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Alibaba: HappyHorse 1.1

alibaba/happyhorse-1.1

BenchmarksCompare

HappyHorse 1.1 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds. It is suited for creative content, social media clips, and image-driven animation, and improves on the prior version with stronger prompt adherence, smoother motion, and more consistent characters across frames.

Modalities
Price
from $0.0988/second
Released
Jun 24, 2026
BenchmarksCompare
PlaygroundBenchmarksProvidersPerformanceUptimeAppsActivityDocumentationFAQExplore

Example outputs summary for Alibaba: HappyHorse 1.1

  • Text to Video: a slow cinematic push-in on a glowing neon sign that reads "OpenRouter" in the window of a cozy coffee shop on a rainy night, rain streaking down the glass, reflections rippling on wet pavement — generated with Alibaba: HappyHorse 1.1

Playground

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark outputs summary for Alibaba: HappyHorse 1.1

  • Traffic light: A fixed camera on a single traffic light against a plain grey sky. The light starts red, changes to green, and then changes to amber, in that exact order, each colour holding for a clearly visible moment. No other objects, no camera movement, no cuts. (Tied 1st of 28 on this prompt)
  • Five candles: A fixed straight-on shot of five lit candles in a row on a dark table. The candles go out one at a time from left to right until all five are out. No hand or person visible, no camera movement, no cuts. (Tied 14th of 28 on this prompt)
  • Wrong-way escalator: A fixed shot of an empty upward-moving escalator. A single red rubber ball bounces down the escalator steps from top to bottom, against the direction of the escalator's movement, and exits the frame at the bottom. No people, no cuts. (Tied 1st of 28 on this prompt)
  • Frozen crowd: A busy pedestrian plaza in daylight where every single person stands perfectly still like a statue for the entire clip. Only the flags on the buildings move in the wind. No person moves at all, not even slightly. Fixed camera, no cuts. (Tied 1st of 28 on this prompt)
  • Sign stays readable: A slow steady dolly toward a shop window sign reading exactly "OPEN 7 DAYS - COFFEE & BAGELS - EST. 2003". The text stays sharp, correctly spelled and unchanged for the whole clip as the camera approaches. No other text in the frame. (Tied 13th of 28 on this prompt)
  • Glass of water: A fixed close shot of a full glass of water on a wooden table. A rolling orange bumps into the glass. The glass tips over away from the camera, water spills across the table and spreads outward, and the empty glass rolls slightly and comes to rest. No hands, no cuts. (Tied 3rd of 28 on this prompt)
  • Domino run: A fixed side-on shot of exactly eight upright black dominoes in a straight evenly spaced row on a white surface. The leftmost domino tips right and the whole row falls in a single left-to-right chain until all eight are flat. No hands after the start, no cuts. (Tied 7th of 28 on this prompt)
  • Mirror walk: A fixed shot of a large wall mirror in a bright dance studio. A man in a green t-shirt walks from left to right in front of the mirror. His reflection matches his movement exactly, mirrored, for the entire clip. No cuts. (Tied 18th of 28 on this prompt)
  • Behind the pillar: A fixed camera on a wide walkway with a single thick concrete pillar in the middle. A woman in a yellow raincoat walks at a steady pace from the left edge to the right edge, passing behind the pillar. She is briefly hidden, then reappears on the other side wearing exactly the same clothes, and exits frame right. No cuts. (Tied 1st of 28 on this prompt)
  • Steaming mug: Thin steam rises from the coffee and drifts slowly to the right. Everything else holds still: the mug and its painted design, the table, the background, the lighting and the framing stay exactly as in the reference. Static camera. No cuts. (Tied 1st of 27 on this prompt)
  • Walk out: The person from the reference turns and walks out of the frame to the right. Their face, hair and clothing stay exactly as in the reference until they exit. The background does not change. No cuts. (Tied 12th of 26 on this prompt)
  • Continuous shot: A single continuous shot, no cuts, orbiting exactly once, slowly, around a stone statue of a horse in an empty courtyard. The statue stays centred the whole time and the camera ends where it started. Nothing else enters the frame. (Tied 2nd of 28 on this prompt)

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Not currently available through EU in-region routing. In-region routing docs

Documentation

Guides, cookbooks, blog posts, and tools for building with Alibaba: HappyHorse 1.1 on OpenRouter.

  • Best Image-to-Video AI Models in 2026: Cost, Resolution, and ControlBlog post

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End-to-end latency
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End-to-end latency
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P50, best provider

Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
91.73%

Availability over the last 3 days

Last 72 hours
Availability 91.73%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
93.34%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

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Frequently asked questions

HappyHorse 1.1 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds.

The HappyHorse 1.1 documentation on OpenRouter includes the Best Image-to-Video AI Models in 2026: Cost, Resolution, and Control blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.

HappyHorse 1.1 costs from $0.0988/second at 720p to $0.1278/second at 1080p.

HappyHorse 1.1 supports 3–15 second clips and can be steered with a first frame image.

HappyHorse 1.1 accepts text and images as input and returns video.

Wan 3.0 Prime, Wan 3.0, HappyHorse 1.0 and 2 more are other video models from Alibaba.

HappyHorse 1.1 was released on June 24, 2026.

More models from alibaba

Wan 3.0 Prime

Wan 3.0 Prime is a fast-mode variant of Wan 3.0 from Alibaba. It supports text-to-video and first-frame image-to-video generation.

Videofrom $0.068/second
Wan 3.0

Wan 3.0 is a video generation model from Alibaba for text-to-video, image-to-video, and reference-guided video generation. It produces 480p, 720p, or 1080p video with durations from 2 to 30 seconds.

Videofrom $0.0425/second
HappyHorse 1.1

HappyHorse 1.1 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds. It is suited for creative content, social media clips, and image-driven animation, and improves on the prior version with stronger prompt adherence, smoother motion, and more consistent characters across frames.

Videofrom $0.0988/second
HappyHorse 1.0

HappyHorse 1.0 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds. It is suited for creative content, social media clips, and image-driven animation across a range of aspect ratios.

Videofrom $0.0988/second
Wan 2.7

Wan 2.7 is a video generation model from Alibaba. It supports text-to-video, image-to-video with first and last frame control, and reference-to-video, where multiple reference images guide the style and content of the generated scene.

Video$0.10/second
Wan 2.6

Alibaba's most advanced video generation model, supporting over 10 visual creation capabilities in a unified system. Wan 2.6 generates 1080p video at 24fps from text, images, reference videos, or audio, with native audio-visual synchronization and precise lip-sync. Key features include reference-to-video (insert a character's appearance and voice into new scenes), multi-shot storytelling from simple prompts, synchronized sound effects and music, and support for 16:9, 9:16, and 1:1 aspect ratios with clips up to 15 seconds.

Videofrom $0.04/second
Tongyi DeepResearch 30B A3B

Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.

The model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.

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