I would start with HappyHorse 1.1 for short, reference-heavy 1080p clips and Kling 3.0 for sequences that need shot planning, multilingual dialogue, or native 4K. That is a starting hypothesis, not a universal ranking. The useful distinction is between output preference and production control: a model can win blind comparisons without exposing the controls a particular workflow needs.
The Artificial Analysis snapshot discussed here favors HappyHorse 1.1 over Kling 3.0 1080p Pro in audio-enabled text-to-video and image-to-video. Kling’s documented advantages sit elsewhere: multi-shot generation, richer reference workflows, dialogue direction, and higher-resolution delivery. I would evaluate those separately rather than compress them into one “best model” score.
Start With the Actual Model Routes
Alibaba’s Model Studio documentation lists three HappyHorse 1.1 models: happyhorse-1.1-t2v, happyhorse-1.1-i2v, and happyhorse-1.1-r2v. They cover text-to-video, first-frame image-to-video, and reference-image-to-video. All three support audio and produce 24 fps MP4 video at 720p or 1080p, with durations of 3–15 seconds.
The release dates need some precision. Alibaba’s lifecycle lists June 22, 2026 for international and mainland scopes, and June 26 for the global scope. Its June 23 launch article describes improvements to motion expressiveness, reference consistency, instruction following, visual quality, and audio-visual synchronization. Those are vendor claims; I would not treat them as independent measurements.
Kuaishou announced Kling’s 3.0 series on February 5, 2026, including Video 3.0, Video 3.0 Omni, Image 3.0, and Image 3.0 Omni. The video series supports output up to 15 seconds and combines text, image, reference, and in-video editing workflows across the family. Native 4K generation followed on April 23, 2026.
Standard Video 3.0 is the prompt-led route, with T2V, I2V, start/end frames, native audio, multi-shot generation, multi-character coreference, and multilingual support. Omni extends the reference-driven side with image/video elements and voice-connected workflows. I would check the specific route before integrating: a capability advertised for the series is not necessarily available through every endpoint.
| Constraint | HappyHorse 1.1 | Kling 3.0 |
|---|---|---|
| Provider | Alibaba | Kuaishou / Kling AI |
| Standard resolution | 720p / 1080p | 720p / 1080p |
| Duration | 3–15 seconds | Up to 15 seconds |
| Audio | T2V, I2V, and R2V | Native audio supported |
| References | 1–9 still images in R2V | Image, element, and video workflows |
| Shot planning | No headline native storyboard feature in 1.1 docs | Multi-shot; custom storyboard capabilities |
| Higher-resolution delivery | No native 4K listed for 1.1 | Native 4K in the 3.0 series |
What the Benchmark Supports, and What It Does Not
Artificial Analysis Video Arena uses blind human preference votes on matched prompts. Elo represents relative preference, not deterministic accuracy. The comparable entries here are HappyHorse 1.1 and Kling 3.0 1080p Pro, not every Kling variant or production route.
| Audio-enabled arena | HappyHorse 1.1 Elo | Kling 3.0 1080p Pro Elo | Snapshot ranks |
|---|---|---|---|
| Text-to-video | 1151 | 1112 | #4 vs #6 |
| Image-to-video | 1112 | 1076 | #5 vs #13 |
The I2V leaderboard and T2V snapshot both favor HappyHorse. Sample counts are already in the thousands, so this is a useful signal for deciding which model to test first. Scores and ranks remain dynamic as votes arrive; I would retain the snapshot alongside any evaluation report rather than present these numbers as permanent properties.
For ordinary audio-enabled T2V or I2V, HappyHorse therefore gets my first test. That conclusion does not establish a winner for reference-to-video, controlled dialogue, storyboards, or 4K. The comparisons do not isolate those capabilities, and they certainly do not guarantee that every HappyHorse generation beats every Kling generation.
Motion is another place where I would keep the evidence categories separate. Alibaba explicitly describes improved motion modeling and temporal consistency, including complex action. Kuaishou emphasizes photorealism, dynamic performance, precise shot control, and more complex sequences. Those descriptions help design test cases, but neither replaces inspecting motion failures in your own outputs.
Match the Reference System to Your Assets
Still Images: HappyHorse’s Direct Approach
HappyHorse’s R2V API accepts 1–9 reference images. Prompts can refer to them explicitly as [Image 1], [Image 2], and so on, and the R2V prompt field supports any language. That is a useful contract when the brief arrives as separate approved assets: a product packshot, a spokesperson, wardrobe, accessories, and a scene reference.
I like this workflow for e-commerce and brand clips because the inputs map directly to the brief. There is no need to rely on a single composite reference to communicate every subject. The intended use is to combine referenced subjects into a prompted scene, although supported input count alone does not prove that every detail will survive generation.
For a 3–15 second product reveal, social ad, character clip, or concept asset delivered at 1080p, this makes HappyHorse a sensible starting point. Alibaba also highlights improved audio-visual synchronization, so I would include sound alignment in acceptance checks rather than assess only the frames.
