Best Open Source Video Generation Models in 2026: Make the Right Choice

Choosing the best open-source video generation models is harder than it looks. On paper, you have dozens of promising options. In practice, if you choose poorly, you will run into plenty of problems. For instance, laggy renders, blurry visuals, or worse, a model that won’t run on your current hardware.

Three things matter most to businesses, creators, and developers: privacy, cost-effectiveness, and quality. This is why they prefer open-source video generation models.

These models offer more complete control, security, and flexibility than their closed counterparts. With so many options out there, sometimes the best solution is a multi-model approach. That’s when platforms like Okara can help to manage your entire AI pipeline.

Here is a list of the best open-source video generation models for 2026, ranked by output quality, hardware requirements, key features, and cost.

Wan 2.2

Wan 2.2, developed by Alibaba Tongyi Lab, introduces a first-of-its-kind Mixture-of-Experts (MoE) setup. As a result, it splits the work between two “specialized” experts. One expert handles layout and motion, and the other handles lighting, texture, and color. Compared with Wan 2.1, this version has better motion quality and improved integration with VACE. It was trained on 65% more images and 83% more video clips compared to the previous version.

The model is available in two variants: Text-to-Video (T2V) and Image-to-Video (I2V). In addition, a lighter 5B-parameter Hybrid version is available for users with lower-spec hardware.

What Can Be Generated

Wan 2.2 makes 5-second video clips at 480P or 720P resolution from text prompts or still images. The I2V version turns still images into short clips, and you can use a text prompt to guide how it moves and looks.

Output Examples

A prompt like "A futuristic city skyline at sunset with flying cars” creates a lifelike video with natural movements. For an I2V example, it can animate a still image of a coffee cup on a rainy windowsill into a short clip.

Cost

It is open source and free under Apache 2.0, so no licensing fees to worry about. The cost largely depends on the setup or cloud GPU rental.

Ideal For: Indie filmmakers, advertisers, and online creators needing high-quality short videos. Plus, it is suitable for researchers experimenting with high-fidelity video without per-render cost.

Limitations:

The main limitation is that the maximum video length is 5 seconds. Multiple clips must be created and stitched together for long videos. Generating videos is not instant or real-time.

HunyuanVideo

HuyuanVideo is Tencent’s flagship open-source AI generation model with 13 billion parameters. It's a dual-stream-to-single-stream transformer design that processes texts and videos separately before fusing them together. Unlike the old CLIP-based encoders, it uses a decoder-only multimodel LLM. In simple terms, it is much better at understanding prompts. The 3VAD compresses videos, making the motion look natural in longer clips.

The complete toolset includes ComfyUI integration, the Diffusers library, a Gradio demo, xDiT for multi-GPU inference, and FP8-quantized weights.

What Can Be Generated

It produces up to 10+ seconds of cinematic-quality text-to-video and image-to-video clips with strong motion stability. HunyuanVideo is especially good at intricate scenes with several elements.

Output Example

Add a prompt for a cinematic view of Tokyo at nighttime, and it produces a 720p video of a busy street showing crowds and neon signs. A still image of pizza baking in an oven can be turned into a clip. In the video, the cheese melts and bubbles, the crust browns perfectly, and steam rises.

Costing Elements

All weights and codes are free and open-source. However, it is resource-intensive and runs on high-end GPUs (A100 or H800). 13B parameters means high VRAM requirement, 40GB+ for full precision.

Ideal For

AI researchers, studios, and businesses are creating complex videos using powerful hardware.

Limitations

Extremely high hardware needs make it inaccessible for solo creators and part-timers. Producing a 5-second clip may take longer than 15 minutes, depending on the settings.

Mochi 1

Mochi 1 from Genmo, containing 10 billion parameters, ranks among the best video generation models. Built on the AsymmDiT architecture, it reliably produces clips based on detailed or quirky descriptions. It uses a custom VAE to reduce video size by 128x without sacrificing quality.

