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Description
K2 Horizon comprises a network of six open models, including the 375B-A23B, 36B-A4B, 32B, 7B, 3.7B, and 0.9B, all engineered to excel in various domains such as reasoning, mathematics, coding, agentic tasks, and overall capabilities. These models share a unified architecture, vocabulary, training techniques, interfaces, evaluation frameworks, and deployment tools, facilitating seamless transitions between sizes and dynamic workload management. The fleet's flagship, the 375B-A23B model, is particularly adept at handling intricate reasoning, software development, research projects, and long-term agentic functions, while the 32B and 36B-A4B models focus on delivering robust local deployment solutions. Notably, the 36B-A4B model features an innovative Mixture-of-Value Attention mechanism, which merges sparse attention with Mixture-of-Experts layers, allowing it to engage approximately 4 billion parameters per token and compete closely with the performance of the denser 32B model. This architecture not only enhances flexibility but also maximizes resource efficiency across a wide range of 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
API Access
Has API
Integrations
APIFree
Alibaba Cloud Model Studio
BaseRT
Claw Code
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Together AI
ZooClaw
Integrations
APIFree
Alibaba Cloud Model Studio
BaseRT
Claw Code
Ollama
OpenClaw
Qwen
Qwen3.5-Plus
Together AI
ZooClaw
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Open source
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
Institute of Foundation Models
Founded
2025
Country
United States
Website
ifm.ai/blog/k2/
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai