Alternatives to HunyuanCustom

Most AI video tools hand you a black box: closed weights, a subscription, and no way to see what is happening under the hood. LTX takes the opposite approach. Built by Lightricks, LTX is an open foundation model that generates and simulates across video, audio, and the physical world, and it puts the weights, the code, and the control in your hands. At the center of the model is LTX-2.5, a 22B-parameter dual-stream diffusion transformer that produces native 4K video at up to 50 frames per second, with audio and video generated together in a single pass rather than stitched together afterward. Artificial Analysis, an independent benchmarking group, currently ranks LTX among the top three AI video models in the world. You choose how you want to use it. Download the open weights and run LTX-2.5 on your own hardware. License the model for on-premise deployment backed by enterprise support. Or build directly on LTX Studio, the production suite that turns the model into a full creative workflow. Companies like ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA already rely on LTX for their own work. LTX is not built for one-off social clips. It is infrastructure for teams that generate motion, audio, and physical environments as part of their own products and pipelines.

MiniMax H3 is a versatile omni-modal generation model that comprehensively grasps multimodal contexts across text, images, video, and audio. It produces videos featuring high-quality stereo sound at resolutions of up to 2K and durations of 15 seconds, catering to various industries such as advertising, branding, e-commerce, product design, UI/UX, gaming, and creative processes. Users have the capability to merge different reference types within a single command, such as replicating camera movements from a video, integrating characters from images into new scenes, and synchronizing vocals from audio clips, all while articulating the relationships using natural language. H3 also facilitates text-to-image and text-to-video conversions, incorporating audio that is generated simultaneously, alongside multi-shot modeling and text-to-audio functionalities, enabling versatile reference and editing across media types. Additionally, voice, sound effects, and music are synthesized cohesively within the model. With a strong emphasis on following instructions accurately, delivering precise text and brand representation, and executing video-to-video motion transfer, it stands out as a powerful tool for creative endeavors. This innovative approach allows for a more seamless integration of multimedia elements, making it easier for users to bring their creative visions to life.

FLUX 3 is an advanced multimodal foundation model that integrates learning from images, video, and audio all within a cohesive framework, effectively modeling how objects connect, how movements occur, and how events produce sound. Utilizing the Self-Flow methodology, it harmonizes the generation and comprehension of multiple modalities in a singular architecture, ensuring that each modality influences the others—sound corresponds to impact, motion adheres to physical laws, and future occurrences are informed by past events. This model is capable of blending modalities, allowing for the simultaneous generation of images, video, and authentic audio based on text prompts or references such as visual and auditory inputs. Its video functionalities are extensive, featuring text-to-video capabilities, image-driven video animation, video transformation, generative continuation of video and audio, controlled transitions using keyframes, multilingual dialogue support, animated text design, and the ability to deliver various styles and aspect ratios, alongside the capacity for agentic chaining into intricate, longer multi-shot sequences. Additionally, FLUX 3 represents a significant leap forward in the field of multimodal AI, offering unprecedented flexibility and creativity in generating rich, interactive content.

Muse Video is Meta’s upcoming AI video generation model developed by Meta Superintelligence Labs as part of the company’s new media generation lineup. Previewed with Muse Image, the model is built on the same pretraining base and is designed to produce visually detailed videos with native audio support. Muse Video is focused on generating clips that follow prompts closely, maintain strong visual fidelity, and preserve temporal consistency across motion and scene changes. It can create realistic short videos with clear beginnings, actions, and payoffs, such as animals moving through a scene, handheld first-person footage, product commercials, and UGC-style social ads. The model supports audio-rich outputs that may include environmental sound, foley, music, voiceover, and synchronized spoken dialogue. Meta highlights Muse Video’s ability to handle cinematic prompts, vertical ad formats, realistic camera movement, product demonstrations, and emotionally engaging creative concepts. The company is still investing in improvements for difficult areas such as audio-video sync and physically accurate fast motion. Muse Video is expected to become available to creators and in Meta AI, expanding Meta’s generative AI tools from image creation into video. As part of Meta’s broader creative ecosystem, Muse Video is built to help users, creators, and businesses turn prompts into dynamic, shareable video content.

Seedance 2.5 is a video creation model from ByteDance Seed designed to move AI video generation from short clips toward complete creative works. Built on the multimodal audio-video joint-generation architecture introduced with Seedance 2.0, the model focuses on foundational generation, reference-based generation, long-form storytelling, and editing control. Seedance 2.5 can generate up to 30 seconds of high-quality audio-video content in one pass and supports multiple rounds of extension. This allows users to create multi-minute videos while maintaining consistency across characters, environments, shot transitions, pacing, motion, and sound. The model supports up to 30 image references, 10 video references, and 10 audio references in a single generation request. It can preserve visual composition, characters, props, voices, scene style, motion paths, and creative intent across complex multi-subject videos. Editing capabilities include timestamp-level changes, green screen workflows, camera perspective editing, reference-based editing, and targeted modifications to characters, actions, or plot elements. Seedance 2.5 is rolling out through Jimeng AI and Doubao Pro, with API access planned through BytePlus ModelArk. By combining long-form generation, multimodal references, cinematic quality, synchronized audio-video output, and professional editing control, Seedance 2.5 helps creators produce more coherent and polished AI videos.

