The 8 best open source AI video models in 2026

Compare the 8 best open source AI video models in 2026 for teams that want weights they can download, self-host, and fine-tune. The right pick depends on output quality, license terms, and the GPU you have to run it on. Morphic is not open source, but it is the fastest way to run open models like Wan and LTX, plus flagship closed ones, with no GPU setup.

Open source AI video model em resumo

Each model below leads on something specific: raw quality, motion, speed on modest hardware, license permissiveness, or ecosystem support. The table is sorted by what each one delivers best, so you can match the model to your setup and license needs instead of picking by ranking alone.

FerramentaIdeal paraRecurso de destaque
1.Morphic
Running open and closed video models without hardwareOpen and flagship closed models on one Canvas
2.Wan (Alibaba)
Best overall open weights qualityStrong quality with a permissive license
3.LTX Video (Lightricks)
Fast generation on consumer GPUsNear real-time speed on modest hardware
4.HunyuanVideo (Tencent)
Cinematic quality from open weightsLarge model with strong prompt adherence
5.Mochi 1 (Genmo)
Fluid motion under a permissive licenseHigh-fidelity motion, Apache licensed
6.CogVideoX (Zhipu / THUDM)
An accessible starting point for self-hostingMultiple sizes for modest hardware
7.Stable Video Diffusion (Stability AI)
Basic image-to-video with broad supportDeep integration across open tooling
8.Open-Sora (HPC-AI Tech)
Full transparency and research useOpen weights, training pipeline, and recipe

As 8 melhores opções de open source AI video model para cada caso de uso

Morphic

Run open models like Wan and LTX, plus flagship closed ones like Veo and Kling, in one visual Canvas with no GPU setup.

  • Open source video models are powerful, but running them means a capable GPU, CUDA drivers, a ComfyUI or diffusers setup, and time. Morphic hosts open models like Wan and LTX so you can generate from a prompt without any of that.
  • The same workspace runs the flagship closed models too, Veo, Kling, Seedance, Hailuo, and Vidu, so you can compare an open model against a closed one on the exact same shot before you commit.
  • Clips land on a free-flowing visual Canvas and stay live. Line the selects up on Compose, the built-in timeline, add generated voiceover and music, and export the finished cut without leaving the workspace.
  • To be clear: Morphic is a hosted platform, not an open source project. If your requirement is downloading weights to self-host or fine-tune, run the open models directly. If your requirement is generating with them fast, this removes the setup.
Try nowIdeal para: Running open and closed video models without hardware

Experimente mais na Morphic

#2

Wan (Alibaba)

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.

Ideal para: Best overall open weights quality
Prós
  • Top-tier open quality on text and image-to-video
  • Large community with LoRAs and tooling
Contras
  • Heavier variants need a serious GPU and VRAM
  • Best results take sampler and setting tuning
#3

LTX Video (Lightricks)

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.

Ideal para: Fast generation on consumer GPUs
Prós
  • Fast enough for rapid iteration
  • Runs on more modest consumer GPUs
Contras
  • Raw fidelity trails the heaviest models
  • Hardest motion suits larger models
#4

HunyuanVideo (Tencent)

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.

Ideal para: Cinematic quality from open weights
Prós
  • Cinematic detail that rivals closed models
  • Open weights with a growing adapter ecosystem
Contras
  • Demands substantial VRAM and compute
  • Slower generation and more involved setup
#5

Mochi 1 (Genmo)

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.

Ideal para: Fluid motion under a permissive license
Prós
  • Smooth, believable motion for an open model
  • Permissive license friendly to commercial use
Contras
  • Image-to-video and control features lighter
  • Full model needs capable hardware
#6

CogVideoX (Zhipu / THUDM)

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.

Ideal para: An accessible starting point for self-hosting
Prós
  • Runs on relatively modest GPUs
  • Good docs and diffusers integration
Contras
  • Does not top quality against the largest models
  • Finest detail and longest clips suit bigger models
#7

Stable Video Diffusion (Stability AI)

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.

Ideal para: Basic image-to-video with broad support
Prós
  • Widely supported across the open ecosystem
  • Simple image-to-video that is easy to run
Contras
  • Short clips and limited control
  • Motion simpler than newer 2026 models
#8

Open-Sora (HPC-AI Tech)

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.

Ideal para: Full transparency and research use
Prós
  • Entire stack is transparent and reproducible
  • Ideal for extending and researching the model
Contras
  • Out-of-the-box quality trails flagship open models
  • Expects comfort with training infrastructure

O que é um modelo de vídeo com IA de código aberto?

