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Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

Qwen3.7-Plus is an advanced multimodal agent model that seamlessly integrates vision and language into a single, adaptable foundation for intelligent agents. Expanding upon the agentic intelligence of Qwen3.7, it enhances its abilities to include visual comprehension, reasoning, grounded interactions, and the use of various multimodal tools, allowing agents to perceive, analyze, and operate within text, images, documents, screens, and intricate real-world scenarios. This model is specifically crafted for dynamic tasks that go beyond mere static question answering, facilitating activities such as visual searches, document understanding, chart and table evaluations, screen comprehension, GUI interactions, image-driven reasoning, and workflows where perception, planning, and action are interlinked. Qwen3.7-Plus fortifies the relationship between linguistic reasoning and visual cues, empowering users to inquire about images, decode complex multimodal information, extract organized data, and formulate responses that incorporate both contextual and visual elements, thus broadening the scope of interactive AI applications. With these enhancements, users can engage in more sophisticated and nuanced interactions with the system, making it a powerful tool for various practical applications.

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

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

Integrations

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

Pricing Details

No price information available.
Free Trial No 
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) No 
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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