HunyuanVideo-Avatar allows for the transformation of any avatar images into high-dynamic, emotion-responsive videos by utilizing straightforward audio inputs. This innovative model is based on a multimodal diffusion transformer (MM-DiT) architecture, enabling the creation of lively, emotion-controllable dialogue videos featuring multiple characters. It can process various styles of avatars, including photorealistic, cartoonish, 3D-rendered, and anthropomorphic designs, accommodating different sizes from close-up portraits to full-body representations. Additionally, it includes a character image injection module that maintains character consistency while facilitating dynamic movements. An Audio Emotion Module (AEM) extracts emotional nuances from a source image, allowing for precise emotional control within the produced video content. Moreover, the Face-Aware Audio Adapter (FAA) isolates audio effects to distinct facial regions through latent-level masking, which supports independent audio-driven animations in scenarios involving multiple characters, enhancing the overall experience of storytelling through animated avatars. This comprehensive approach ensures that creators can craft richly animated narratives that resonate emotionally with audiences.

Veo 3.1 expands upon the features of its predecessor, allowing for the creation of longer and more adaptable AI-generated videos. This upgraded version empowers users to produce multi-shot videos based on various prompts, generate sequences using three reference images, and incorporate frames in video projects that smoothly transition between a starting and ending image, all while maintaining synchronized, native audio. A notable addition is the scene extension capability, which permits the lengthening of the last second of a clip by up to an entire minute of newly generated visuals and sound. Furthermore, Veo 3.1 includes editing tools for adjusting lighting and shadow effects, enhancing realism and consistency throughout the scenes, and features advanced object removal techniques that intelligently reconstruct backgrounds to eliminate unwanted elements from the footage. These improvements render Veo 3.1 more precise in following prompts, present a more cinematic experience, and provide a broader scope compared to models designed for shorter clips. Additionally, developers can easily utilize Veo 3.1 through the Gemini API or via the Flow tool, which is specifically aimed at enhancing professional video production workflows. This new version not only refines the creative process but also opens up new avenues for innovation in video content creation.

Tencent Hunyuan represents a comprehensive family of multimodal AI models crafted by Tencent, encompassing a range of modalities including text, images, video, and 3D data, all aimed at facilitating general-purpose AI applications such as content creation, visual reasoning, and automating business processes. This model family features various iterations tailored for tasks like natural language interpretation, multimodal comprehension that combines vision and language (such as understanding images and videos), generating images from text, creating videos, and producing 3D content. The Hunyuan models utilize a mixture-of-experts framework alongside innovative strategies, including hybrid "mamba-transformer" architectures, to excel in tasks requiring reasoning, long-context comprehension, cross-modal interactions, and efficient inference capabilities. A notable example is the Hunyuan-Vision-1.5 vision-language model, which facilitates "thinking-on-image," allowing for intricate multimodal understanding and reasoning across images, video segments, diagrams, or spatial information. This robust architecture positions Hunyuan as a versatile tool in the rapidly evolving field of AI, capable of addressing a diverse array of challenges.

VideoPoet is an innovative modeling technique that transforms any autoregressive language model or large language model (LLM) into an effective video generator. It comprises several straightforward components. An autoregressive language model is trained across multiple modalities—video, image, audio, and text—to predict the subsequent video or audio token in a sequence. The training framework for the LLM incorporates a range of multimodal generative learning objectives, such as text-to-video, text-to-image, image-to-video, video frame continuation, inpainting and outpainting of videos, video stylization, and video-to-audio conversion. Additionally, these tasks can be combined to enhance zero-shot capabilities. This straightforward approach demonstrates that language models are capable of generating and editing videos with impressive temporal coherence, showcasing the potential for advanced multimedia applications. As a result, VideoPoet opens up exciting possibilities for creative expression and automated content creation.

Qwen3-VL represents the latest addition to Alibaba Cloud's Qwen model lineup, integrating sophisticated text processing with exceptional visual and video analysis capabilities into a cohesive multimodal framework. This model accommodates diverse input types, including text, images, and videos, and it is adept at managing lengthy and intertwined contexts, supporting up to 256 K tokens with potential for further expansion. With significant enhancements in spatial reasoning, visual understanding, and multimodal reasoning, Qwen3-VL's architecture features several groundbreaking innovations like Interleaved-MRoPE for reliable spatio-temporal positional encoding, DeepStack to utilize multi-level features from its Vision Transformer backbone for improved image-text correlation, and text–timestamp alignment for accurate reasoning of video content and time-related events. These advancements empower Qwen3-VL to analyze intricate scenes, track fluid video narratives, and interpret visual compositions with a high degree of sophistication. The model's capabilities mark a notable leap forward in the field of multimodal AI applications, showcasing its potential for a wide array of practical uses.