What Can be Generated

Mochi 1 is primarily a Text-to-Video (T2V) generator and can create 5.4-second 480p videos. It is exceptionally good at producing photorealistic clips with smooth motion at 30fps. Native I2V support is not part of the preview release.

Output Examples

Mochi 1 delivers realistic clips from tricky prompts like “A panda playing a guitar on stage.” Similarly, “A glass hitting the floor and breaking in slow motion" shows how it handles realistic physics.

Costing Elements

Mochi 1 is fully free and open-source (Apache 2.0) but you will need powerful hardware, ideally an A100 or H100 with 40-80GB VRAM.

Ideal For

This model is ideal for designers, artists, marketing professionals, digital creators, and AI enthusiasts.

Limitations

Right now, the version supports only 420p output. Since it is built for photorealistic styles, it does not handle animated or non-photorealistic content well. Notably, you may see minor distortions in scenes with fast motion.

LTX Video

LTX Video from Lightricks is the fastest open-source video generation model on this list, by a wide margin. This DiT model can render 30fps video at 1216×704 resolution faster than real-time on an H100. Additionally, you can see a lower-resolution preview in about 3 seconds on a consumer-grade GPU like the RTX 4090.

LTX Video’s 13B “Dev” and distilled models offer higher quality. The 2B versions are lighter and easier to run on not-so-powerful hardware. FP8 models reduce memory use and work on systems with low VRAM.

What can be generated

LTX Video supports multiple modes: text-to-video, image-to-video, and video-to-video. By default, it produces a 5-20 second video clip and performs best at 720×1280.

Output Examples

A prompt of “a person walking on a beach at sunset” creates a quick video of the person strolling, with footprints in the sand and waves hitting the shore, along with other details. It creates a video of the puppy running on the grass from a still image of the puppy.

Costing Elements

This open-source video generation model is very cost-effective and can run on a single consumer GPU (such as an RTX 3080/3090 or 4070/4080).

Ideal For

LTX Video is suited for fast prototyping, live demos, and social media content. Developers and professionals can try out new ideas without long render times.

Limitations

Undoubtedly, the model is fast, but the cinematic quality is not on par with HunyuanVideo or Wan 2.2. Close-ups often show flaws, and fine-tuning options are limited.

CogVideoX 5B

CogVideoX 5B from Zhipu AI is a mid-sized model in the CogVideoX lineup. It is more capable than the smaller 2B version and less advanced than the newer 1.5 release. The model uses a 3D Causal VAE with an expert Transformer to preserve spatial and temporal details.

It can produce 6-second videos at 720×480 resolution at 8 fps and supports English prompts up to 226 tokens. It supports LoRA fine-tuning and can run on modest GPUs with TorchAO quantization. Free Colab T4 notebooks are also available for those without powerful hardware.

What Can Be Generated

The model delivers 6-second T2V and 12V videos at 720×480 from written prompts. It also accepts images at the same resolution. Furthermore, it preserves the subject's color, lighting, and details.

Output Examples

A prompt like "A fantasy battle with knights and dragons" produces a rich, large-scale scene. A video of a hummingbird hovering by a red flower shows how it handles tiny, fast-moving subjects.

Costing Elements

Free weights. CogVideoX 5B runs efficiently on 12 to 16GB VRAM and can be tested for free on Google Colab T4 instances.

Ideal For

It is beginner-friendly and suitable for professionals exploring AI video generation or working on small demo projects.

Limitations

8fps output feels noticeably less fluid than the 24 or 30fps you get from newer models. Resolution tops out at 720x480p. It is not fit for projects involving complicated, multi-stage actions.

SkyReels V1

SkyReels V1 from Skywork AI is the first open-source video model built for human-focused content. It was fine-tuned on HunyuanVideo using over 10 million high-quality film and TV clips. As a result, it does an excellent job at capturing natural facial expressions and body language. It supports 33 facial expressions and over 400 natural movements.

