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ease
features
design
support

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Description

Mistral 3 represents the newest iteration of open-weight AI models developed by Mistral AI, encompassing a diverse range of models that span from compact, edge-optimized versions to a leading large-scale multimodal model. This lineup features three efficient “Ministral 3” models with 3 billion, 8 billion, and 14 billion parameters, tailored for deployment on devices with limited resources, such as laptops, drones, or other edge devices. Additionally, there is the robust “Mistral Large 3,” which is a sparse mixture-of-experts model boasting a staggering 675 billion total parameters, with 41 billion of them being active. These models are designed to handle multimodal and multilingual tasks, excelling not only in text processing but also in image comprehension, and they have showcased exceptional performance on general queries, multilingual dialogues, and multimodal inputs. Furthermore, both the base and instruction-fine-tuned versions are made available under the Apache 2.0 license, allowing for extensive customization and integration into various enterprise and open-source initiatives. This flexibility in licensing encourages innovation and collaboration among developers and organizations alike.

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
NexaSDK
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Oxlo.ai
Python
Qwen Code
Qwen Studio
QwenCloud

Integrations

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

Pricing Details

Free
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

Mistral AI

Founded

2023

Country

France

Website

mistral.ai/news/mistral-3

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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