Zhang Di connection
MixedZhang Di, who led the Kling video generation team at Kuaishou, is suspected to be connected to HappyHorse's development, possibly through Alibaba's Taotian Group
HappyHorse vs Kling
HappyHorse and Kling share a possible connection through Zhang Di, former Kling team lead, making this the most technically interesting comparison in the AI video space right now.
Key facts
Quick facts
Kling's market position
VerifiedKling has been one of the most capable publicly available AI video models since mid-2024, with established API access and a global user base
Architectural lineage
UnknownBoth models appear to use transformer-based architectures for video generation, but whether HappyHorse directly builds on Kling's technical foundations is unconfirmed
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Mixed signal
Some facts are supported, but other details remain uncertain
Comparison framing is valid, but specific performance claims should be limited to what public evidence supports.
Readers should expect careful wording here because public reporting confirms the topic, while some product details still need cautious treatment.
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30-second verdict
This is the most narratively compelling comparison in the AI video space. HappyHorse's anonymous April 2026 launch is widely suspected to involve Zhang Di, the former lead of Kuaishou's Kling video team. If true, HappyHorse represents what happens when a top researcher leaves an established lab and builds something new with different constraints and resources — possibly backed by Alibaba's Taotian Group. Kling remains a strong, accessible model with proven infrastructure. HappyHorse appears to have surpassed it on benchmarks, but Kling has the practical advantage of being a known, working product.
Key comparison dimensions
Quality
HappyHorse currently ranks above Kling on the Artificial Analysis leaderboard. Both produce 1080p video output. Kling has been iterating steadily since its mid-2024 launch, with Kling 1.5 and subsequent updates improving motion quality. HappyHorse's 15B-parameter model with 8-step denoising appears to deliver a quality jump, though side-by-side comparisons on identical prompts are still limited.
Speed
Kling offers reasonably fast generation through its established infrastructure, with API-based generation typically completing in under a minute for short clips. HappyHorse's generation speed is less well documented due to its recent and somewhat opaque launch.
Price and access
Kling has clear pricing through Kuaishou's platform with both free tiers and paid plans. It offers API access for developers. HappyHorse's access and pricing remain uncertain — the anonymous launch means no official pricing page exists yet.
Features
Kling supports text-to-video, image-to-video, and has added features like lip sync and motion brush over time. HappyHorse claims text-to-video, image-to-video, and audio-video sync capabilities. On paper the feature sets overlap significantly, which makes the suspected shared lineage more interesting.
The Zhang Di question
The elephant in this comparison is Zhang Di. Reporting suggests he left Kuaishou and may now be connected to HappyHorse through Alibaba's Future Life Lab. If this is accurate, it means HappyHorse's creator has deep knowledge of what Kling does well and where it falls short — and may have built specifically to address those gaps. This is unconfirmed, and readers should treat it as context rather than established fact.
What can be compared with confidence
- Both are Chinese-developed AI video generation models producing 1080p output.
- Kling has established access, pricing, and API infrastructure.
- HappyHorse currently ranks higher on the Artificial Analysis leaderboard.
- The Zhang Di connection is widely reported but not officially confirmed.
What should stay cautious
- Whether HappyHorse technically derives from Kling's codebase or research
- Long-term availability and support for HappyHorse
- Direct output quality comparisons on identical prompts
- Any claim about intellectual property or trade secret issues
Where to go next
See how HappyHorse compares to the model it beat on the leaderboard: HappyHorse vs Seedance. For the full landscape, check Best AI Video Models. To understand HappyHorse's background, read What Is HappyHorse?.
FAQ
Frequently asked questions
Was HappyHorse built by the same team as Kling?
There is strong speculation that Zhang Di, who previously led Kling's development at Kuaishou, is connected to HappyHorse. However, this has not been officially confirmed, and HappyHorse launched anonymously. The connection remains in the "widely reported but unverified" category.
Is HappyHorse just an improved version of Kling?
No. While there may be shared personnel, HappyHorse appears to be a separate model with its own architecture (reported as 15B parameters with 8-step denoising). It topped different benchmarks than Kling and launched through a different channel entirely.
Which one should I use for video generation today?
Kling has the advantage of established access — it has a working API, a web interface, and known pricing. HappyHorse may produce higher-quality outputs based on current leaderboard signals, but its access path is less clear. If you need to generate video right now with a reliable pipeline, Kling is the safer choice.
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