AI Video Generator Comparison 2026: Open Source Models Tested

Open-source video generation finally grew teeth in 2026. Twelve months ago, running an AI video generator on your own GPU meant either a 45GB Hunyuan checkpoint that would not fit on a consumer card or nothing. Today you have three real contenders: Wan 2.2 from Alibaba, Mochi-1 from Genmo, and HunyuanVideo-1.5 from Tencent. Two of them are Apache 2.0, the third ships under a community license with carve-outs, each one runs on a single consumer-grade GPU with the right quantization, and each one generates clips you can actually use. This open source AI video generator comparison puts the three side by side on VRAM, speed, quality, and license.

Why This AI Video Generator Comparison Matters

Closed APIs are the easy answer, and they are not always the right one. You pay per second, you ship your prompts to a third party, and you do not control the upgrade cadence. If you generate more than a few hundred seconds of video a day, if you need consistent output across months, or if you work on content your legal team wants kept on-prem, a self-hosted AI video generator is the only path that scales.

The question is which open-source model to commit to. Wan 2.2, Mochi-1, and HunyuanVideo-1.5 are the three weights that actually landed in the open with usable quality on consumer hardware. We tested them against the same prompts on the same hardware class, so you can see where each one wins and where each one still hurts.

Methodology: Prompts, Hardware, and Quantization

We used five prompts across three categories: a slow dolly push on a product still, a wide outdoor landscape with moving clouds, a character walking toward the camera in city lighting, a fast action sports clip, and a stylized abstract data visualization. Each prompt ran text-to-video where supported, and image-to-video where the model offered it (Wan 2.2 I2V and HunyuanVideo-1.5 I2V).

Hardware target was a single RTX 4090 24GB box on Ubuntu 24.04 with CUDA 12.4 and PyTorch 2.5. We also spot-checked results on an A100 80GB for the Wan 2.2 A14B variants that do not fit on 24GB without aggressive offloading. Quantization used per model is listed in the results table. Numbers below are a mix of our hands-on runs and community-reported benchmarks, cited inline, because full 720p runs on every model on every prompt would burn a week of GPU time and this is a draft comparison, not a funded study.

The Three Open-Source AI Video Generator Contenders

Wan 2.2. Alibaba shipped Wan 2.2 in July 2025 as the successor to Wan 2.1, with the consumer story built around the 5B parameter Wan2.2-TI2V-5B model that natively handles text-to-video and image-to-video in a single checkpoint at 720p, 24fps, on a 24GB RTX 4090. The 14B flagships, Wan2.2-T2V-A14B and Wan2.2-I2V-A14B, target 80GB cards for maximum quality. Source lives in the Wan-Video/Wan2.2 repo under Apache 2.0. The older Wan 2.1 1.3B model is still around as a lightweight 8GB VRAM fallback.

Mochi-1. Genmo released mochi-1-preview on HuggingFace under Apache 2.0, with the genmoai/mochi repo on GitHub. The architecture is a 10B parameter asymmetric diffusion transformer. Stock weights need roughly 60GB of VRAM on a single GPU, the bfloat16 variant drops that to around 22GB, and full precision needs about 42GB for maximum quality. The weights have not been refreshed since September 2025, so treat this as a stable research baseline rather than an actively evolving flagship.

HunyuanVideo-1.5. Tencent released HunyuanVideo-1.5 on November 20, 2025 as the consumer-friendly successor to the original 13B HunyuanVideo. It is an 8.3B parameter DiT with a 3D causal VAE, runs on 14GB of VRAM with pipeline and group offloading enabled (peak 13.6GB on a 4090 at 720p, 121 frames, per community benchmarks), and ships with an 8 to 12 step distilled 480p I2V variant that generates a clip in roughly 75 seconds on a single RTX 4090. Source is in Tencent-Hunyuan/HunyuanVideo-1.5. Licensing is the Tencent Hunyuan Community License, which allows commercial use with territory and user-count carve-outs that we cover below. The original 13B HunyuanVideo weights in the Tencent-Hunyuan/HunyuanVideo repo are still available for teams with 80GB cards that want the absolute quality ceiling.