Video and Voice References: Kling’s Broader Controls
Kling uses a broader element/reference system. Kuaishou says standard Video 3.0 can use reference videos and multiple image references; Omni can extract visual traits and voice characteristics from reference video and reuse them in new scenes. That is a different requirement from assembling a scene from still images.
I would start with Omni when recurring subjects must persist through a structured narrative, especially when the reference package includes video or voice. For still-image product work, HappyHorse’s explicit image indexing is attractive. For a recurring spokesperson across several directed shots, Kling’s reference and storyboard controls deserve the first evaluation.
Shot Structure, Dialogue, and Delivery Can Decide the Choice
Kling’s clearest structural advantage is intelligent multi-shot storytelling. Its documented examples include shot-reverse-shot dialogue, cross-cutting, voice-over, and camera changes driven by narrative instructions. Video 3.0 can interpret multi-scene, multi-shot prompts and adjust angles and coverage.
Omni adds storyboard controls for each shot’s duration, shot size, perspective, narrative content, and camera movement. If one generation needs to produce a planned mini-sequence, those controls matter more to me than a modest preference-score advantage. HappyHorse can generate cinematic clips, but its public 1.1 documentation centers T2V, first-frame I2V, and multi-image R2V, not a comparable native storyboard system.
Both families support audio. Kling has the more explicit dialogue specification: Chinese, English, Japanese, Korean, and Spanish, plus English accents and Chinese dialects. Kuaishou also documents multi-character scenes with different languages and controlled speaking order. HappyHorse’s multilingual R2V prompt field should not be confused with an equivalent documented speech-language feature set.
Resolution creates another hard constraint. HappyHorse’s documented 720p/1080p, 24 fps MP4 output covers many web, social, product-page, and internal production tasks. Kling’s native 4K path is relevant when the final deliverable requires 4K or needs additional raster headroom for cropping and post-production. It is a finishing capability, not proof of better motion or more faithful subjects. I would also verify the audio configuration: the priced 4K route below is no-audio.
Compare Prices at the Route Level
My baseline would be Alibaba’s official pricing for the international scope and Kling’s official API pricing. HappyHorse lists $0.14/s at 720p and $0.18/s at 1080p. Kling distinguishes no-audio generation from native audio without Voice Control; Motion Control has the same listed rates as those native-audio routes.
A unified multi-model API is relevant when running this comparison through shared credentials and billing. CometAPI lists the gateway prices below, each 20% below the corresponding standard provider list/base rate; that does not guarantee a lower price than a temporary Alibaba promotion. Its HappyHorse listing describes a /v1/videos workflow, while the Kling catalog separates v3, v3 Omni, native-audio, Motion Control, and 4K options.
| Route | Official provider price | Gateway price |
|---|---|---|
| HappyHorse 1.1, 720p | $0.140/s | $0.112/s |
| HappyHorse 1.1, 1080p | $0.180/s | $0.144/s |
| Kling 3.0, 720p, no audio | $0.084/s | $0.0672/s |
| Kling 3.0, 1080p, no audio | $0.112/s | $0.0896/s |
| Kling 3.0, 720p, native audio without Voice Control / Motion Control | $0.126/s | $0.1008/s |
| Kling 3.0, 1080p, native audio without Voice Control / Motion Control | $0.168/s | $0.1344/s |
| Kling 3.0, 4K, no audio | $0.420/s | $0.336/s |
The gateway’s Kling native-audio/Motion Control listings are $0.504 for 5 seconds at 720p and $0.672 for 5 seconds at 1080p, equivalent to the per-second figures above. On these comparable audio-enabled routes, Kling is slightly cheaper at both official standard pricing and gateway pricing. Artificial Analysis’ creator-API normalization instead shows HappyHorse as cheaper than Kling 3.0 Pro, which is another reason not to transfer a pricing conclusion between differently defined routes.
Measure Approved Clips, Not Just Generated Seconds
The metric I would optimize is effective cost per approved clip = cost per generation × average attempts required for approval. A model that costs 10% more per generation can still cost less overall if it reduces retries by 30%. Small differences in per-second pricing are easy to overwhelm with rejected outputs.
For a repeatable evaluation, I would keep prompt, duration, aspect ratio, resolution, and reference assets as similar as the APIs permit. Each output should retain model ID, route, price, latency, seed if exposed, human pass/fail label, and failure reason. I would track first-pass acceptance, subject drift, wrong product details, bad hands or faces, prompt non-compliance, motion failures, unusable camera moves, and audio mismatch.
My initial routing would be straightforward: HappyHorse for high-volume short-form clips, e-commerce assets, and characters anchored by still references; Kling 3.0 for prompt-led multi-shot scenes and dialogue; Omni for recurring subjects with video/voice references; Kling’s 4K route for native 4K delivery. For uncomplicated 1080p API generation, I would test both. The benchmark determines where I start; approval rate and the controls the brief actually requires determine what I deploy.
Originally published at cometapi.com
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