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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

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.

Description

Qwen3.8-Flash-Next represents an open-weight multimodal Mixture-of-Experts architecture and serves as an initial glimpse into the design intended for Qwen4. This model strategically enhances attention mechanisms, residual pathways, embeddings, and optimization techniques to boost its capabilities, improve computational efficiency, expand model capacity, and ensure training stability. Its innovative hybrid architecture merges Gated DeltaNet, which adeptly compresses past information, with Qwen Sparse Attention, enabling the selection of significant context at a micro-block level to lessen both attention and indexing costs associated with lengthy sequences. The Gated Residual feature broadens the residual pathway into four streams, dynamically managing the flow of information across different layers. Additionally, the N-gram Embedding integrates large-scale local-pattern memory with minimal added computation per token, and it can be transferred to host memory for further efficiency. The model is structured around a 125B-parameter main network supplemented by 51B parameters dedicated to N-gram embeddings, activating only 6B parameters for each token processed. This sophisticated framework highlights the ongoing advancements in machine learning architectures, setting a promising stage for future developments.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Happy Shrimp 1.0 Yes 
Alibaba Cloud No 
Alibaba Cloud Model Studio No 
Cherry Studio No 
ClinePass No 
Hermes Agent No 
Hugging Face No 
Model Context Protocol (MCP) No 
ModelScope No 
Novita AI No 
Odysseus No 
OfoxAI No 
Ollama No 
OpenClaw No 
Python No 
Qwen No 
Qwen Code No 
Qwen Studio No 
QwenCloud No 
QwenWork No 

Integrations

Happy Shrimp 1.0 Yes 
Alibaba Cloud Yes 
Alibaba Cloud Model Studio Yes 
Cherry Studio Yes 
ClinePass Yes 
Hermes Agent Yes 
Hugging Face Yes 
Model Context Protocol (MCP) Yes 
ModelScope Yes 
Novita AI Yes 
Odysseus Yes 
OfoxAI Yes 
Ollama Yes 
OpenClaw Yes 
Python Yes 
Qwen Yes 
Qwen Code Yes 
Qwen Studio Yes 
QwenCloud Yes 
QwenWork Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

$2 per 1M (input)
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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