Qwen3-Omni is a comprehensive multilingual omni-modal foundation model designed to handle text, images, audio, and video, providing real-time streaming responses in both textual and natural spoken formats. Utilizing a unique Thinker-Talker architecture along with a Mixture-of-Experts (MoE) framework, it employs early text-centric pretraining and mixed multimodal training, ensuring high-quality performance across all formats without compromising on text or image fidelity. This model is capable of supporting 119 different text languages, 19 languages for speech input, and 10 languages for speech output. Demonstrating exceptional capabilities, it achieves state-of-the-art performance across 36 benchmarks related to audio and audio-visual tasks, securing open-source SOTA on 32 benchmarks and overall SOTA on 22, thereby rivaling or equaling prominent closed-source models like Gemini-2.5 Pro and GPT-4o. To enhance efficiency and reduce latency in audio and video streaming, the Talker component leverages a multi-codebook strategy to predict discrete speech codecs, effectively replacing more cumbersome diffusion methods. Additionally, this innovative model stands out for its versatility and adaptability across a wide array of applications.

Gen-2: Advancing the Frontier of Generative AI. This innovative multi-modal AI platform is capable of creating original videos from text, images, or existing video segments. It can accurately and consistently produce new video content by either adapting the composition and style of a source image or text prompt to the framework of an existing video (Video to Video), or by solely using textual descriptions (Text to Video). This process allows for the creation of new visual narratives without the need for actual filming. User studies indicate that Gen-2's outputs are favored over traditional techniques for both image-to-image and video-to-video transformation, showcasing its superiority in the field. Furthermore, its ability to seamlessly blend creativity and technology marks a significant leap forward in generative AI capabilities.

Seaweed, an advanced AI model for video generation created by ByteDance, employs a diffusion transformer framework that boasts around 7 billion parameters and has been trained using computing power equivalent to 1,000 H100 GPUs. This model is designed to grasp world representations from extensive multi-modal datasets, which encompass video, image, and text formats, allowing it to produce videos in a variety of resolutions, aspect ratios, and lengths based solely on textual prompts. Seaweed stands out for its ability to generate realistic human characters that can exhibit a range of actions, gestures, and emotions, alongside a diverse array of meticulously detailed landscapes featuring dynamic compositions. Moreover, the model provides users with enhanced control options, enabling them to generate videos from initial images that help maintain consistent motion and aesthetic throughout the footage. It is also capable of conditioning on both the opening and closing frames to facilitate smooth transition videos, and can be fine-tuned to create content based on specific reference images, thus broadening its applicability and versatility in video production. As a result, Seaweed represents a significant leap forward in the intersection of AI and creative video generation.

The NVIDIA Nemotron 3 Nano Omni represents a groundbreaking open foundation model that integrates various modes of perception and reasoning—including text, images, audio, video, and documents—into a single streamlined architecture. By eliminating the necessity for distinct models tailored to each modality, it effectively minimizes inference delays, simplifies orchestration, and lowers costs while ensuring a cohesive cross-modal context. This innovative model is specifically engineered for agentic AI systems, functioning as a perception and context sub-agent that empowers larger AI entities to perceive and interpret their surroundings in real-time across various formats such as screens, recordings, and both structured and unstructured data. Its capabilities extend to complex multimodal reasoning tasks, encompassing document comprehension, speech recognition, extensive audio-video analysis, and intricate computer workflows, thus allowing agents to navigate dynamic interfaces and multifaceted environments with ease. With a hybrid architecture that is finely tuned for handling long contexts and high throughput, the Nemotron 3 Nano Omni is adept at managing sizable inputs, including multi-page documents, making it a versatile tool in the realm of AI development. Not only does it unify modalities, but it also enhances the overall efficiency of intelligent systems in processing and understanding diverse data types.

Marengo is an advanced multimodal model designed to convert video, audio, images, and text into cohesive embeddings, facilitating versatile “any-to-any” capabilities for searching, retrieving, classifying, and analyzing extensive video and multimedia collections. By harmonizing visual frames that capture both spatial and temporal elements with audio components—such as speech, background sounds, and music—and incorporating textual elements like subtitles and metadata, Marengo crafts a comprehensive, multidimensional depiction of each media asset. With its sophisticated embedding framework, Marengo is equipped to handle a variety of demanding tasks, including diverse types of searches (such as text-to-video and video-to-audio), semantic content exploration, anomaly detection, hybrid searching, clustering, and recommendations based on similarity. Recent iterations have enhanced the model with multi-vector embeddings that distinguish between appearance, motion, and audio/text characteristics, leading to marked improvements in both accuracy and contextual understanding, particularly for intricate or lengthy content. This evolution not only enriches the user experience but also broadens the potential applications of the model in various multimedia industries.

Hailuo 2.3 represents a state-of-the-art AI video creation model accessible via the Hailuo AI platform, enabling users to effortlessly produce short videos from text descriptions or still images, featuring seamless motion, authentic expressions, and a polished cinematic finish. This model facilitates multi-modal workflows, allowing users to either narrate a scene in straightforward language or upload a reference image, subsequently generating vibrant and fluid video content within seconds. It adeptly handles intricate movements like dynamic dance routines and realistic facial micro-expressions, showcasing enhanced visual consistency compared to previous iterations. Furthermore, Hailuo 2.3 improves stylistic reliability for both anime and artistic visuals, elevating realism in movement and facial expressions while ensuring consistent lighting and motion throughout each clip. A Fast mode variant is also available, designed for quicker processing and reduced costs without compromising on quality, making it particularly well-suited for addressing typical challenges encountered in ecommerce and marketing materials. This advancement opens up new possibilities for creative expression and efficiency in video production.