Um modelo de vídeo com IA de código aberto é um modelo de geração de vídeo. Os pesos ficam publicados para qualquer pessoa baixar, rodar e, em muitos casos, ajustar por conta própria. Em vez de chamar a API hospedada de uma empresa, você recebe o modelo em si. Você roda no seu próprio hardware ou numa GPU de nuvem alugada. A categoria vai de modelos grandes e cinematográficos, como o HunyuanVideo, até modelos leves, como o LTX Video. Esses modelos leves são feitos para rodar numa placa de consumidor.

O atrativo é o controle. Pesos abertos significam nenhuma cobrança por geração e a possibilidade de ajustar o modelo com o seu próprio material. Também dão privacidade total dos dados e liberdade para encaixar o modelo no seu próprio pipeline. O preço é que a infraestrutura e a configuração ficam por sua conta. É um compromisso real de engenharia, bem diferente de um simples cadastro.

Modelos de vídeo com IA de código aberto x modelos fechados

Modelos fechados como Veo, Kling e Sora chegam como um serviço hospedado. Você envia um prompt, o modelo roda no hardware deles, e você recebe o clipe pronto. Eles costumam liderar em qualidade máxima e não pedem nenhuma configuração. Mas cobram por uso, impõem limites e não dão acesso aos pesos por trás do modelo. O seu material passa pelos servidores deles, e você constrói apenas sobre o que eles expõem.

Os modelos de código aberto invertem essa lógica. Os pesos ficam com você, então dá para rodar offline, ajustar o modelo, manter os dados privados e pagar só pelo processamento. Em troca, você cuida das GPUs, dos drivers e do pipeline de geração. Nenhum dos dois é melhor em absoluto. Times que precisam de personalização, privacidade ou controle de custo em escala preferem o caminho aberto. Times que querem o melhor resultado sem nenhuma configuração preferem o fechado, e muitos times usam os dois ao mesmo tempo.

Como funcionam os modelos de vídeo com IA de código aberto

Esses modelos são transformers de difusão. O modelo parte do ruído e refina passo a passo, guiado pelo seu prompt. O resultado é uma sequência de quadros consistente no tempo. Para rodar um deles, você carrega os pesos num framework como o diffusers ou um grafo do ComfyUI. Depois, garante VRAM suficiente e ajusta samplers, steps e resolução, conforme a qualidade e a velocidade que você quer. O ajuste fino soma uma LoRA ou um treinamento completo com os seus próprios dados.

O Morphic chega aos mesmos modelos por outro caminho. Em vez de hospedar você mesmo, você escolhe um modelo aberto como Wan ou LTX direto no navegador. Você cria como um profissional, sem precisar de equipe nem equipamento. Gera a partir de um prompt ou de uma imagem, sem GPU, driver ou instalação. O clipe cai direto no Canvas, ao lado de modelos fechados como Veo e Kling. Assim, você compara os dois no mesmo plano. Depois, corta a sequência no Compose, a linha do tempo integrada, e exporta sem sair do workspace, mesmo quando o fluxo começa no celular.

O que os criadores dizem sobre a Morphic

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Perguntas frequentes

What is the best open source AI video model in 2026?
Wan is the common answer for overall open weights quality, with HunyuanVideo close behind on cinematic detail and Mochi 1 leading on motion. The best choice depends on your GPU and license needs. If you would rather skip the hardware, Morphic runs Wan and LTX hosted, next to closed models like Veo and Kling.
What hardware do I need to run open source video models?
It varies widely. Lighter models like LTX Video and smaller CogVideoX variants run on consumer GPUs, while HunyuanVideo and heavier Wan variants want a data-center card with lots of VRAM. If you do not want to manage hardware, Morphic runs the open models in the cloud with no setup.
Are open source video models free to use commercially?
Often, but check the license per model. Mochi 1 and Wan ship under permissive licenses friendly to commercial use, while some releases carry restrictions. Always read the specific license before shipping commercial work, since terms differ across projects and can change between versions.
Is Morphic an open source video model?
No. Morphic is a hosted platform, not an open source model, and you cannot download its weights or self-host it. What it does is run open models like Wan and LTX, alongside flagship closed models, so you can generate with them fast without any GPU or setup. For self-hosting or fine-tuning, run the open models directly.
Can I fine-tune an open source video model?
Yes, that is a key reason to use one. Open weights let you train LoRAs or fine-tune on your own footage, and models like Wan have large communities sharing adapters. This needs your own hardware and ML setup; a hosted platform like Morphic trades that flexibility for zero-setup generation.
Open source vs closed video models: which is better?
Closed models like Veo and Kling still lead on peak quality and ease, while open models win on control, cost at scale, privacy, and customization. Many teams use both. On Morphic you can compare an open model against a closed one on the same prompt before deciding which fits the shot.