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ease
features
design
support

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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

Qwen3.8-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

Screenshots View All

Screenshots View All

Integrations

Alibaba Cloud
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations

Alibaba Cloud
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

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

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/blog

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