The 8 best open source AI video models in 2026

Alibaba's open video model family, widely regarded as the leading open weights for quality and motion.

Wan is the model most self-hosters reach for first. Released under a permissive license with open weights, it delivers strong text-to-video and image-to-video quality that closes much of the gap to closed models, and its motion holds up on demanding prompts. A large community means abundant ComfyUI nodes, LoRAs, and fine-tunes. The heavier variants demand a serious GPU and VRAM, and getting the best results still takes tuning of samplers and settings that a hosted service hides.

An open model from Lightricks tuned for speed, capable of near real-time generation on consumer hardware.

LTX Video is built for efficiency. It generates markedly faster than most open models and runs on more modest GPUs, which makes it a favorite for rapid iteration and for creators without a data-center card. The speed suits previews, drafts, and high-volume experimentation. The trade is that raw fidelity and the hardest motion trail the heaviest models, so it is often used to iterate quickly and then finish elsewhere. Open weights and an active ecosystem keep it accessible.

Tencent's large open video model, known for cinematic quality and strong prompt adherence.

HunyuanVideo is one of the largest open models available, and it shows in the output: cinematic, detailed shots with good prompt adherence that rival closed offerings on the right prompt. Open weights and a growing ecosystem of adapters make it a serious option for teams that can host it. The size is the cost, though: it demands substantial VRAM and compute, generation is slower than lighter models, and the setup is more involved than a plug-and-play tool.

Genmo's open model praised for fluid, high-fidelity motion under a permissive license.

Mochi 1 earned attention for motion quality. It produces smooth, physically believable movement that stands out among open models, released under a permissive Apache license that is friendly to commercial use. For creators who care most about how a shot moves, it is a strong pick. It centers on text-to-video, so image-to-video and some control features are lighter than rivals, and the full model needs capable hardware to run at its best resolution and length.

An accessible open model line with variants that run on relatively modest GPUs.

CogVideoX is a practical entry point into open video generation. It ships in multiple sizes, including variants light enough to run on consumer cards, and both text-to-video and image-to-video are supported with solid, dependable results. Strong documentation and diffusers integration lower the setup barrier. It does not top the quality charts against the largest models, and the finest detail and longest clips are better served elsewhere, but the accessibility and reliability are the draw.

Stability AI's image-to-video model, one of the earliest widely-adopted open releases.

Stable Video Diffusion helped open the category and remains widely integrated across open tooling. It animates a still image into a short clip and benefits from the enormous Stable Diffusion ecosystem, so nodes, guides, and community support are everywhere. It is showing its age against 2026 models: clips are short, control is limited, and motion is simpler than newer releases. For image-to-video basics and learning the pipeline, it is still a reasonable, well-supported starting point.

A fully open project that reproduces a Sora-style pipeline, transparent end to end.

Open-Sora is aimed at the research and tinkering crowd. It open-sources not just weights but the training pipeline and data recipe, so the whole stack is transparent and reproducible, which is valuable for teams that want to understand or extend the model rather than just call it. That openness is the point. The trade is that out-of-the-box quality trails the polished flagship open models, and it expects more comfort with training code and infrastructure than a ready-to-use release.