Wan2.5-Preview arrives with a groundbreaking multimodal foundation that unifies understanding and generation across text, imagery, audio, and video. Its native multimodal design, trained jointly across diverse data sources, enables tighter modal alignment, smoother instruction execution, and highly coherent audio-visual output. Through reinforcement learning from human feedback, it continually adapts to aesthetic preferences, resulting in more natural visuals and fluid motion dynamics. Wan2.5 supports cinematic 1080p video generation with synchronized audio, including multi-speaker content, layered sound effects, and dynamic compositions. Creators can control outputs using text prompts, reference images, or audio cues, unlocking a new range of storytelling and production workflows. For still imagery, the model achieves photorealism, artistic versatility, and strong typography, plus professional-level chart and design rendering. Its editing tools allow users to perform conversational adjustments, merge concepts, recolor products, modify materials, and refine details at pixel precision. This preview marks a major leap toward fully integrated multimodal creativity powered by AI.

HunyuanVideo is a cutting-edge video generation model powered by AI, created by Tencent, that expertly merges virtual and real components, unlocking endless creative opportunities. This innovative tool produces videos of cinematic quality, showcasing smooth movements and accurate expressions while transitioning effortlessly between lifelike and virtual aesthetics. By surpassing the limitations of brief dynamic visuals, it offers complete, fluid actions alongside comprehensive semantic content. As a result, this technology is exceptionally suited for use in various sectors, including advertising, film production, and other commercial ventures, where high-quality video content is essential. Its versatility also opens doors for new storytelling methods and enhances viewer engagement.

HunyuanVision, an innovative vision-language model created by Tencent's Hunyuan team, employs a mamba-transformer hybrid architecture that excels in performance and offers efficient inference for multimodal reasoning challenges. The latest iteration, Hunyuan-Vision-1.5, focuses on the concept of “thinking on images,” enabling it to not only comprehend the interplay of visual and linguistic content but also engage in advanced reasoning that includes tasks like cropping, zooming, pointing, box drawing, or annotating images for enhanced understanding. This model is versatile, supporting various vision tasks such as image and video recognition, OCR, and diagram interpretation, in addition to facilitating visual reasoning and 3D spatial awareness, all within a cohesive multilingual framework. Designed for compatibility across different languages and tasks, HunyuanVision aims to be open-sourced, providing access to checkpoints, a technical report, and inference support to foster community engagement and experimentation. Ultimately, this initiative encourages researchers and developers to explore and leverage the model's capabilities in diverse applications.

OmniHuman-1 is an innovative AI system created by ByteDance that transforms a single image along with motion cues, such as audio or video, into realistic human videos. This advanced platform employs multimodal motion conditioning to craft lifelike avatars that exhibit accurate gestures, synchronized lip movements, and facial expressions that correspond with spoken words or music. It has the flexibility to handle various input types, including portraits, half-body, and full-body images, and can generate high-quality videos even when starting with minimal audio signals. The capabilities of OmniHuman-1 go beyond just human representation; it can animate cartoons, animals, and inanimate objects, making it ideal for a broad spectrum of creative uses, including virtual influencers, educational content, and entertainment. This groundbreaking tool provides an exceptional method for animating static images, yielding realistic outputs across diverse video formats and aspect ratios, thereby opening new avenues for creative expression. Its ability to seamlessly integrate various forms of media makes it a valuable asset for content creators looking to engage audiences in fresh and dynamic ways.

Nova 2 Omni is an innovative model that seamlessly integrates multimodal reasoning and generation, allowing it to comprehend and generate diverse types of content, including text, images, video, and audio. Its capability to process exceptionally large inputs, which can encompass hundreds of thousands of words or several hours of audiovisual material, enables it to maintain a coherent analysis across various formats. As a result, it can simultaneously analyze comprehensive product catalogs, extensive documents, customer reviews, and entire video libraries, providing teams with a singular system that eliminates the necessity for multiple specialized models. By managing mixed media within a unified workflow, Nova 2 Omni paves the way for new opportunities in both creative and operational automation. For instance, a marketing team can input product specifications, brand standards, reference visuals, and video content to effortlessly generate an entire campaign that includes messaging, social media content, and visuals, all in one streamlined process. This efficiency not only enhances productivity but also fosters innovation in how teams approach their marketing strategies.

Seedance 1.5 Pro, an advanced AI model for audio and video generation, has been created by the Seed research team at ByteDance to produce synchronized video and sound seamlessly from text prompts alongside image or visual inputs, which removes the conventional approach of generating visuals before adding audio. This innovative model is designed for joint audio-visual generation, achieving precise lip-sync and motion alignment while offering support for multilingual audio and spatial sound effects that enhance the storytelling experience. Furthermore, it ensures visual consistency and maintains cinematic motion throughout multi-shot sequences, accommodating camera movements and narrative continuity. The system can generate short clips, typically ranging from 4 to 12 seconds, in resolutions up to 1080p and features expressive motion, stable aesthetics, and options for controlling the first and last frames. It caters to both text-to-video and image-to-video workflows, enabling creators to animate still images or construct complete cinematic sequences that flow coherently, thus expanding creative possibilities in audiovisual production. Ultimately, Seedance 1.5 Pro stands as a transformative tool for content creators aiming to elevate their storytelling capabilities.

