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Average Ratings 0 Ratings
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.
Description
UNI-1, a groundbreaking multimodal artificial intelligence model from Luma AI, combines visual generation and reasoning within a singular framework, marking progress towards achieving multimodal general intelligence. This innovative design addresses the challenges faced by conventional AI systems, where various components like language models and image generators function in isolation, lacking cohesive reasoning. By merging these features, UNI-1 enables seamless interaction between language comprehension, visual analysis, and image creation, allowing the model to logically interpret scenes, follow instructions, and produce visual outputs that adhere to both logical and spatial parameters. Central to its architecture is a decoder-only autoregressive transformer that processes both text and images as a unified sequence of tokens, facilitating a coherent interaction between linguistic and visual data. This integration not only enhances the efficiency of the AI but also broadens the scope of its applications across various domains.
API Access
Has API
API Access
Has API
Integrations
APIFree
Alibaba Cloud Model Studio
Claw Code
Luma AI
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Together AI
ZooClaw
Integrations
APIFree
Alibaba Cloud Model Studio
Claw Code
Luma AI
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Together AI
ZooClaw
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
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
Luma AI
Founded
2021
Country
United States
Website
lumalabs.ai/uni-1