ModelFoldhappyhorse/text-to-video
HappyHorse-1.0 is an emerging AI video model developed by Alibaba ATH and has quickly become one of the most watched new names in AI video generation. It is associated with strong text-to-video and image-to-video performance, along with audio support and multilingual capability.
Pricing: From 35 credits / sec at 720p (175 credits for 5 seconds), billed on output seconds. Every resolution is in the rate card below.
Explore more Text to Video models
More AI models from Alibaba
Latest and popular AI models
Affordable HappyHorse-1.0 API for AI Video Creation
HappyHorse-1.0 API on ModelFold delivers affordable access to powerful AI video generation from text or image, with support for sound, silent output, and multilingual creation.
HappyHorse-1.0: An Emerging AI Video Model Of Alibaba ATH
On April 8, 2026, HappyHorse-1.0 quickly became one of the most talked-about new names in AI video after appearing on Artificial Analysis as a standout model in its public video arena. It drew immediate attention by ranking at the top of major leaderboard views, including Text-to-Video and Image-to-Video, placing it directly into the same competitive conversation as leading models such as Seedance 2.0. Then, on April 27, 2026, HappyHorse-1.0 was officially released, marking its shift from a closely watched emerging model to a formally launched contender in AI video generation. Developed by Alibaba ATH, HappyHorse-1.0 stands out for its strong benchmark momentum and increasingly complete video creation capabilities.
Text-to-Video Generation
With HappyHorse-1.0 API, text-to-video generation makes it possible to turn natural language prompts into dynamic video content for storytelling, visual ideation, marketing concepts, short-form clips, and other creative uses. This mode is especially useful when a project starts from an idea rather than an existing visual, giving users a direct way to transform written concepts into more vivid and expressive video results.
Image-to-Video Generation
HappyHorse-1.0 API also supports image-to-video generation, making it easier to transform reference images into more engaging video outputs with stronger visual continuity. This mode is well suited for character animation, product motion, stylized scene generation, and other reference-based creation needs where preserving the original image remains an important part of the result.
Reference-to-Video Generation
HappyHorse-1.0 API further expands creative flexibility through reference-to-video generation, allowing users to guide video results with more specific visual references. This mode is valuable when the goal is to keep closer alignment with a desired style, subject appearance, composition, or scene direction, making generated videos feel more controlled and more consistent with the source material.
Video Editing with HappyHorse-1.0 API
HappyHorse-1.0 API also supports video editing, giving users a way to refine, adjust, or extend existing video content instead of generating everything from scratch. This makes the model more useful for creators who want to modify motion details, update visual elements, or improve existing clips while keeping the broader scene and structure intact.
Core Features That Make HappyHorse-1.0 API Stand Out
Turn Text-to-Video Ideas into Dynamic Visual Content with HappyHorse-1.0 API
With HappyHorse-1.0 API, text prompts can be transformed into vivid video content that feels more expressive, more cinematic, and more visually complete. Instead of stopping at a written idea, users can turn descriptions into motion-driven scenes for storytelling, concept exploration, marketing visuals, and short-form creative output. This makes HappyHorse-1.0 API especially appealing for prompt-led creation, where the value comes from converting language into stronger visual results with less friction.
Bring Image-to-Video Creation to Life with Happy Horse 1.0 API
Happy Horse 1.0 API makes image-to-video creation more compelling by turning still visuals into motion content while keeping the original image central to the final result. A single character portrait, product shot, or stylized image can become more engaging once movement is introduced, especially when the goal is to preserve the source visual rather than replace it. That gives Happy Horse 1.0 API clear value for creators who want static images to feel more alive, more dynamic, and more suitable for video-first presentation.
Multi-Shot Video Generation and 1080P Output in HappyHorse API
HappyHorse API also supports multi-shot video generation and 1080P output, giving it stronger capability for more structured and higher-quality video creation. Multi-shot generation allows a single result to include richer scene progression and more layered visual storytelling, while 1080P output helps ensure that generated videos look clearer, sharper, and more suitable for professional presentation. This makes HappyHorse API more valuable for users who need both stronger narrative composition and higher-resolution video quality.
Support Multilingual Video Generation with HappyHorse-1.0 API
HappyHorse-1.0 API is also associated with multilingual video generation, which gives it broader relevance as AI video moves toward more global use. Rather than being limited to a single-language environment, HappyHorse-1.0 API carries stronger appeal for content intended for international audiences, multilingual creator experiences, and video generation needs that extend across different language contexts. This makes the model feel more adaptable in a landscape where broader language support is becoming increasingly important.