LTX-2.3 represents a cutting-edge AI video generation model that transforms text prompts, images, or various media inputs into high-quality videos, all while ensuring precise control over motion, structure, and the synchronization of audio and visuals. This model is a key component of the LTX series of multimodal generative tools aimed at developers and production teams seeking scalable solutions for programmatic video creation and editing. Enhancements over previous LTX versions include improved detail rendering, greater motion consistency, superior prompt comprehension, and enhanced audio quality throughout the video creation process. One of its standout features is a newly designed latent representation, utilizing an upgraded VAE trained on more refined datasets, which significantly enhances the retention of intricate details such as fine textures, edges, and small visual elements like hair, text, and complex surfaces across multiple frames. This evolution in video generation technology marks a significant leap forward for creators and professionals in the multimedia domain.

Wan 2.6 is a state-of-the-art video generation model developed by Alibaba for high-fidelity multimodal content creation. It enables users to generate short videos directly from text prompts, images, or existing video inputs. The model produces clips up to 15 seconds long while preserving visual coherence and storytelling quality. Built-in audio and visual synchronization ensures that speech, music, and sound effects match the generated visuals seamlessly. Wan 2.6 delivers fluid motion, realistic character animation, and smooth camera transitions. Advanced lip-sync capabilities enhance realism in dialogue-driven scenes. The model supports multiple resolutions, making it suitable for professional and social media use. Users can animate still images into consistent video sequences without losing character identity. Flexible prompt handling supports multiple languages natively. Wan 2.6 streamlines short-form video production with speed and precision.

Ray2 represents a cutting-edge video generation model that excels at producing lifelike visuals combined with fluid, coherent motion. Its proficiency in interpreting text prompts is impressive, and it can also process images and videos as inputs. This advanced model has been developed using Luma’s innovative multi-modal architecture, which has been enhanced to provide ten times the computational power of its predecessor, Ray1. With Ray2, we are witnessing the dawn of a new era in video generation technology, characterized by rapid, coherent movement, exquisite detail, and logical narrative progression. These enhancements significantly boost the viability of the generated content, resulting in videos that are far more suitable for production purposes. Currently, Ray2 offers text-to-video generation capabilities, with plans to introduce image-to-video, video-to-video, and editing features in the near future. The model elevates the quality of motion fidelity to unprecedented heights, delivering smooth, cinematic experiences that are truly awe-inspiring. Transform your creative ideas into stunning visual narratives, and let Ray2 help you create mesmerizing scenes with accurate camera movements that bring your story to life. In this way, Ray2 empowers users to express their artistic vision like never before.

Qwen3.5-Omni, an advanced multimodal AI model created by Alibaba, seamlessly integrates the understanding and generation of text, images, audio, and video within a cohesive framework, facilitating more intuitive and instantaneous interactions between humans and AI. In contrast to conventional models that analyze each modality in isolation, this innovative system is built from the ground up using vast audiovisual datasets, enabling it to effectively manage intricate inputs like lengthy audio recordings, videos, and spoken commands concurrently while excelling in all formats. It accommodates long-context inputs of up to 256K tokens and is capable of processing over ten hours of audio or extended video sequences, making it ideal for high-demand real-world scenarios. A standout characteristic of this model is its sophisticated voice interaction features, which encompass end-to-end speech dialogue, the ability to control emotional tone, and voice cloning, allowing for extraordinarily natural conversational exchanges that can vary in volume and adapt speaking styles in real-time. Furthermore, this versatility ensures that users can enjoy a truly personalized and engaging interaction experience.

CogVideoX-3 is an advanced video generation model that enhances frame creation, resulting in improved image clarity and stability. It excels in scenarios involving fast-moving subjects, follows instructions more accurately, and offers highly realistic video simulations. The model accommodates various input types including images, text, and start-and-end-frame sequences, producing video outputs, which broadens its application in text-to-video, image-to-video, and transitional video processes. This versatility makes CogVideoX-3 particularly valuable for advertising and marketing, enabling users to input product visuals or marketing copy to swiftly generate engaging ads in diverse styles, while also supporting realistic lighting and seamless scene transitions. Additionally, it facilitates the production of short videos by transforming single-frame images or written scripts into fluid, naturally animated clips, available in both realistic and 3D formats. For tourism marketing, users can simply upload scenic photographs along with promotional text to create captivating short videos that enhance the appeal of travel destinations, effectively drawing in potential visitors. Ultimately, CogVideoX-3 empowers creators across various industries to produce high-quality video content with ease.

