Happy Horse vs LTX 2.3
An in-depth look at how Happy Horse 1.0 compares to LTX 2.3 for AI video generation.
Quick Verdict
The closest competition. Happy Horse edges out on visual quality and lip-sync while being smaller and faster. LTX has a larger model, more input types, and a more mature ecosystem. Both are excellent open-source choices.
Specifications
| Feature | Happy Horse 1.0 | LTX 2.3 |
|---|---|---|
| Developer | Happy Horse Team (Sand.ai) | Lightricks |
| Parameters | ~15B | ~22B |
| Inputs | Text / Image | Text / Image / Video / Audio |
| License | Open Source (Commercial) | Apache 2.0 |
| Audio Generation | Yes | Yes |
| Lip-Sync | 7 languages | 5 languages |
| Open Source | Yes | Yes |
| Inference Speed | 38s for 5s 1080p (H100) | ~50s for 5s 1080p |
Benchmark Scores
| Metric | Happy Horse 1.0 | LTX 2.3 | Winner |
|---|---|---|---|
| Visual Quality ↑ | 4.8 | 4.76 | Happy Horse 1.0 |
| Text Alignment ↑ | 4.18 | 4.12 | Happy Horse 1.0 |
| Physical Realism ↑ | 4.52 | 4.56 | LTX 2.3 |
| WER (%) ↓ | 14.6% | 19.23% | Happy Horse 1.0 |
Happy Horse 1.0
Strengths
- + Highest visual quality score (4.80) among tested models
- + Lowest Word Error Rate (14.60%) — best lip-sync accuracy
- + Joint video + audio generation from a single model
- + Fully open source with commercial use rights
- + Fast inference via DMD-2 distillation (8 steps) and MagiCompiler
Weaknesses
- - Weights not yet publicly released (Coming Soon as of April 2026)
- - Requires H100/A100 GPU — not accessible on consumer hardware
- - Best at single-character scenes; multi-person quality drops
- - Limited to ~10 second generation length
- - New model with limited community ecosystem and tooling
LTX 2.3
Strengths
- + Largest parameter count (22B) — most capable architecture
- + Strong physical realism score (4.56)
- + Apache 2.0 license — very permissive open source
- + Supports all input types including video-to-video
- + More mature ecosystem with community tools and fine-tunes
Weaknesses
- - Higher VRAM requirements due to 22B parameter count
- - Slower inference than Happy Horse (no 8-step distillation)
- - Slightly lower visual quality than Happy Horse (4.76 vs 4.80)
- - Higher WER (19.23%) than Happy Horse for lip-sync
- - More complex deployment due to larger model size
Which Should You Choose?
Choose Happy Horse 1.0 if:
Users prioritizing inference speed, lip-sync quality, and joint audio generation
Choose LTX 2.3 if:
Users needing video-to-video capabilities and a mature tool ecosystem
Video Samples
Same prompt, both models — judge the quality yourself.
Prompt #2 A cobblestone street after rain, looking dark and glossy, reflecting the yellow streetlamps perfectly.
Happy Horse 1.0
LTX 2.3 Pro