HappyHorse-1.0 Benchmark Comparison in the Artificial Analysis Video Arena
Across the four Artificial Analysis Video Arena benchmark views shown here, HappyHorse-1.0 delivers its strongest results in the no-audio categories, where it ranks #1 in both text-to-video and image-to-video. In the two with-audio categories, HappyHorse-1.0 ranks #2 in both views, which shows that its performance is not limited to a single generation mode. Taken together, these results make HappyHorse-1.0 stand out as one of the most competitive new video models across both prompt-based and image-based creation.
| Artificial Analysis Video Arena View | HappyHorse-1.0 | Dreamina Seedance 2.0 720p | SkyReels V4 | PixVerse V6 | Kling Models | Veo Models |
|---|---|---|---|---|---|---|
| Text to Video (No Audio) | #1 · 1,332 | #2 · 1,273 | #3 · 1,245 | #4 · 1,241 | Kling 3.0 1080p (Pro) #5 · 1,241 Kling 3.0 Omni 1080p (Pro) #6 · 1,231 | / |
| Text to Video (With Audio) | #2 · 1,204 | #1 · 1,219 | #3 · 1,139 | / | Kling 3.0 Omni 1080p (Pro) #4 · 1,104 Kling 3.0 1080p (Pro) #5 · 1,099 Kling 3.0 720p (Standard) #7 · 1,094 Kling 3.0 Omni 720p (Standard) #8 · 1,092 | Veo 3.1 Fast #6 · 1,095 Veo 3.1 #9 · 1,088 |
| Image to Video (No Audio) | #1 · 1,391 | #2 · 1,356 | #8 · 1,291 | #3 · 1,340 | Kling 3.0 Omni 1080p (Pro) #5 · 1,298 Kling 2.5 Turbo 1080p #6 · 1,296 | Veo 3.1 Fast #9 · 1,291 |
| Image to Video (With Audio) | #2 · 1,159 | #1 · 1,162 | #3 · 1,088 | #5 · 1,077 | Kling 3.0 1080p (Pro) #8 · 1,058 Kling 3.0 720p (Standard) #9 · 1,057 | Veo 3.1 Fast #6 · 1,074 Veo 3.1 #7 · 1,072 |
How to Access and Deploy HappyHorse-1.0 API on ModelFold
- 1
Step 1: Sign Up or Log In to ModelFold and Get Your HappyHorse-1.0 API Key
Create your ModelFold account or log in to an existing one to access HappyHorse-1.0 API, which is available now. Obtain your API key from the dashboard and prepare for testing or integration.
- 2
Step 2: Test HappyHorse-1.0 API Free in the Playground
Before deployment, you can try HappyHorse-1.0 API directly in the ModelFold playground. This gives you a simple way to explore output quality, test prompts or image inputs, and get a clearer sense of how HappyHorse-1.0 API performs before using it in production.
- ✓
Step 3: Deploy HappyHorse-1.0 API in Production
Once testing is complete, the next step is to deploy HappyHorse-1.0 API in your own application, product, or internal environment. This makes it possible to use HappyHorse-1.0 API for real video generation scenarios where stable access and practical integration matter.
HappyHorse-1.0 vs Leading Video Models in High-Precision Motion and Physics Tests
Motion Stability Showdown: HappyHorse-1.0 vs. Seedance 2.0
In this specific hula hoop stress test, Dreamina’s Seedance 2.0, despite its vibrant and "cinematic" visual aesthetic, revealed clear limitations in handling complex motion and physical interactions. In sharp contrast, HappyHorse-1.0 demonstrated superior structural stability and temporal coherence. HappyHorse notably excels during the challenging transition from a standing to a kneeling position, where the generated hula hoop maintains a realistic trajectory and coherent interaction with both the floor and the subject's body. Seedance 2.0 struggled to "lock" the object to the character's waist, exhibiting subtle "ghosting" and clipping artifacts, suggesting a greater need for improvement in motion forecasting and physical constraints.
Precision and Physics: HappyHorse-1.0 vs. Kling 3.0 Pro
In this sports-focused evaluation, the HappyHorse-1.0 vs. Kling 3.0 Pro comparison highlights the challenge of simulating small-scale physics and complex interactions. HappyHorse-1.0 delivers a stunningly realistic performance; the ball’s roll, the subtle dip into the hole, and the golfer’s subsequent reaction are all rendered with impeccable physical logic and temporal stability. On the other hand, Kling 3.0 Pro opts for a more dramatic, close-up cinematic angle. While visually sharp, it encounters significant "hallucination" issues as the ball approaches the hole, with the object's geometry and the ground's texture warping under pressure. This round clearly demonstrates HappyHorse's superior ability to maintain world-model consistency in high-precision scenarios.
Reflection and Realism: HappyHorse-1.0 vs. Grok-Video-Imagine
The challenge of rendering accurate reflections is a well-known benchmark for video models, and the HappyHorse-1.0 vs. Grok-Video-Imagine comparison puts this to the ultimate test. HappyHorse-1.0 showcases a masterful grasp of optical physics; as the cat interacts with the chrome toaster, the reflection moves in perfect synchronicity with the subject, maintaining consistent proportions and lighting. In contrast, Grok-Video-Imagine struggles with spatial awareness—the reflection within the toaster fails to mirror the cat's actual movements accurately, often appearing as a separate entity rather than a reactive surface. This demonstration solidifies HappyHorse-1.0’s lead in generating complex, multi-layered environments with high logical fidelity.