Furthermore, its SkyReels-Infer framework reduces inference latency by up to 58%. Besides all, SkyReels supports multi-GPU setups, parameter offloading, and FP8 quantization.

What Can Be Generated

It makes videos up to 4 seconds long at 24 fps, with a resolution of 544×960. The model produces T2V and I2V outputs with real-looking human characters. Facial reactions have nuance, and body movements look more natural. It is perfect for dialogue-heavy scenes or short-form drama work.

Output Examples

A prompt like "A close-up of two women smiling at each other" would be its forte. Another ideal use case is a “detective examining a clue with a slight smirk.”

Costing Elements

A high-end GPU is recommended for the best performance and comfortable use. Its weights and inference code are freely available on Hugging Face and GitHub.

Ideal For

SkyReels V1 makes better short films, ads, social media videos, and educational content.

Limitations

The maximum video length is approximately 4 seconds. Resolution caps at 544x960; a 720p version will arrive later. Vague descriptions often deliver average output.

CogVideoX-1.5

CogVideoX-1.5 is a more capable version than the 5B and earlier CogVideoX models. It understands prompts more accurately and produces longer clips with smooth motion. It currently supports English prompts with a 224-token limit.

Technically, it uses an expert transformer architecture to handle fast motion effectively. The model continues to support LoRA fine-tuning and works with Diffuser. Also, it supports DDIM Inverse for video editing and interpolation tasks.

What Can Be Generated

CogVideoX-1.5 contains 5 billion parameters and comes in two versions. A text-to-video model creates AI video clips from written prompts at 1360×768 resolution. In contrast, an image-to-video version turns still images into animated clips.

Output Examples

A prompt for “A hyperrealistic macro shot of a water droplet falling from a leaf” would create a video with fine details and perfect physics. Check its improved scene composition with a prompt for ‘A bustling street market in Marrakech.”

Costing Elements

CogVideoX-1.5 is open source and is freely available for both research and commercial use. Similar to 5B, it runs smoothly on 12–16 GB of VRAM and can be quantized to work on systems with lower memory.

Ideal For

Existing CogVideoX users are looking for an upgrade. Creators who want better quality and longer clips without heavy hardware requirements.

Limitations

Generating a 5-second video takes 9–17 minutes on high-end hardware, so it is not real-time. High-end hardware like the A100 or H100 is required for high performance.

Allegro (Rhymes AI)

Allegro (Rhymes AI) is a versatile and accessible text-to-video generator designed for business purposes. At its core, it combines a 2.8B-parameter VideoDiT with a 175M-parameter VideoVAE. As a result, it produces surprisingly high-quality despite its small size.

Plus, it uses 3D RoPE positional embeddings and 3D full attention to capture spatial and temporal details across frames. Allegro supports a T5 text encoder to better understand prompts. CPU offloading keeps GPU memory use around 9.3 GB in BF16 mode. This is among the lowest memory requirements for similar models.

What Can Be Generated

Allegro generates 6-second clips at 720×1280 resolution and 15 fps. Users can interpolate the videos to 30 fps using EMA-VFI. This version accepts both text and image inputs. It can use the first frame and, optionally, the last frame to create follow-up videos.

Output Example

A prompt for “An animation of a hot air balloon floating over a beautiful landscape” would help you test its strength. It creates a video from a still image of a car on the road. The card moves forward naturally as the background changes.

Costing Elements

Allegro provides free model weights under the Apache-2.0 license and typically requires 12–24 GB of VRAM.

Ideal For

Developers and creators who are looking for a commercially usable model. It is simple to deploy and works well with advertising prototypes, educational videos, and general creative projects.

Limitations

On the flip side, the model can not render celebrities or specific real-world locations. Clips are limited to 6 seconds and take longer to generate. It takes around 20 minutes on high-end hardware like the H100, and over an hour on consumer GPUs such as the RTX 3090.

How to Choose the Best Open Source Video Generation AI Model?

