HappyHorse's actual generation quality did not demonstrate the "crushing" advantage that its Elo ranking would suggest. On key dimensions such as motion consistency, detail expressiveness, and instruction-following accuracy, its user experience did not show a clear differentiated advantage compared to competitors like Seedance 2.0 and Kling AI.

This has once again prompted the industry to reflect on the relationship between AI evaluation leaderboards and real product capability. The Elo rating system is based on user blind-test voting and does possess a degree of objectivity, but factors such as voting sample size, prompt distribution, and evaluation preferences can all influence the final rankings. Leaderboards can generate buzz, but product capability must ultimately be validated through user reputation.

The AI Video Model Track: Competition Enters Deep Waters

From a more macro perspective, HappyHorse's "no surprises" verdict precisely reflects the competitive landscape of the current AI video generation track — the capabilities of various models are rapidly converging, and the bar for truly stunning the audience is getting higher and higher.

The reason Seedance 2.0 was able to make such a massive splash was that it appeared during a time window when the capability gradient across the industry was still pronounced. By the time HappyHorse entered the arena, users' expectation thresholds had already been significantly raised. After successive waves of products like Veo 3, Seedance 2.0, and Kling AI, audiences have developed a certain degree of desensitization to the "wow factor" of video models.

This means the AI video track is transitioning from the "demo amazement" phase to the "product refinement" phase. Whoever can first establish barriers in generation stability, controllability, and commercial deployment will be the one to truly win out.

Outlook: A Long-Term Battle for a Young Team

For HappyHorse and the ATH Innovation Division, a debut with "no surprises" does not mean elimination. As a young team established only months ago, the ability to go head-to-head with top-tier industry players on a blind-test leaderboard already demonstrates significant technical potential.

The key lies in the iteration cadence going forward. Alibaba's computing resources, data accumulation, and ecosystem distribution capabilities are all strategic assets that HappyHorse can leverage. But on this crowded AI video model track, both "speed" and "precision" are indispensable — the team must iterate rapidly to close the experience gap while also precisely identifying a differentiated positioning.

After all, the endgame competition in AI video generation has never been a ranking match on leaderboards, but rather a war of attrition over product capability, ecosystem, and commercialization. HappyHorse's story has only just begun.