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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

QwQ-Max-Preview is a cutting-edge AI model based on the Qwen2.5-Max framework, specifically engineered to excel in areas such as complex reasoning, mathematical problem-solving, programming, and agent tasks. This preview showcases its enhanced capabilities across a variety of general-domain applications while demonstrating proficiency in managing intricate workflows. Anticipated to be officially released as open-source software under the Apache 2.0 license, QwQ-Max-Preview promises significant improvements and upgrades in its final iteration. Additionally, it contributes to the development of a more inclusive AI environment, as evidenced by the forthcoming introduction of the Qwen Chat application and streamlined model versions like QwQ-32B, which cater to developers interested in local deployment solutions. This initiative not only broadens accessibility but also encourages innovation within the AI community.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Qwen Studio Yes 
Alibaba Cloud No 
Alibaba Cloud Model Studio No 
Cherry Studio No 
Cline No 
ClinePass No 
Happy Shrimp 1.0 No 
Hermes Agent No 
Hugging Face No 
ModelScope No 
Novita AI No 
Odysseus No 
OfoxAI No 
Ollama No 
OpenClaw No 
Python No 
Qwen No 
QwenCloud No 
QwenWork No 
Sesterce Yes 

Integrations

Qwen Studio Yes 
Alibaba Cloud Yes 
Alibaba Cloud Model Studio Yes 
Cherry Studio Yes 
Cline Yes 
ClinePass Yes 
Happy Shrimp 1.0 Yes 
Hermes Agent Yes 
Hugging Face Yes 
ModelScope Yes 
Novita AI Yes 
Odysseus Yes 
OfoxAI Yes 
Ollama Yes 
OpenClaw Yes 
Python Yes 
Qwen Yes 
QwenCloud Yes 
QwenWork Yes 
Sesterce No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

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

Deployment

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

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

qwenlm.github.io/blog/qwq-max-preview/

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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