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- The open source video generation landscape in 2026 is dominated by DiT-based architectures, with LTX-2.5, Wan Video, and CogVideoX as the leading production-capable models — each with distinct strengths in audio support, motion quality, and hardware flexibility.
- LTX-2.5 is the only open source model with native audio-video generation in a single pass, plus the most extensive fine-tuning ecosystem including IC-LoRA adapters, camera control LoRAs, and FP8 quantization for 32GB GPU workflows.
- Choosing between models comes down to three factors: whether your workflow needs audio, what VRAM tier you're working with, and how deeply you need ComfyUI or Python pipeline integration.
The open source video generation landscape has shifted dramatically. What was once dominated by closed APIs and waitlists now includes several production-capable models you can run locally, fine-tune, and deploy without per-generation fees. Choosing the right one depends on your technical setup, workflow requirements, and what you actually need to generate.
This comparison covers the leading open source AI video generation models active in 2026: LTX-2.5, Wan, HunyuanVideo, CogVideoX, and Mochi 1. Each has distinct architectural choices, licensing terms, and practical trade-offs.
What Makes an Open Source Video Model?
Before comparing models, it helps to be precise about what "open source" means in this context. Models exist on a spectrum:
• Open weights only: The model checkpoint is available for download, but the training code, data, and pipeline implementation may be proprietary or unavailable
• Open weights + inference code: Both the model and the code to run it are available
• Fully open source: Weights, inference code, training code, and optionally training data are publicly available
For practical workflows, the distinction matters most around fine-tuning and deployment. A model with open weights and no training code requires reverse-engineering to fine-tune. A model with full training infrastructure can be adapted to domain-specific data directly.
LTX-2.5
Architecture and Capabilities
Architecture and Capabilities
LTX-2.5 is a 22-billion-parameter audio-video foundation model from Lightricks. It generates sound and picture together in a single pass rather than adding audio afterwards, which is what keeps dialogue and effects locked to the footage instead of drifting out of sync.
It also generates multishot sequences. A single run can produce several connected shots that hold the same character, setting, lighting and voice across a cut, where every other model in this comparison gives you one shot at a time.
Its video decoder is built to hold fine detail through fast motion, and the model works out how long a clip should be from what the prompt describes rather than taking duration as a fixed setting.
Beyond straight generation, LTX-2.5 covers image-to-video, audio-to-video, text-to-audio, keyframe interpolation, retake for regenerating one stretch of a clip, and Dub-It for replacing dialogue in existing footage.
IC-LoRA adapters extend it into editing work: pose and depth control, in and out painting, relighting, colour work, and upscaling, all conditioned on a reference clip.
Performance
The distilled checkpoint is built for iteration and runs in a handful of steps where the full model takes twenty or more. Self-hosted on a pair of GB200s at 720p, LTX-2.5 turns out a ten-second clip in under seven seconds, faster than the clip itself plays. FP8 quantization cuts memory use by around 40% with little visible cost.
You will need an NVIDIA GPU with at least 32GB of VRAM. An A100 or H100 is the recommended setup, so this sits at the heavier end of the models here.
Licensing
LTX-2.5 ships with open weights under a permissive licence: no branding requirement, and commercial use free under $10M in revenue. For most teams that means you can fine-tune it, deploy it and ship on it without negotiating a separate agreement first. Above that revenue line, commercial terms come from Lightricks.
Open Source Access
Weights, training code and inference pipelines are all public, along with a raw pretrained checkpoint for teams fine-tuning to a domain well outside cinematic video. ComfyUI support is first-party, with ready-made templates rather than community workarounds.
If you would rather not run it yourself, the hosted API bills per second of output, from $0.09 at 720p to $0.37 at 4K.
Wan 2.1
Architecture and Capabilities
Wan 2.1 is a video generation model released by Alibaba under an Apache 2.0 license. The architecture uses a causal 3D VAE for temporal compression and supports text-to-video and image-to-video generation. Wan 2.1 comes in two parameter scales: 1.3B and 14B. The 14B model produces higher quality output; the 1.3B model is designed for faster inference on lower-end hardware.
Wan 2.1 supports resolutions up to 1280×720 and clip lengths up to 81 frames. It does not include integrated audio generation — audio is a separate step.
Performance
The 14B model requires significant VRAM (40GB+) for full precision operation. With quantization (INT8 or FP8), it runs on 24GB consumer GPUs. The 1.3B model runs on GPUs with as little as 8GB VRAM. Generation speed varies by configuration but is generally slower than LTX-2.5's distilled pipeline at equivalent quality settings.
Licensing
Apache 2.0 license with no commercial restriction. This is the most permissive licensing among the models in this comparison and makes Wan 2.1 attractive for commercial applications that cannot accommodate dual-license terms.
