February 1, 2026
Comparing Open Video Diffusion Models
Testing CogVideoX, Mochi, and LTX Video for quality, speed, and practical usability.
Overview
Video diffusion models have made remarkable progress in recent months. In this research post, we compare three leading open-source video generation models: CogVideoX, Mochi, and LTX Video.
Models Tested
CogVideoX
CogVideoX is a transformer-based video generation model that excels at producing coherent motion sequences. Key characteristics:
- Architecture: Diffusion transformer (DiT)
- Resolution: Up to 720p
- Duration: 6 seconds
- VRAM: 24GB minimum
Mochi
Mochi offers a balance between quality and computational efficiency:
- Architecture: Latent diffusion
- Resolution: Up to 480p
- Duration: 4 seconds
- VRAM: 16GB minimum
LTX Video
LTX Video focuses on fast generation with reasonable quality:
- Architecture: Optimized latent diffusion
- Resolution: Up to 512p
- Duration: 5 seconds
- VRAM: 12GB minimum
Test Methodology
We tested each model with identical prompts across several categories:
- Natural landscapes
- Human motion
- Object manipulation
- Abstract concepts
Results
| Model | Quality | Speed | Coherence | Overall |
|---|---|---|---|---|
| CogVideoX | 9/10 | 6/10 | 8/10 | 7.7/10 |
| Mochi | 7/10 | 8/10 | 7/10 | 7.3/10 |
| LTX Video | 6/10 | 9/10 | 6/10 | 7.0/10 |
Conclusion
Each model has its strengths. CogVideoX leads in quality, Mochi offers the best balance, and LTX Video is ideal for rapid prototyping. Choose based on your specific needs.