My Stack0+ Submit ToolSide-by-side technical trade-offs across real GGUF quantization VRAM math (24GB target rig), context retention, commercial license terms, and brutal truth verdicts.
01VideoWan-AI's official 14B open-weight video foundation model for text-to-video and image-to-video generation, with native ComfyUI and Comfy-Org modular workflow repacks.
02VideoA hosted generative video workflow for creating and iterating on cinematic clips, with strong creative controls and a credit-based model.
03VideoOpenAI's cinematic text-to-video model, available through ChatGPT Plus and Pro tiers, with the highest visual fidelity in the current market.
| CRITERION | Wan 2.1 14BWan / Alibaba | Runway Gen-3 AlphaRunway | SoraOpenAI |
|---|---|---|---|
| VERDICT | USE IT | USE IT | WAIT |
| THE BRUTAL TRUTH | The most accessible open video foundation model released to date. Apache 2.0 with modular Comfy-Org safetensors and low-VRAM GGUF builds. | The pro-grade video standard. Credits burn fast — budget for 3-5x your initial estimate. Sora is better fidelity but less controllable. | Best-in-class fidelity, but no standalone API, geoblocked in many regions, and queue times can be brutal. Wait for the API before building on it. |
| CATEGORY | Video | Video | Video |
| ARCHITECTURE & PARAMS | Wan 2.1 14B (T2V / I2V Diffusion Transformer) | Runway Gen-4 family | Sora 2 |
| QUANTIZATION & MIN VRAM | GGUF Q4: 6GB · GGUF Q8: 10GB · FP16: 24GB | Hosted only | Hosted only |
| RECOMMENDED VRAM | 8 GB (with KV Cache) | Cloud API | Cloud API |
| CONTEXT WINDOW | Text-to-video / image-to-video | Text-to-video / image-to-video | Text-to-video |
| LICENSE TERMS | Apache 2.0 | Plan terms | OpenAI terms |
| PRICING & ENTRY COST | Free weights · your hardware | Free credits / $15 mo per seat | Via ChatGPT plans per seat |
| TRUE FOUNDATION ENGINE? | ✓ Foundation Engine | ✓ Foundation Engine | ✓ Foundation Engine |
| STRENGTHS | State-of-the-art open-weight video generation quality · Apache 2.0 permissive commercial license · Official Comfy-Org modular repacks and GGUF quantizations | Strong creative controls for cinematic output · Hosted workflow avoids local GPU setup | Highest visual fidelity in class · Integrated with ChatGPT workflows |
| KNOWN GOTCHAS | High generation compute latency on consumer GPUs · Requires separate UMT5 text encoder and 3D VAE | Credits can constrain experimentation · Feature availability changes over time | No standalone free tier · Generation can be slow at peak demand |
| CLOUD GPU DEPLOY | Deploy on RunPod ($0.14/hr) ↗ | Official Website ↗ | Official Website ↗ |
VRAM Precision Footnote: Stactora benchmarks assume typical KV cache overhead at 8K context and modern runtime engines (Ollama, vLLM, ComfyUI). Actual memory usage may vary with longer context or parallel batching.