Three open-source video models lead in 2026, and they win at different things: Wan 2.2 for photorealism and people, HunyuanVideo for natural motion and physics, and LTX for speed and low hardware. All three run on your own GPU for free, so the choice comes down to what you are making and which card you own. Here is how they compare on quality, hardware, speed, and licensing.
These are the models behind running video locally on your own PC; if you want the cloud side instead, our comparison of hosted generators covers Veo, Kling, and the rest.
Why go open-source for AI video?
Open models trade a little peak quality for a lot of freedom. You run them on your own hardware, so generation is free after the GPU, nothing leaves your machine, and there is no subscription or per-clip meter. For high-volume or private work that is decisive, and the quality gap to the best cloud tools narrows with every release.
Be honest about the trade. The very best hosted models still edge these on the hardest shots, and you take on setup plus a GPU bill the cloud hides. What you get back is control and cost: no metered credits, no data leaving your machine, and the freedom to tune and script the pipeline yourself. For a creator generating daily, or anyone under confidentiality, that trade favors open every time.
These models also improve fast, and openly. Because the weights are public, the community fine-tunes and optimizes them constantly, so a card that struggled last year often runs this year's build fine. Betting on open source is betting on that curve continuing, and it has not slowed yet.
Three names cover almost every open-source use case in 2026: Wan 2.2 from Alibaba, HunyuanVideo from Tencent, and LTX from Lightricks. Others like CogVideoX and Mochi exist, but these three are where most people land, and each has a clear strength. Picking well means matching the model to your priority, not chasing a single "best" for everything.
Wan 2.2: the quality leader
Wan 2.2 produces the best photorealism among open models, especially for people: facial detail and skin hold up where other models go plasticky. It is built on a mixture-of-experts design that splits the work across specialized networks, so it reaches near-top quality without a matching jump in compute. A 5-billion-parameter variant even runs on 8 GB of VRAM, which makes it the most accessible of the three on a mid-range card.
Its licensing is the other big draw: Wan 2.2 is Apache 2.0, fully commercial with no strings, so anything you generate is yours to sell. One model also handles both text-to-video and image-to-video in a single setup. If your work features human subjects, start here, and pair it with our guide on keeping a character consistent across clips.
The mixture-of-experts design is why Wan punches above its size. Instead of one network doing everything, it routes each part of the job to a specialist, so you get high quality without the compute a single dense model of that quality would demand. Its weaker spot is fast, chaotic motion, where HunyuanVideo pulls ahead, but for portraits and steady scenes Wan is the open benchmark.
HunyuanVideo: the motion specialist
HunyuanVideo, from Tencent, is the one to reach for when movement matters. It handles natural motion and physics better than the others: water and smoke behave believably, and objects interact the way the eye expects. For action or anything where stiff, uncanny motion would break the illusion, it is the strongest pick of the three.
The catch is licensing. HunyuanVideo ships under Tencent's community license, which needs review for some commercial uses, so check the terms before you build a paid product on it. It also asks more of your hardware than Wan's small variant, though a quantized build runs on a 24 GB card.
Think of the shots that expose bad AI physics: a person pouring water, or a flag moving in wind. Those are where HunyuanVideo earns its place, because stiff or floaty motion reads as fake instantly. If your content leans on believable movement rather than a perfect still face, it is worth the heavier setup.
LTX: the speed champion
LTX, from Lightricks, wins on speed and accessibility. It generates a five-second clip in under thirty seconds on an RTX 4090, several times faster than Wan or HunyuanVideo, and that changes how you work: you iterate in seconds instead of waiting minutes per try. For volume and fast experimentation, nothing open touches it.
LTX is also the first open-weights model to generate synchronized video and audio, dialogue and lip-sync included, in a single pass at high resolution. The trade-off is a separate commercial agreement with Lightricks rather than a permissive license, so confirm the terms for paid use. If you value iteration speed and built-in audio over absolute photorealism, LTX is the one.
The single-pass audio is a bigger deal than it sounds. Every other open model hands back silent video that you then score and sync separately; LTX gives you a clip that already talks and has ambience, cutting out a whole editing stage. The official LTX site lists the current builds and their hardware needs if you want the lighter, faster one.
Which one should you pick?
Match the model to your priority. Choose Wan 2.2 for photorealistic people and the cleanest commercial license, HunyuanVideo for believable motion and physics, and LTX for speed and one-pass audio. Many people keep two installed and switch by shot rather than committing to one.
Getting them is easier than it was. All three publish their weights on Hugging Face, and ComfyUI has ready nodes and community workflows for each, so you rarely start from a blank setup. Download the model, load a shared workflow, and you are generating the same afternoon.
No capable GPU yet? You have two moves: rent one by the hour in the cloud and run the same models there, or start with Wan's 8 GB variant on whatever card you have. Neither locks you in, and both cost far less than a year of cloud-tool subscriptions. Open source has an entry ramp at almost every budget.
Hardware decides the shortlist as much as taste. On a mid-range 8 GB card, Wan's small variant is realistically your only option; with 24 GB you can run all three in quantized form; and the heaviest configurations want more still. Once you know your GPU and your priority, the choice usually makes itself. To set any of them up in practice, our guide to generating AI video locally walks through the install.
One mistake to avoid: picking by benchmark scores instead of your actual shots. A model that tops a leaderboard on cinematic scenery may be the wrong choice for talking-head clips on your card. Test your two or three most common shot types on each before you commit; ten minutes of your own footage tells you more than any comparison table, including this one.
Want to build a real workflow around these tools? The Future Tech program teaches AI video production end to end, on open-source or cloud models.
