HunyuanVideo (Tencent) and Wan2.2 (Alibaba) are both open video generation models. HunyuanVideo is a single large model with heavy hardware demands, while Wan2.2 is a family of checkpoints built around a Mixture-of-Experts design and explicit control over cinematic look. For motion designers, that means Wan2.2 offers more ways to fit your hardware and steer lighting and composition, and HunyuanVideo is a heavier option that earned its reputation on cinematic quality. Styleframe sits beside both as a complement inside an After Effects or Nuke workflow, not as a replacement for either.

HunyuanVideo

HunyuanVideo is Tencent's video generation model, supporting text-to-video and image-to-video, and it is positioned for cinematic-looking footage with natural movement. Compared with Wan2.2, it is a single large model (about 13B parameters) with a reputation for slow generation and high VRAM needs, with 24GB or more commonly cited. Pick it if you already have strong GPU capacity, want to run it through ComfyUI (quantized GGUF versions exist to reduce memory use), and care more about its visual character than about iteration speed.

Wan2.2

Wan2.2 is Alibaba's major upgrade to the Wan family of open video models, released under Apache 2.0. Unlike HunyuanVideo, it splits denoising between high-noise and low-noise expert models to expand capacity without raising compute cost, and it was trained on aesthetic labels covering lighting, composition, contrast, and color tone. Its checkpoints (T2V-A14B, I2V-A14B, and the compact TI2V-5B) cover different hardware tiers, and the 5B model targets 720p at 24 fps on a consumer card like an RTX 4090. Pick it if you want more control over the look of a shot, need to run on more modest hardware, or want access to the wider Wan toolset, including the Animate-14B model for character animation and replacement and the S2V-14B model for audio-driven video.

How they compare in practice

Architecture and scale. Wan2.2's expert-split design is its main technical difference from HunyuanVideo. Its training set also grew by roughly 65% more images and 83% more videos than Wan2.1, which the Wan team says improves motion, semantics, and aesthetics.

Hardware. This is where the gap is clearest. HunyuanVideo's footprint is demanding, and Wan2.2 gives you a smaller 5B option. Earlier benchmarks comparing HunyuanVideo with Wan2.1 on an A100 showed Wan2.1 14B finishing a standard clip in roughly a third of Hunyuan's time. Those figures predate Wan2.2, so treat them as directional rather than as a measurement of the current models.

Control. Wan2.2's aesthetic labeling is meant to let prompts steer lighting, color, and composition more precisely. For artists matching a board or a lighting setup, that matters more than raw benchmark scores. Both models accept image input, so you can anchor a shot to a still instead of relying on text alone.

Evidence quality. Be cautious with public comparisons. Most head-to-head tests online pit HunyuanVideo against Wan 2.1, not Wan 2.2, and many are single-prompt demos or marketing-flavored takes. Software comparison pages for the two models mostly repeat vendor descriptions and carry no user ratings.

Which one should you pick?

• Pick HunyuanVideo if you have a high-VRAM workstation or cloud GPU, are comfortable with long generation times, and like its cinematic output on your own tests.
• Pick Wan2.2 if you need flexibility across hardware, want finer influence over look and lighting, or plan to use character animation or audio-driven variants.
• Run both if you are building a ComfyUI-based pipeline anyway. Both are supported there, so swapping the model on the same reference frame costs you little.

Whichever you choose, evaluate on your own material. Use a real storyboard frame, a real character, and the camera move you actually need. The difference between "impressive demo" and "usable plate" shows up when you try to match an existing shot.

Where Styleframe fits

Neither model is a finished production tool on its own. Both give you raw clips that still need consistent style, upscaling, cleanup, and compositing. Styleframe (styleframe.ai) is useful in that gap. It is aimed at artists who want more control over the look and finish of generated footage while keeping their compositing pipeline in After Effects or Nuke. If you already generate with Hunyuan or Wan2.2 locally, Styleframe is a way to push the look and finish of that material while keeping your compositing pipeline intact.

Practical testing checklist

Before committing to either model for a project, run a short, structured test:

1. Pick three shots: one human motion, one environment or lighting-heavy shot, and one stylized or abstract shot.
2. Generate the same shot from the same reference frame in each model.
3. Compare temporal stability, edge detail, and how well each follows your lighting direction.
4. Note render time per clip on your actual hardware, not a published benchmark.
5. Check how each result holds up after being brought into After Effects or Nuke, where flicker, banding, and edge crawl become obvious.

That process will tell you more than any leaderboard, because your shots and your tolerance for iteration time are specific to your work.

Bottom line

HunyuanVideo and Wan2.2 are both capable open models, but they serve different working conditions. HunyuanVideo suits artists with ample GPU headroom who like its cinematic output. Wan2.2 suits those who want hardware flexibility, aesthetic control, and a broader family of models. Test each on a real shot from your project, and pick the one that cleans up best in your compositing stage.