List of the Top AI Video Models for ComfyUI in 2026

LTX builds open world models, AI systems that generate, simulate, and shape video, audio, and the physical world. Lightricks created LTX so that developers, studios, and enterprises can own the model they build on, not just rent access to someone else's. The current release, LTX-2.5, is a 22B-parameter dual-stream diffusion transformer. It renders native 4K footage at up to 50fps and produces synchronized audio and video in one pass, no separate tools required. Independent benchmarks from Artificial Analysis place LTX in the top three AI video models worldwide. There is no single way to work with LTX. Pull the open weights and run the model yourself on your own machines. Take a commercial license for on-premise deployment with full enterprise support. Or use LTX Studio, the packaged production suite for creative teams that want the model without managing the infrastructure. ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA all build on it today. If you need a quick clip for social media, look elsewhere. LTX exists for AI teams turning video, audio, and simulation into part of their own product, not a novelty.

HappyHorse-1.1-T2V is a text-to-video model on QwenCloud built to generate high-quality videos from natural language prompts. The model supports video generation workflows where the input is text and the output is video. HappyHorse-1.1-T2V is designed with improved semantic understanding so it can more accurately interpret creative instructions. It also supports cinematic shot control, helping users guide the style, composition, and feel of generated scenes. Dynamic motion rendering helps the model produce smoother movement and more natural video sequences. The model is positioned to create richer details, stronger visual consistency, natural character actions, convincing scene atmosphere, and realistic physical dynamics. Developers can access HappyHorse-1.1-T2V through the QwenCloud API using the DashScope video synthesis endpoint. API requests can specify parameters such as resolution, aspect ratio, duration, and the text prompt. The model supports 480P, 720P, and 1080P video generation with per-second pricing, along with rate limits for requests, concurrency, and async queue tasks. HappyHorse-1.1-T2V is a hosted model rather than an open source model, and it can be tested through QwenCloud’s Try AI experience or integrated with an API key. By combining prompt-based video creation, cinematic control, motion quality, visual consistency, scalable API access, and configurable output settings, HappyHorse-1.1-T2V helps creators and developers turn ideas into generated video.

Wan2.2 represents a major upgrade to the Wan collection of open video foundation models by implementing a Mixture-of-Experts (MoE) architecture that differentiates the diffusion denoising process into distinct pathways for high and low noise, which significantly boosts model capacity while keeping inference costs low. This improvement utilizes meticulously labeled aesthetic data that includes factors like lighting, composition, contrast, and color tone, enabling the production of cinematic-style videos with high precision and control. With a training dataset that includes over 65% more images and 83% more videos than its predecessor, Wan2.2 excels in areas such as motion representation, semantic comprehension, and aesthetic versatility. In addition, the release introduces a compact TI2V-5B model that features an advanced VAE and achieves a remarkable compression ratio of 16×16×4, allowing for both text-to-video and image-to-video synthesis at 720p/24 fps on consumer-grade GPUs like the RTX 4090. Prebuilt checkpoints for the T2V-A14B, I2V-A14B, and TI2V-5B models are also provided, making it easy to integrate these advancements into a variety of projects and workflows. This development not only improves video generation capabilities but also establishes a new standard for the performance and quality of open video models within the industry, showcasing the potential for future innovations in video technology.

Happy Horse is an AI video generation and editing platform designed to help creators transform prompts, images, references, and first-frame ideas into cinematic video content. The platform gives users multiple ways to begin a project, including text-based generation, reference-driven generation, first-frame input, and video editing. Creators can generate videos from imaginative concepts, then modify details to refine the final result. Happy Horse is built for visual experimentation, storytelling, and AI cinema, making it useful for artists who want to explore ideas quickly without traditional production barriers. Its creative environment includes featured projects, community videos, short AI films, and showcase content from different creators. The platform also highlights AI cinema events, encouraging users to submit and celebrate AI-made cinematic work. Users can sign in to receive free credits and take advantage of special offers for additional generation access. Happy Horse supports short-form video experimentation, concept development, visual storytelling, and creative exploration. The platform’s tools help users turn sparks of imagination into videos that can be shared, refined, or developed into larger creative projects. Its combination of generation, reference input, first-frame control, editing, and community inspiration makes it a practical workspace for AI video creators. Happy Horse helps filmmakers, designers, artists, and everyday creators bring visual ideas to life with speed, flexibility, and expressive control.