Fluid Dynamics Excellence: HappyHorse-1.0 vs. PixVerse V6
The intricate process of latte art serves as a rigorous test for AI fluid simulation, and the HappyHorse-1.0 vs. PixVerse V6 comparison highlights a clear distinction in technical execution. HappyHorse-1.0 demonstrates an exceptional understanding of fluid dynamics; the milk flow interacts naturally with the coffee surface, creating a "leaf" pattern that expands logically with each pour. In contrast, PixVerse V6 exhibits typical "morphing" artifacts, where the heart-shaped pattern appears to vibrate or spontaneously generate new layers without consistent physical input from the pitcher. HappyHorse-1.0’s ability to maintain the structural integrity of the foam while simulating realistic liquid surface tension further establishes its prowess in high-precision video synthesis.
Where Happy Horse 1.0 API Can Create the Most Value
Turn Creative Ideas into Short Video Scenes with HappyHorse-1.0 API
When a concept starts as a rough prompt, HappyHorse-1.0 API can help turn that idea into a more watchable video scene with motion, atmosphere, and stronger visual expression. This makes HappyHorse-1.0 API especially suitable for short-form storytelling, concept testing, mood-driven video creation, and other content that benefits from moving quickly from imagination to visual output.
Make Still Characters and Visuals Feel More Alive with Happy Horse 1.0 API
A static image often becomes more engaging once motion is added, and that is where Happy Horse 1.0 API becomes especially useful. Happy Horse 1.0 API fits character animation, stylized visual motion, product-focused scenes, and other image-led video creation needs where the original image should remain recognizable while gaining a more dynamic presence.
Create More Engaging Marketing Videos with HappyHorse API
For branded clips, promotional visuals, and campaign content, HappyHorse API can help transform simple creative direction into video output that feels more vivid and attention-grabbing. This gives HappyHorse API clear value for marketing content that needs stronger movement, a more polished visual impression, and a format that feels better suited to modern social and digital platforms.
Reach Broader Audiences Through Global Video Content with HappyHorse-1.0 API
As video content increasingly needs to travel across languages and markets, HappyHorse-1.0 API becomes more relevant for globally oriented creation. HappyHorse-1.0 API is a better fit for multilingual video experiences, international-facing content, and broader audience communication where flexibility across language contexts can make the final output more useful and more scalable.
Why Choose ModelFold for HappyHorse-1.0 API Access and Deployment
Text, Image, Reference and Video Edit Models on One Endpoint
happyhorse/text-to-video, happyhorse/image-to-video, happyhorse/reference-to-video and happyhorse/video-edit run on POST https://ai.modelfold.online/v1/videos/generations with 720p or 1080p output and prompts in any language. Text, image and reference videos run 3 to 15 seconds; the full parameter list is in the Playground and the API section on this page. Pricing for each resolution is shown in the pricing line above and in the rate card of the API section.
First Frame and Character Order Rules
Image-to-video takes exactly one first-frame image and keeps its aspect ratio. Reference-to-video takes 1 to 9 reference images, and their order defines character1, character2 and so on. Reference images of 720P or higher are recommended, since small, blurry or heavily compressed images degrade the output.
Video Edit Keeps the Input Length
Video edit takes one MP4 or MOV clip of 3 to 60 seconds, returns a video as long as the input, and is billed on input plus output duration. Choose auto audio to let the model handle sound, or origin to keep the original audio where possible. New ModelFold users receive 80 free credits for testing.
Frequently Asked Questions About HappyHorse-1.0 API
What is HappyHorse-1.0 and is it related to Alibaba?⌄
HappyHorse-1.0 is an emerging AI video model developed by Alibaba ATH. It has quickly attracted attention for its strong performance in public video evaluation, especially in text-to-video and image-to-video generation, and is increasingly seen as a serious new contender in AI video.
When will HappyHorse 1.0 API be available on ModelFold?⌄
HappyHorse 1.0 is now released, and ModelFold now provides HappyHorse-1.0 API to make access, testing, and deployment easier for developers.
Does HappyHorse-1.0 API support audio generation?⌄
Yes, HappyHorse-1.0 API is associated with video generation both with sound and without sound, which is one reason it stands out from models that draw attention mainly in silent video evaluation. This makes the model more relevant for a broader range of video creation needs, from simple visual generation to richer audio-inclusive output.
Does HappyHorse-1.0 API support multilingual video generation?⌄
HappyHorse-1.0 API is also associated with multilingual capability, which adds to its appeal as AI video moves toward wider international use. This makes the model more interesting for video generation scenarios that may need to support broader language coverage rather than remaining limited to a single-language environment.
| 720p | 175 credits per 5 seconds |
| 1080p | 300 credits per 5 seconds |
Billed per second. The price above is for a 5-second clip at each resolution — longer clips cost proportionally more, and the Playground charges the exact same way.
curl https://ai.modelfold.online/v1/videos/generations \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"happyhorse/text-to-video","prompt":"...","resolution":"720p","duration":5}'