HappyHorse-1.0 vs Wan 2.2 vs HunyuanVideo: Which Open-Source AI Video Model Should You Use in 2026?
The open-source AI video landscape now has real options at every hardware tier. Here's how the top three stack up on quality, speed, audio, and GPU requirements.
Where developers once had to choose between a handful of models topping out at 720p with no audio, we now have 15-billion-parameter systems generating native 1080p video with synchronized speech — all running on hardware you can rent by the hour.
This comparison covers HappyHorse-1.0, Wan 2.2, and HunyuanVideo-1.5 — the three most-discussed open-source video generation models of 2026. We'll look at output quality, generation speed, audio capabilities, hardware requirements, and licensing so you can make a practical choice for your actual workload.
At a Glance
| Feature | HappyHorse-1.0 | Wan 2.2 | HunyuanVideo-1.5 |
|---|---|---|---|
| Parameters | 15B | ~14B (MoE) | 8.3B |
| Max Resolution | 1080p | 1080p | 720p |
| Native Audio | ✅ Joint synthesis | ❌ | ❌ |
| Denoising Steps | 8 (DMD-2) | 20–50 | 20–50 |
| Min VRAM | 40 GB (FP8) | 16 GB | 24 GB |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Arena Rank (T2V) | #1 — Elo 1374 | Top 5 | Top 5 |
HappyHorse-1.0: Joint Audio-Video in a Single Pass
HappyHorse-1.0 is currently ranked #1 on the Artificial Analysis Video Arena for both text-to-video (Elo 1374) and image-to-video (Elo 1410) — beating commercial models like Seedance 2.0 and Kling 3.0 Pro.
The architecture is a 40-layer single-stream Transformer where a central 32-layer shared backbone processes visual and audio tokens together. A per-head gating mechanism controls cross-modal blending at fine granularity — which is why lip-sync stays accurate even when speakers switch languages mid-clip.
DMD-2 distillation cuts denoising to just 8 steps. Combined with MagiCompiler full-graph compilation, it generates a 5-second 1080p clip in ~38 seconds on an H100 80GB.
Audio is generated natively — no post-processing TTS step. Lip-sync is supported in 6 languages: English, Mandarin (incl. Cantonese), Japanese, Korean, German, and French. Hardware floor: 40 GB VRAM (FP8 quantized).
Wan 2.2: Cinematic Quality on a Tighter Budget
Wan 2.2 uses a Mixture-of-Experts diffusion backbone, routing denoising to specialized experts per timestep. The practical result is cinematic motion smoothness — especially for crowd scenes and camera movements — without a linear increase in compute.
The biggest win is the 16 GB VRAM floor on quantized variants, letting developers run experiments on RTX 4090s without renting cloud GPUs. Output tops out at 1080p but audio is not native — you need a separate pipeline for speech or sound.
HunyuanVideo-1.5: Efficient Inference, Multi-Person Strength
At 8.3B parameters, HunyuanVideo-1.5 is the lightest of the three. It runs on 24 GB consumer GPUs (RTX 3090, RTX 4090), which makes it useful for fast prototyping. Output maxes at 720p, and audio is not native.
Where Hunyuan consistently outperforms is multi-person scenes — multiple speakers in the same frame tend to stay coherent for longer. For single-subject content, Wan 2.2 and HappyHorse-1.0 both pull ahead on benchmark scores.
Which Should You Use?
Use HappyHorse-1.0 if:
- You need 1080p video with synchronized audio in one inference call
- Your content is multilingual (English + CJK languages)
- You have H100 or A100 80 GB hardware available
- Quality benchmarks matter — it's the current arena leader
Use Wan 2.2 if:
- You need high-quality silent video on a 16–24 GB GPU
- You're assembling your own audio pipeline
- Cost efficiency on consumer hardware is a priority
Use HunyuanVideo-1.5 if:
- You're prototyping on a consumer GPU and 720p is acceptable
- Your scenes involve multiple people
- You want the fastest iteration loop before scaling up
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