HunyuanVideo
Architecture and Capabilities
HunyuanVideo is Tencent's open source video generation model, released in late 2024. The architecture is based on a full attention transformer that processes video and text jointly. The model supports text-to-video generation with output up to 1280×720 resolution and 129 frames (approximately 5 seconds at 24 fps).
HunyuanVideo does not support image conditioning or audio generation in its base release. Community extensions (I2V adapters) have been developed outside Tencent's official codebase.
Performance
HunyuanVideo requires significant GPU resources. The base model needs 60GB+ VRAM for full precision. Quantized versions (INT8) run on 24GB consumer GPUs with some quality trade-off. Generation speed at 720p/129 frames is measured in minutes on most consumer hardware, which makes iteration slower than distilled models like LTX-2.5.
Licensing
Tencent HunyuanVideo Community License permits commercial use up to 100 million monthly active users. Beyond that threshold, a separate commercial license is required. This makes it effectively open for most applications but introduces restrictions at scale.
CogVideoX
Architecture and Capabilities
CogVideoX is developed by Zhipu AI and is available in 2B and 5B parameter variants. The model uses an expert adaptive LayerNorm architecture and a 3D causal VAE with spatial (8x8) and temporal (4 frames) compression. It supports text-to-video and image-to-video generation. A separate CogVideoX-Fun variant extends the base model with additional capabilities including resolution flexibility and LoRA support.
CogVideoX does not include integrated audio generation. Output quality is generally competitive for prompt adherence and temporal consistency at its parameter scale.
Performance
The 5B model runs on 24GB consumer GPUs with standard precision. The 2B model runs on 16GB GPUs. Generation time at 720p/49 frames is approximately 2-3 minutes on a 4090 without optimizations. FP8 quantization reduces VRAM usage further.
Licensing
CogVideoX 2B uses the Apache 2.0 license. CogVideoX 5B uses Tsinghua University's model license, which permits commercial use with restrictions on prohibited applications. The licensing terms require review for production deployments.
Mochi 1
Architecture and Capabilities
Mochi 1 is developed by Genmo and released under an Apache 2.0 license. The architecture uses an Asymmetric Diffusion Transformer (AsymmDiT) that processes video tokens asymmetrically with different attention configurations for the conditioning and generation streams. Mochi 1 targets high motion quality and realistic motion dynamics.
Mochi 1 supports text-to-video generation at 480p resolution. It does not support image conditioning, audio generation, or the range of pipeline variants available in LTX-2.5. The model is positioned as a research release focused on motion quality rather than a production pipeline.
Performance
Mochi 1 requires 80GB+ VRAM for full precision operation. With linear quadrant attention (linear_quadrant), it runs on 24GB GPUs. Generation speed is competitive at its resolution target.
Licensing
Apache 2.0 license with no commercial restriction.
Comparison Summary
Choosing the Right Model for Your Use Case
Production Pipelines Requiring Audio
If audio is something you ship rather than something you add later, LTX-2.5 is the only model here that generates it natively. Sound and picture come out of the same pass, which avoids the drift you get when audio is bolted on as a separate step.
It also handles the reverse direction, generating video from an audio track, and can replace dialogue in footage you already have.
Multi-Shot Sequences
If your work involves cuts rather than one continuous take, this is the clearest reason to pick LTX-2.5. It generates connected shots in a single run and keeps the character, setting and lighting consistent across the cut.
Every other model in this comparison produces one shot at a time, leaving continuity to reference images and manual assembly.
Commercial Workflows with Simple Licensing Requirements
If Apache 2.0 is a hard requirement in your legal context, Wan 2.1 and Mochi 1 are still the unconditional options, and Wan 2.1 is the stronger of the two. LTX-2.5 is now a realistic choice for most commercial teams though, with free use under $10M in revenue and no branding attached.
Below that line the practical difference is small.
Consumer GPU Deployment
On 24GB consumer cards, Wan 2.1, HunyuanVideo, CogVideoX and Mochi 1 all run without enterprise hardware. LTX-2.5 needs 32GB, so it sits a tier above even with quantization. For smaller cards, Wan 2.1's 1.3B model is still the most accessible thing here.
This is the honest trade-off with LTX-2.5: more capability, higher floor.
Maximum Pipeline Flexibility
LTX-2.5 covers the widest range of jobs without switching models: generating from text, images or audio, editing and relighting existing footage, replacing dialogue, regenerating a single section of a clip, and upscaling.
If your workflow needs several of those and you would rather not stitch together separate tools for each, that breadth is the argument.
Getting Started
All five models are available through their GitHub repositories and HuggingFace pages. For LTX-2.5, the official repository covers setup, the available pipelines, and what hardware each configuration needs, and ComfyUI users can start from the built-in templates.
For teams without local GPUs, the hosted API gives you the same model without the infrastructure.