WaveSpeedAI stands out as a powerful generative media platform engineered to significantly enhance the speed of creating images, videos, and audio by leveraging advanced multimodal models paired with an exceptionally quick inference engine. It accommodates a diverse range of creative processes, including transforming text into video, converting images into video, generating images from text, producing voice content, and developing 3D assets, all through a cohesive API built for scalability and rapid performance. The platform integrates leading foundation models such as WAN 2.1/2.2, Seedream, FLUX, and HunyuanVideo, granting users seamless access to an extensive library of models. With its remarkable generation speeds, real-time processing capabilities, and enterprise-level reliability, users enjoy consistently high-quality outcomes. WaveSpeedAI focuses on delivering a “fast, vast, efficient” experience, ensuring quick production of creative assets, access to a comprehensive selection of cutting-edge models, and economical execution that maintains exceptional quality. Additionally, this platform is tailored to meet the demands of modern creators, making it an indispensable tool for anyone looking to elevate their media production capabilities.

Wan3.0-Video is an integrated video generation tool from Qwen Cloud that consolidates a variety of creative functionalities within a single platform, such as converting text to video, transforming images into video, and creating videos based on references, along with editing, duplication, and motion guidance. This model accommodates various inputs including audio, images, text, and videos, enabling creators to influence the generation process with diverse source materials beyond mere text prompts. Capable of producing videos that last up to 30 seconds, it offers omni-modal reference support, which enhances user flexibility by allowing the incorporation of visual elements, movement, characters, and other artistic cues into the final product. Additionally, Wan3.0-Video can analyze files, web pages, and intricate images as part of its creation process. Its image-to-video features encompass both first-frame and first-and-last-frame generation, enabling users to control the initiation of a sequence or anchor both ends of a shot, thus enhancing the storytelling potential of the videos produced. Furthermore, this comprehensive model opens up new avenues for creativity by allowing seamless integration of different media types into the video production process.

HunyuanWorld-1.0 is an open-source AI framework and generative model created by Tencent Hunyuan, designed to generate immersive, interactive 3D environments from text inputs or images by merging the advantages of both 2D and 3D generation methods into a single cohesive process. Central to the framework is a semantically layered 3D mesh representation that utilizes 360° panoramic world proxies to break down and rebuild scenes with geometric fidelity and semantic understanding, allowing for the generation of varied and coherent spaces that users can navigate and engage with. In contrast to conventional 3D generation techniques that often face challenges related to limited diversity or ineffective data representations, HunyuanWorld-1.0 adeptly combines panoramic proxy creation, hierarchical 3D reconstruction, and semantic layering to achieve a synthesis of high visual quality and structural soundness, while also providing exportable meshes that fit seamlessly into standard graphics workflows. This innovative approach not only enhances the realism of generated environments but also opens new possibilities for creative applications in various industries.

Ming-Flash Omni 2.0, developed by Ant Group, represents a comprehensive large language model that operates on a cohesive multimodal framework, emphasizing a philosophy of “modal unity + task unity.” This model, as a part of the Ming series, is engineered to facilitate an integrated understanding and generation of content across various modalities, including text, images, audio, and video, thus eliminating the need for multiple specialized models to perform distinct tasks such as seeing, hearing, speaking, and drawing. Progressing from its predecessors, Ming-Light Omni and Ming-Flash Omni Preview, this iteration advances from validating a unified architecture and scaling to hundreds of billions of parameters to implementing a Data Scaling approach that achieves state-of-the-art performance in open-source environments across numerous benchmarks. Notably, the model encompasses four essential capability modules: image-text comprehension, video interpretation, speech generation, and image creation or manipulation. To enhance image-text understanding, Ming employs structured knowledge graphs that contribute to a more nuanced visual perception. This innovative approach not only broadens the model's applicability but also sets a new standard in the field of artificial intelligence.

Kling O1 serves as a generative AI platform that converts text, images, and videos into high-quality video content, effectively merging video generation with editing capabilities into a cohesive workflow. It accommodates various input types, including text-to-video, image-to-video, and video editing, and features an array of models, prominently the “Video O1 / Kling O1,” which empowers users to create, remix, or modify clips utilizing natural language prompts. The advanced model facilitates actions such as object removal throughout an entire clip without the need for manual masking or painstaking frame-by-frame adjustments, alongside restyling and the effortless amalgamation of different media forms (text, image, and video) for versatile creative projects. Kling AI prioritizes smooth motion, authentic lighting, cinematic-quality visuals, and precise adherence to user prompts, ensuring that actions, camera movements, and scene transitions closely align with user specifications. This combination of features allows creators to explore new dimensions of storytelling and visual expression, making the platform a valuable tool for both professionals and hobbyists in the digital content landscape.

The Kling 3.0 Omni model represents an innovative generative video platform that crafts creative videos from text inputs, images, or other reference materials by utilizing cutting-edge multimodal AI technology. This system enables the production of seamless video clips with duration options that span from about 3 to 15 seconds, perfect for creating brief cinematic sequences that align closely with user prompts. Additionally, it accommodates both prompt-driven video creation and workflows based on visual references, allowing users to input images or other visual cues to influence the scene's subject, style, or composition. By enhancing prompt fidelity and maintaining subject consistency, the model ensures that characters, objects, and environments exhibit stability throughout the duration of the video while also delivering realistic motion and visual coherence. Moreover, the Omni model significantly boosts reference-based generation, ensuring that characters or elements introduced via images retain their recognizability across multiple frames, thereby enriching the overall viewing experience. This capability makes it an invaluable tool for creators seeking to produce visually engaging content with ease and precision.

