5 Open-Weight AI Video Models Worth Testing

Open AI video models let developers download weights, run inference on their own infrastructure, and customize a workflow. “Open” does not always mean the same thing, however: some releases use Apache 2.0, while others use a model-specific community license with commercial limits. Check the exact code and weight licenses before building a product.

Five models at a glance

Model	Best fit	Important constraint
Wan 2.2 A14B	High-quality text-to-video or image-to-video research	Large MoE checkpoints need substantial GPU resources
HunyuanVideo 1.5	A newer, lighter general video foundation model	Tencent model terms require review
LTX-2.3	Synchronized audio and video plus iterative pipelines	Weight license has an organization-revenue threshold
Mochi 1	Permissively licensed research and fine-tuning	Official implementation is memory-intensive
CogVideoX1.5-5B	Established text-to-video and image-to-video ecosystem	Older generation with model-specific limits

1. Wan 2.2 A14B

Wan 2.2's 14-billion-parameter text-to-video and image-to-video variants use a Mixture-of-Experts design that activates different expert networks during denoising. The official T2V model card supports five-second output at 480p or 720p. It is a strong option for teams prioritizing visual quality and a well-documented research release.

The official Wan 2.2 repository publishes the models under Apache 2.0 and says it claims no rights over generated content, subject to the license and lawful use. Hardware needs depend on the checkpoint, precision, resolution, and offloading setup; do not assume the full A14B model will run comfortably on an ordinary laptop GPU.

2. HunyuanVideo 1.5

Tencent's HunyuanVideo 1.5 is an 8.3-billion-parameter follow-up intended to reduce the barrier presented by the original 13B model. It supports a broader local-development audience than the first release and provides official implementation tools for text-to-video and related workflows.

Use the HunyuanVideo 1.5 repository, not an old setup guide for the original model. Review the Hunyuan community license, supported CUDA environment, checkpoint requirements, and current VRAM guidance before downloading large files.

3. LTX-2.3

LTX-2.3 is a multimodal model that generates synchronized video and audio. The official Python stack includes inference pipelines, LoRA resources, and training components, making it useful for teams that need more than silent text-to-video output.

Its weights are available for local use, but “open source” needs qualification. The LTX model license permits free use for organizations below a stated $10 million annual recurring revenue threshold; larger organizations need a commercial license. Confirm the latest terms on the official LTX-2.3 model card.

4. Mochi 1

Genmo's Mochi 1 remains notable because the project and model were released under Apache 2.0 and designed as a serious research foundation rather than an API-only demo. Its 10B asymmetric diffusion architecture was built for prompt adherence and coherent motion.

The tradeoff is hardware. The official Mochi repository lists about 60 GB of VRAM for its single-GPU reference implementation and recommends an H100-class GPU. Community quantization and ComfyUI workflows may lower memory use, but they can change speed, quality, and support expectations.

5. CogVideoX1.5-5B

CogVideoX1.5-5B is the upgraded member of the CogVideoX family. The official project supports text-to-video and image-to-video variants, with ten-second output and higher resolution than the original CogVideoX-5B. It remains useful for developers already working with the SAT, Diffusers, or ComfyUI ecosystems.

Use the official CogVideo repository to select the current checkpoint. The older 5B model was limited to six-second, 720 × 480 output at 8 fps, so setup articles that quote those numbers should not be applied to the 1.5 checkpoint automatically.

How to choose

Choose Wan 2.2 when quality and Apache-licensed weights matter more than lightweight local inference.
Choose HunyuanVideo 1.5 when you want a newer general foundation model designed for more accessible hardware.
Choose LTX-2.3 when synchronized audio-video generation and an active pipeline ecosystem are central.
Choose Mochi 1 when a permissive Apache 2.0 research base is more important than low VRAM use.
Choose CogVideoX1.5-5B when you value a mature 5B family and established integrations.

What to check before downloading

License: inspect the code license and each checkpoint's weight license separately.
Hardware: calculate GPU VRAM, system RAM, storage, CUDA support, and expected generation time.
Task: confirm text-to-video, image-to-video, editing, audio, duration, and resolution support.
Safety: define rules for real people, sexual content, violence, impersonation, and misleading media.
Provenance: retain generation records and label synthetic media when the context requires it.
Evaluation: test motion, prompt adherence, temporal consistency, text rendering, and failure cases on your own prompts.

Running a model locally can improve control, but it does not automatically guarantee privacy. Prompts may still reach cloud text encoders, APIs, package telemetry, or shared storage depending on the workflow. Audit the entire pipeline.

If local deployment is unnecessary, compare managed production options such as creating videos with HeyGen or adding AI music in Google Vids. Managed tools reduce setup work, while open weights provide more control and engineering responsibility.