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What is Wan3.0?

Wan3.0 is a comprehensive video generation platform developed by Qwen Cloud that combines multiple creative features within a single interface, including the ability to convert text into video, turn images into video, and create videos inspired by references, alongside capabilities for editing, duplication, and motion guidance. This versatile model supports an array of inputs like audio, images, text, and videos, which empowers creators to shape the generation process using diverse sources beyond simple text prompts. With the capacity to create videos lasting up to 30 seconds, it provides omni-modal reference support, thereby enhancing user creativity by facilitating the integration of visual elements, movements, characters, and various artistic cues into the final output. Additionally, Wan3.0 can process files, analyze web pages, and interpret complex images during its creation phase. Its image-to-video functionality allows for both first-frame and first-and-last-frame generation, offering users the ability to dictate the beginning of a sequence or anchor both ends of a shot, thus significantly enriching the storytelling potential of the generated videos. Moreover, this all-encompassing model fosters innovative creative possibilities by enabling the effortless combination of different media types throughout the video production workflow, ultimately transforming the way creators engage with their projects.

What is Qwen3-Omni?

Qwen3-Omni represents a cutting-edge multilingual omni-modal foundation model adept at processing text, images, audio, and video, and it delivers real-time responses in both written and spoken forms. It features a distinctive Thinker-Talker architecture paired with a Mixture-of-Experts (MoE) framework, employing an initial text-focused pretraining phase followed by a mixed multimodal training approach, which guarantees superior performance across all media types while maintaining high fidelity in both text and images. This advanced model supports an impressive array of 119 text languages, alongside 19 for speech input and 10 for speech output. Exhibiting remarkable capabilities, it achieves top-tier performance across 36 benchmarks in audio and audio-visual tasks, claiming open-source SOTA on 32 benchmarks and overall SOTA on 22, thus competing effectively with notable closed-source alternatives like Gemini-2.5 Pro and GPT-4o. To optimize efficiency and minimize latency in audio and video delivery, the Talker component employs a multi-codebook strategy for predicting discrete speech codecs, which streamlines the process compared to traditional, bulkier diffusion techniques. Furthermore, its remarkable versatility allows it to adapt seamlessly to a wide range of applications, making it a valuable tool in various fields. Ultimately, this model is paving the way for the future of multimodal interaction.

Media

Media

Integrations Supported

Alibaba Cloud Model Studio
Happy Shrimp 1.0
Makernice
QwenCloud

Integrations Supported

ConvNetJS
GPT-4o
Gemini 2.5 Pro
Gemini 2.5 Pro Deep Think
Gemini 3 Deep Think
Hermes Agent
OpenClaw

API Availability

Has API

API Availability

Has API

Pricing Information

$0.05 per second

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

wan.video/

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai/blog

Categories and Features

AI Video Models

Not specified

Categories and Features

AI Models

Not specified

AI Video Models

Not specified

Foundation Models

Not specified

Multimodal Models

Not specified

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