HEAD-TO-HEAD SPECIFICATION & VRAM MATRIX

HunyuanVideo (13B) vs Wan 2.1 14B

Side-by-side technical trade-offs across real GGUF quantization VRAM math (24GB target rig), context retention, commercial license terms, and brutal truth verdicts.

COMPARED ENGINES2 / 3

+Add a 3rd engine to this comparison:

01Video

Tencent

HunyuanVideo (13B)

Tencent's frontier 13B dual-stream diffusion transformer for video generation. Produces photorealistic 720p/1080p cinematic video rivaling proprietary models under Apache 2.0.

SELF-HOST

ENTRY COSTFree weights; compute cost applies

80/100INDEX SIGNAL

Engine Page →Hugging Face ↗

02Video

Wan / Alibaba

Wan 2.1 14B

Wan-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.

USE IT

ENTRY COSTFree weights; GPU compute costs extra

82/100INDEX SIGNAL

Engine Page →Hugging Face ↗

SIDE-BY-SIDE TECHNICAL MATRIX

Where each engine wins: HunyuanVideo (13B) vs Wan 2.1 14B

2 engines · 13 criteria

CRITERION	HunyuanVideo (13B)Tencent	Wan 2.1 14BWan / Alibaba
•VERDICT	SELF-HOST	USE IT
•THE BRUTAL TRUTH	The Sora of open source. If you have an RTX 4090 or A6000, the video fidelity destroys everything else on the open market under Apache 2.0.	The most accessible open video foundation model released to date. Apache 2.0 with modular Comfy-Org safetensors and low-VRAM GGUF builds.
•CATEGORY	Video	Video
•ARCHITECTURE & PARAMS	HunyuanVideo (13B)	Wan 2.1 14B (T2V / I2V Diffusion Transformer)
•QUANTIZATION & MIN VRAM	GGUF Q4: 18GB · FP8: 24GB · BF16: 44GB	GGUF Q4: 6GB · GGUF Q8: 10GB · FP16: 24GB
•RECOMMENDED VRAM	23 GB (with KV Cache)	8 GB (with KV Cache)
•CONTEXT WINDOW	Text-to-video (Dual-Stream DiT)	Text-to-video / image-to-video
•LICENSE TERMS	Apache 2.0	Apache 2.0
•PRICING & ENTRY COST	Free weights · your hardware	Free weights · your hardware
•TRUE FOUNDATION ENGINE?	✓ Foundation Engine	✓ Foundation Engine
•STRENGTHS	Highest photorealism and physics accuracy of any open video model · Dual-stream video DiT with MLLM text understanding · Apache 2.0 licensing	State-of-the-art open-weight video generation quality · Apache 2.0 permissive commercial license · Official Comfy-Org modular repacks and GGUF quantizations
•KNOWN GOTCHAS	Extremely heavy: requires 24GB GPU at minimum with offloading · Generation takes 5-15 minutes on single GPUs	High generation compute latency on consumer GPUs · Requires separate UMT5 text encoder and 3D VAE
•CLOUD GPU DEPLOY	Deploy on RunPod ($0.14/hr) ↗	Deploy on RunPod ($0.14/hr) ↗

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.