Results Table: VRAM, Speed, Resolution, License

A few things to pull out of this table. Wan 2.2 TI2V-5B is the single best starting point on a 24GB card if you want a 720p, 24fps, Apache 2.0 model in one checkpoint. Wan 2.1 1.3B is still the lightest option at 8GB for dev-machine iteration. HunyuanVideo-1.5 is the biggest surprise of 2026, it drops the original Hunyuan's 45GB VRAM floor to roughly 14GB on a 4090 through a mix of smaller parameters, offloading, and tiling, while reaching 720p with 121 frames. Mochi-1 sits in the middle, cheaper than HunyuanVideo classic and more flexible than Wan 1.3B, but slower than the Wan 2.2 5B model at equivalent resolution.

Winner by Use Case

Longest clips. Wan 2.2 TI2V-5B for a clean 720p 24fps 5 second take on consumer hardware, or the 14B A14B variants on an 80GB card for the premium deliverable. Mochi-1 tops out at around 5.4 seconds at 30fps in the stock pipeline. HunyuanVideo-1.5 generates up to 121 frames at 24fps, about 5 seconds, and the original 13B HunyuanVideo matches that length on an 80GB card. If the brief is a hard 10 second shot, no open-source model delivers it in one pass today, you splice two generations.

Highest fidelity on prompt following. HunyuanVideo-1.5 with the non-distilled path on an RTX 4090 trades speed for the cleanest motion and camera work we tested on consumer hardware. On an 80GB card, the original 13B HunyuanVideo still edges it out on complex multi-subject scenes, but the gap is small and shrinking fast. Wan 2.2 A14B on the same 80GB tier is competitive on cinematic prompts.

Lowest VRAM, fastest iteration. HunyuanVideo-1.5 distilled I2V at 480p, which finishes a clip in roughly 75 seconds on an RTX 4090 and fits in about 14GB with offloading enabled (peak 13.6GB for 720p 121-frame T2V in community benchmarks). Wan 2.1 1.3B is the 8GB runner up at about 4 minutes for a 5 second 480p clip, useful on older 3060 and 4060 cards.

Best license for commercial use. Wan 2.2, Wan 2.1, and Mochi-1, all Apache 2.0, all unambiguously commercial. HunyuanVideo-1.5 ships under the same Tencent Hunyuan Community License as the original. The license explicitly excludes the European Union, United Kingdom, and South Korea from its territory, and requires a separate agreement if your platform serves more than 100 million monthly active users on the version release date. For a small team in North America, all three are viable. For an EU consumer app, both HunyuanVideo versions are off the table.

Honest Limitations We Hit

Wan 2.2. The 5B TI2V model is the sweet spot on a 24GB card, but generation time is still roughly 9 minutes per 5 second 720p clip without custom optimization, so interactive prompt iteration hurts. The A14B flagships want 80GB and do not fit on consumer cards at all in their stock configuration. Text rendering inside generated frames is still rough across every Wan variant.

Mochi-1. The bfloat16 variant fits on a 4090 but is slow, and the fp8 path with CPU offloading is roughly twice as slow again. Output at 480p is fine for social and product clips, but it does not compete with HunyuanVideo-1.5 or Wan 2.2 at 720p for a premium deliverable. Prompt adherence on complex compositions is weaker than Wan, and the weights have not been refreshed since September 2025.

HunyuanVideo-1.5. The VRAM story is far better than the 13B original, but first-run setup is still fiddly, you need pipeline offloading, group offloading, and VAE tiling all enabled to hit the 13.6GB peak on a 4090. Without those flags, expect OOM. The 720p, 121-frame path is closer to 15 to 25 minutes on a 4090 than the 75 second distilled figure that gets quoted most often, which applies to the 480p I2V distilled variant only. And the Tencent community license means you are still blocked in the EU, UK, and South Korea for commercial deployment.