Veo 3.1 Fast represents a major leap forward in generative video technology, combining the creative intelligence of Veo 3.1 with faster generation times and expanded control. Available through the Gemini API, the model turns written prompts and still images into cinematic videos with synchronized sound and expressive storytelling. Developers can guide scene generation using up to three reference images, extend video length continuously with “Scene Extension,” and even create dynamic transitions between first and last frames. Its enhanced AI engine maintains character and visual consistency across sequences while improving adherence to user intent and narrative tone. Veo 3.1 Fast’s audio generation adds depth with natural voices and realistic soundscapes, enabling richer, more immersive outputs. Integration with Google AI Studio and Gemini Enterprise Agent Platform makes it simple to build, test, and deploy creative applications. Leading creative teams, such as Promise Studios and Latitude, are already using Veo 3.1 Fast for generative filmmaking and interactive storytelling. Offering the same price as Veo 3.0 but vastly improved capability, it sets a new benchmark for AI-driven video production.

Our advanced multimodal assistant is meticulously crafted with a focus on privacy, security, and operational efficiency. Yasa is trained to interpret various forms of content, including text, images, videos, and tabular data, with plans to expand to additional modalities in the future. It can assist you in brainstorming for creative projects, answering fundamental questions, or extracting valuable insights from your internal datasets. With just a few straightforward commands, you can generate, train, compress, or deploy it on your own servers. Our proprietary algorithms enable you to customize the model according to your specific data and requirements. We utilize innovative techniques that encompass retrieval, fine-tuning, self-supervised instruction tuning, and reinforcement learning to optimize our model based on your unique datasets, ensuring that it meets your operational needs effectively. In doing so, we aim to enhance user experience and deliver tailored solutions that drive productivity and innovation.

Gen-3 Alpha marks the inaugural release in a new line of models developed by Runway, leveraging an advanced infrastructure designed for extensive multimodal training. This model represents a significant leap forward in terms of fidelity, consistency, and motion capabilities compared to Gen-2, paving the way for the creation of General World Models. By being trained on both videos and images, Gen-3 Alpha will enhance Runway's various tools, including Text to Video, Image to Video, and Text to Image, while also supporting existing functionalities like Motion Brush, Advanced Camera Controls, and Director Mode. Furthermore, it will introduce new features that allow for more precise manipulation of structure, style, and motion, offering users even greater creative flexibility.

Seedance 2.0 is a next-generation AI video creation model developed by ByteDance to simplify high-quality video production. It allows users to generate complete videos using text, images, audio, and existing clips as creative inputs. The platform excels at maintaining visual coherence, ensuring characters, styles, and scenes remain consistent across shots. Advanced motion synthesis enables smooth transitions and realistic camera movement throughout each video. Users can reference multiple assets at once, combining visuals and sound to shape the final output. Seedance 2.0 removes the need for traditional editing tools by handling pacing and shot composition automatically. Videos are produced in professional-grade resolutions suitable for commercial use. The model has gained attention for producing complex animated sequences, including anime-style visuals. It empowers individual creators and small teams to achieve studio-like results. At the same time, it introduces new conversations around responsible AI use and content authenticity.

MiniMax is an AI platform that provides multimodal foundation models, developer tools, and intelligent agent solutions designed to support coding, automation, content generation, and enterprise AI applications. The company’s flagship model, MiniMax M3, delivers advanced coding performance, long-context processing capabilities of up to one million tokens, and native multimodal functionality that enables seamless understanding and generation of text, audio, images, video, and music. MiniMax Code serves as an AI-powered coding environment that allows users to build agent teams, automate repetitive development tasks, create custom skills, and manage workflows through a unified conversational interface. In addition to coding solutions, the platform offers video generation through Hailuo AI, speech and music generation models, conversational AI products, and developer APIs for integrating AI into custom applications. The platform is designed to support both individual users and enterprise teams seeking scalable AI tools for software development, business automation, creative production, and research. By combining frontier AI models with practical productivity applications, MiniMax enables organizations to streamline operations, enhance innovation, and build intelligent systems more efficiently.

Tencent HY is a versatile and comprehensive family of large models developed in-house by Tencent, designed to deliver AI solutions tailored for enterprise needs across various domains such as content creation, business automation, and real-world agent functionalities. It encompasses multiple modalities, including language, imagery, 3D modeling, and translation, seamlessly integrating Tencent’s proprietary algorithms with advanced natural language processing and computer vision technologies to enable superior image generation, 3D content creation, and intelligent applications. Via Tencent Hunyuan AI Studio, users can engage with the model through intuitive human-computer dialogue, which allows the system to comprehend commands, perform tasks, assist users in information retrieval, generate content, and discover the model's extensive capabilities in a user-friendly environment. Additionally, Tencent HY facilitates API integration and customizable parameter configurations, enhancing accessibility and usability for developers, product teams, and enterprise-focused applications. This adaptability ensures that a wide range of users can leverage the power of Tencent HY in their projects, driving innovation and efficiency across industries.

