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Description
Qwen is a next-generation AI system that brings advanced intelligence to users and developers alike, offering free access to a versatile suite of tools. Its capabilities include Qwen VLo for image generation, Deep Research for multi-step online investigation, and Web Dev for generating full websites from natural language prompts. The “Thinking” engine enhances Qwen’s reasoning and logical clarity, helping it tackle complex technical, analytical, and academic challenges. Qwen’s intelligent Search mode retrieves web information with precision, using contextual understanding and smart filtering. Its multimodal processing allows it to interpret content across text, images, audio, and video, enabling more accurate and comprehensive responses. Qwen Chat makes these features accessible to everyone, while developers can tap into the Qwen API to build apps, integrate Qwen into workflows, or create entirely new AI-driven experiences. The API follows an OpenAI-compatible format, making migration and adoption seamless. With broad platform support—web, Windows, macOS, iOS, and Android—Qwen delivers a unified, powerful AI ecosystem for all kinds of users.
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
API Access
Has API
Screenshots View All
No images available
Integrations
Alibaba Cloud
Alibaba Cloud Model Studio
ClinePass
Hugging Face
ModelScope
Novita AI
OfoxAI
Ollama
OpenClaw
Python
Integrations
Alibaba Cloud
Alibaba Cloud Model Studio
ClinePass
Hugging Face
ModelScope
Novita AI
OfoxAI
Ollama
OpenClaw
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
$2 per 1M (input)
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai/
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai/blog