All three. Frame consistency across long clips is still the hard problem. Motion collapses, faces morph, and hands do the hands thing. A 10 second clip with a single character rarely comes out usable on the first generation in any of the three models.

Replication Commands for Each AI Video Generator

Here are the starting commands for each. All assume you have CUDA 12.4, PyTorch 2.5, and at least 64GB of system RAM.

Wan 2.2 TI2V-5B text-to-video and image-to-video.

git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2
pip install -r requirements.txt
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./models/Wan2.2-TI2V-5B
python generate.py --task ti2v-5B --size 1280*720 \
  --ckpt_dir ./models/Wan2.2-TI2V-5B \
  --prompt "a sleek server rack pulsing with neon data streams, slow dolly in"

Mochi-1 via Diffusers.

import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video

pipe = MochiPipeline.from_pretrained(
    "genmo/mochi-1-preview",
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

frames = pipe(
    "a product photo of a laptop rotating on a dark glass table, cinematic",
    num_frames=84,
).frames[0]

export_to_video(frames, "mochi.mp4", fps=30)

HunyuanVideo-1.5 inference.

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5.git
cd HunyuanVideo-1.5
pip install -r requirements.txt
huggingface-cli download tencent/HunyuanVideo-1.5 --local-dir ./ckpts
python sample_video.py \
  --prompt "a wide landscape with moving clouds over a neon city at dusk" \
  --video-size 720 1280 --video-length 129 --seed 42

Each one will hit OOM on the first run with default settings if your GPU is tight. Read the repo's memory optimization section before you file an issue, the maintainers have all written detailed notes on offloading flags and tile sizes.

Which Open Source AI Video Generator Should You Ship On

For most builders in 2026 the answer is a two-model stack. Start with Wan 2.2 TI2V-5B as your default 720p Apache 2.0 model on a 24GB card, then reach for HunyuanVideo-1.5 when you want the quality ceiling on the same hardware and you are shipping outside the EU, UK, and South Korea. Use Wan 2.1 1.3B as the 8GB fallback on older cards or for batch prompt exploration where speed matters more than fidelity. Reserve Mochi-1 for research work, Apache 2.0 commercial deploys in regions where HunyuanVideo is not an option, or the niche where its specific output style fits the brief.

If you have an 80GB card available, Wan 2.2 A14B and the original 13B HunyuanVideo share the quality ceiling on difficult prompts, but the gap between that tier and HunyuanVideo-1.5 on consumer hardware is closing fast. A year from now the distilled and optimized variants will probably close it completely.

Run Your First Open-Source Clip in 10 Minutes

1. Pull Wan-Video/Wan2.2 and pip install -r requirements.txt
2. huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./models/Wan2.2-TI2V-5B
3. Run the generate command from the snippet above with your own prompt
4. Open the resulting mp4 in VLC and compare against your mental reference
5. Tweak the prompt, not the hyperparameters, until it matches your brief

That is the on-ramp. You will know within an hour whether open-source AI video is ready for your specific use case, without spending a dollar on a hosted API.

Sources and Further Reading

• Wan-Video/Wan2.2 on GitHub
• Wan2.2-TI2V-5B on HuggingFace
• Wan2.2-T2V-A14B on HuggingFace
• Wan2.2-I2V-A14B on HuggingFace
• Wan2.1-T2V-1.3B on HuggingFace (legacy 8GB fallback)
• genmo/mochi-1-preview on HuggingFace
• genmoai/mochi on GitHub
• tencent/HunyuanVideo-1.5 on HuggingFace
• Tencent-Hunyuan/HunyuanVideo-1.5 on GitHub
• tencent/HunyuanVideo on HuggingFace (original 13B)
• Tencent-Hunyuan/HunyuanVideo on GitHub (original 13B)
• HunyuanVideo-1.5 Technical Report (arXiv)