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What is Qwen3.6-35B-A3B?

Qwen3.5-35B-A3B is part of the Qwen3.5 "Medium" model lineup, designed as an efficient multimodal foundation model that effectively balances strong reasoning skills with real-world application demands. It features a Mixture-of-Experts (MoE) architecture, comprising 35 billion parameters but activating approximately 3 billion for each token, which allows it to deliver performance comparable to much larger models while significantly reducing computational costs. The model incorporates a hybrid attention mechanism that fuses linear attention with conventional attention layers, enhancing its capability to manage extensive context and improving scalability for complex tasks. As a vision-language model, it adeptly processes both text and visual inputs, catering to a wide range of applications such as multimodal reasoning, programming, and automated workflows. Additionally, it is designed to function as a flexible "AI agent," skilled in planning, tool utilization, and systematic problem-solving, thereby expanding its utility beyond simple conversational exchanges. This versatility not only enhances its performance in various tasks but also makes it an invaluable resource in fields that increasingly rely on sophisticated AI-driven solutions. Its adaptability and efficiency position it as a key player in the evolving landscape of artificial intelligence applications.

What is Mixtral 8x22B?

The Mixtral 8x22B is our latest open model, setting a new standard in performance and efficiency within the realm of AI. By utilizing a sparse Mixture-of-Experts (SMoE) architecture, it activates only 39 billion parameters out of a total of 141 billion, leading to remarkable cost efficiency relative to its size. Moreover, it exhibits proficiency in several languages, such as English, French, Italian, German, and Spanish, alongside strong capabilities in mathematics and programming. Its native function calling feature, paired with the constrained output mode used on la Plateforme, greatly aids in application development and the large-scale modernization of technology infrastructures. The model boasts a context window of up to 64,000 tokens, allowing for precise information extraction from extensive documents. We are committed to designing models that optimize cost efficiency, thus providing exceptional performance-to-cost ratios compared to alternatives available in the market. As a continuation of our open model lineage, the Mixtral 8x22B's sparse activation patterns enhance its speed, making it faster than any similarly sized dense 70 billion model available. Additionally, its pioneering design and performance metrics make it an outstanding option for developers in search of high-performance AI solutions, further solidifying its position as a vital asset in the fast-evolving tech landscape.

Media

Media

Integrations Supported

AI Assistify
Airtrain
AlphaCorp
C++
Clojure
Echo AI
EvalsOne
Go
Graydient AI
HoneyHive
LLaMA-Factory
Literal AI
Overseer AI
Scout
Superinterface
TypeScript
Unify AI
WebLLM
Wordware
promptmate.io

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai/blog

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mixtral-8x22b/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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