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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Qwen3 is a state-of-the-art large language model designed to revolutionize the way we interact with AI. Featuring both thinking and non-thinking modes, Qwen3 allows users to customize its response style, ensuring optimal performance for both complex reasoning tasks and quick inquiries. With the ability to support 119 languages, the model is suitable for international projects. The model's hybrid training approach, which involves over 36 trillion tokens, ensures accuracy across a variety of disciplines, from coding to STEM problems. Its integration with platforms such as Hugging Face, ModelScope, and Kaggle allows for easy adoption in both research and production environments. By enhancing multilingual support and incorporating advanced AI techniques, Qwen3 is designed to push the boundaries of AI-driven applications.

Description

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

BaseRT Yes 
OpenClaw Yes 
APIFree No 
C Yes 
Clojure Yes 
ExecuTorch Yes 
HTML Yes 
Hugging Face Yes 
JavaScript Yes 
LFM2 Yes 
Okara Yes 
Phala Yes 
R Yes 
Runpod Yes 
SSSModel Yes 
Scala Yes 
Sesterce Yes 
SiliconFlow Yes 
TypeScript Yes 
kluster.ai Yes 

Integrations

BaseRT Yes 
OpenClaw Yes 
APIFree Yes 
C No 
Clojure No 
ExecuTorch No 
HTML No 
Hugging Face No 
JavaScript No 
LFM2 No 
Okara No 
Phala No 
R No 
Runpod No 
SSSModel No 
Scala No 
Sesterce No 
SiliconFlow No 
TypeScript No 
kluster.ai No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook 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 

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 No 

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/qwen3/

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai

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