Without a doubt, choosing the right AI video generator can be daunting. Ultimately, your choice should come down to a few critical factors:

• T2V vs 12V: First and foremost, consider your input type. If you want to create videos purely from text, you can work with most models listed above. In contrast, if you are animating a still image, look for models such as HunyuanVideo, SkyReels V1, LTX Video, and CogVideoX-1.5 that offer better 12V capabilities.
• Video Length: Next, video length has always been the main concern for users. Typically, most open source AI video generation models produce up to 10 or 12 seconds of video clips. Models like LTX Video and HunyuanVideo are better suited for long scenes. For longer projects, you will likely need to stitch multiple clips together in post-production. Notably, Allegro handles follow-up videos well.
• Resolution and FPS - Resolution and frame rate also matter. LTX Video leads on fps (native 30 fps). Wan 2.2 and HunyuanVideo offer better 720p resolution. On the contrary, CogVideoX 5B runs at lower quality and fps, such as 720x480p at 8fps. If you want smoother motion, choose models with high frame rates. Similarly, decide whether 1080p or 720p is sufficient for your platform.
• Setup Path: ComfyUI is popular for local, node-based workflows. Diffusers work well when integrating into a Python pipeline. For cloud deployment, RunPod and Hyperstack allow users to run inference without managing hardware themselves.
• Licensing: Finally, always read the licensing details before using a model in a commercial project. Most are permissive, but it does not hurt to verify. Apache-2.0–licensed models such as Wan 2.2, CogVideoX-1.5, Mochi 1, Allegro, and LTX-Video generally allow commercial use. Always read license details before using these models in products or client projects.

How Do These Text-to-Video LLMs Compare to Closed AI Models?

The gap between open-source and closed models has narrowed dramatically. A year ago, closed AI models Sora and Veo clearly outperformed anything available in open source. Today, open AI models (Wan 2.2, HunyuanViden) are directly comparable in visuals and motion fluidity.

The cinematic-quality outputs from these aforementioned models hold up surprisingly well in side-to-side tests with Kling and Hailou.

That said, closed models often still hold an edge in three areas: scale, speed, and refinement. Compared to open-source AIs, they are trained on much larger, more diverse, and often higher-quality datasets. Consequently, they create elaborate, multi-scene videos with accurate physics.

On the other hand, open-source models win on three important fronts that matter more to many users:

• Privacy and Control: Users have control over their privacy, and their outputs, prompts, and creative ideas are not stored on a third-party server.
• Cost: While you pay for hardware, it is still cheaper than paying per-generation API fees. A user can produce thousands of videos on their own hardware or on rented cloud inference.
• Customization and Longevity: You can fine-tune an open-source model to your unique style. More importantly, you are not at the mercy of a company that might change its pricing, policies, or shut down its service tomorrow.

Open source models are no longer “budget alternatives” but complete production tools. Still, full creative workflows require juggling multiple tools. Thankfully, platforms like Okara simplify this by bringing various AI tools into one workspace.

FAQs

What is the best open-source text-to-video model for beginners?
CogVideoX 5B is beginner-friendly and offers good quality. Plus, it has a huge community, ample tutorials, and ComfyUI support.

What hardware do I need to run open source video generation models?
Allegro and LTX-Video run on as little as 12GB VRAM. Cards like the RTX 3060 12GB or RTX 4070 work for lighter models. Heavy and high-quality models like HunyuanVideo and Mochi 1 need A100 or H100.

Is Comfyui or Diffusers better for text-to-video generation?
ComfyUI is better for experimenting, connecting models, and changing settings without coding. Alternatively, Diffusers, a Python library, is fit for developers looking to integrate models into their own apps or scripts.

How long can open source models generate videos?
Mostly, standard open source AI video generation models produce 4-10 seconds of video clips.

Do open-source video generation models have any restrictions?
Yes, always review the license. Some models are designed for non-commercial use or research purposes only. Others use permissive licenses (like Apache 2.0 or MIT) that allow for commercial use.