SeyftAI is an advanced platform for real-time, multi-modal content moderation that effectively screens harmful and irrelevant materials across various formats, including text, images, and videos, to guarantee compliance while providing customized solutions for different languages and cultural nuances. With a wide-ranging set of tools, SeyftAI assists in maintaining clean and safe digital environments. It can identify and eliminate harmful textual content in numerous languages effortlessly. The API provided by SeyftAI facilitates the smooth integration of its content moderation features into your existing applications and workflows. Additionally, it can autonomously detect and filter out inappropriate or explicit images without the need for human oversight. SeyftAI enables users to customize content moderation workflows according to their unique requirements. Furthermore, users can obtain detailed reports and analytics on their content moderation efforts, enhancing transparency and effectiveness. By utilizing this platform, businesses can ensure that their digital content remains safe and compliant, adapting to the ever-evolving landscape of online interactions.

Kling 2.6 is a next-generation AI video model built to merge sound and visuals into a single, seamless creative process. It eliminates the need for separate voiceovers, sound effects, and audio mixing by generating everything at once. Users can create complete videos from either text prompts or images with synchronized audio output. Kling 2.6 produces natural speech, ambient soundscapes, and action-based sound effects that match visual motion and pacing. The Native Audio system ensures emotional consistency between dialogue, background audio, and scene dynamics. Creators have control over who speaks, how they sound, and the overall mood of the video. The model supports narration, dialogue, music, and mixed sound effects. Kling 2.6 simplifies professional video creation for small teams and solo creators. Its intuitive workflow reduces technical complexity while maintaining creative flexibility. The result is faster production of immersive, shareable video content.

Wan2.2 Animate is a dedicated component of the Wan video generation suite, which focuses on producing high-quality character animations and facilitating character swaps in videos. This module empowers users to convert still images into lively videos or change subjects in pre-existing clips while ensuring that realism and motion continuity are upheld. It operates by utilizing two main inputs: a reference image that illustrates the character's look and a reference video that conveys the necessary motion, expressions, and context of the scene. By combining these elements, it can effectively bring a static character to life by mirroring the body movements, gestures, and facial expressions from the provided video or replace an existing character while keeping the original lighting, camera dynamics, and surrounding environment intact for a fluid transition. The technology employs sophisticated methodologies, including spatially aligned skeleton signals and implicit facial feature extraction, to faithfully capture and reproduce the nuances of movement and expression. Moreover, the module's innovative design allows for a wide range of creative applications in filmmaking and animation, making it a valuable tool for content creators.

Pika Soundtrack is an innovative model that transforms silent videos into rich audio experiences by integrating motion-sensitive sound effects, music, ambient noises, and voiceovers that align perfectly with the visual content. Users have the option to leave the input prompt empty for the model to create a complete soundscape automatically or to provide specific instructions regarding which elements to highlight, include, or exclude. Unlike conventional methods that merely attach sounds to videos, this model comprehensively analyzes the scene, ensuring that every sound is precisely timed and that all audio components remain consistent throughout the video. This thoughtful synchronization allows for a seamless blend of sound effects, ambient sounds, music, and dialogue, giving the impression that they all naturally coexist within the same environment. According to Pika's testing, Soundtrack outperformed other models like LTX-2.3 Foley V2A, HunyuanVideo-Foley, and MMAudio v2 in achieving the best semantic coherence and minimal audiovisual misalignment in its full-duration benchmark. The ability to capture the essence of a scene while maintaining audio clarity makes Pika Soundtrack a standout choice for video creators looking to enhance their content.

Runway Aleph represents a revolutionary advancement in in-context video modeling, transforming the landscape of multi-task visual generation and editing by allowing extensive modifications on any video clip. This model can effortlessly add, delete, or modify objects within a scene, create alternative camera perspectives, and fine-tune style and lighting based on either natural language commands or visual cues. Leveraging advanced deep-learning techniques and trained on a wide range of video data, Aleph functions entirely in context, comprehending both spatial and temporal dynamics to preserve realism throughout the editing process. Users are empowered to implement intricate effects such as inserting objects, swapping backgrounds, adjusting lighting dynamically, and transferring styles without the need for multiple separate applications for each function. The user-friendly interface of this model is seamlessly integrated into Runway's Gen-4 ecosystem, providing an API for developers alongside a visual workspace for creators, making it a versatile tool for both professionals and enthusiasts in video editing. With its innovative capabilities, Aleph is set to revolutionize how creators approach video content transformation.

Runway Gen-4.5 stands as a revolutionary text-to-video AI model by Runway, offering stunningly realistic and cinematic video results with unparalleled precision and control. This innovative model marks a significant leap in AI-driven video production, effectively utilizing pre-training data and advanced post-training methods to redefine the limits of video creation. Gen-4.5 particularly shines in generating dynamic actions that are controllable, ensuring temporal consistency while granting users meticulous oversight over various elements such as camera movement, scene setup, timing, and mood, all achievable through a single prompt. As per independent assessments, it boasts the top ranking on the "Artificial Analysis Text-to-Video" leaderboard, scoring an impressive 1,247 Elo points and surpassing rival models developed by larger laboratories. This capability empowers creators to craft high-quality video content from initial idea to final product, all without reliance on conventional filmmaking tools or specialized knowledge. The ease of use and efficiency of Gen-4.5 further revolutionizes the landscape of video production, making it accessible to a broader audience.