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Average Ratings 0 Ratings
Description
Qwen3-Max-Thinking represents Alibaba's newest flagship model in the realm of large language models, extending the capabilities of the Qwen3-Max series while emphasizing enhanced reasoning and analytical performance. This model builds on one of the most substantial parameter sets within the Qwen ecosystem and integrates sophisticated reinforcement learning alongside adaptive tool functionalities, allowing it to utilize search, memory, and code interpretation dynamically during the inference process, thus effectively tackling complex multi-stage challenges with improved precision and contextual understanding compared to traditional generative models. It features an innovative Thinking Mode that provides a clear, step-by-step display of its reasoning processes prior to producing final results, which enhances both transparency and the traceability of its logical conclusions. Furthermore, Qwen3-Max-Thinking can be adjusted with customizable "thinking budgets," allowing users to find an optimal balance between the quality of performance and the associated computational costs, making it an efficient tool for various applications. The incorporation of these features marks a significant advancement in the way language models can assist in complex reasoning tasks.
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
Integrations
OpenClaw
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
Integrations
OpenClaw
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
Pricing Details
No price information available.
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/blog
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai/blog