Mistral: Advanced AI Language Model with 671B Parameters
Experience the next generation of language models with groundbreaking efficiency in reasoning, coding, and mathematical computation
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Key Features
Discover the powerful capabilities that make Mistral stand out
Advanced MoE Architecture
Revolutionary 671B parameter model with only 37B activated per token, achieving optimal efficiency through innovative load balancing
- •Multi-head Latent Attention (MLA)
- •Auxiliary-loss-free load balancing
- •MistralMoE architecture
- •Multi-token prediction objective
State-of-the-Art Performance
Exceptional results across multiple benchmarks including MMLU (87.1%), BBH (87.5%), and mathematical reasoning tasks
- •Top scores in coding competitions
- •Advanced mathematical computation
- •Multilingual capabilities
- •Complex reasoning tasks
Efficient Training
Groundbreaking training approach requiring only 2.788M H800 GPU hours, with remarkable cost efficiency of $5.5M
- •FP8 mixed precision training
- •Optimized training framework
- •Stable training process
- •No rollbacks required
Versatile Deployment
Multiple deployment options supporting NVIDIA, AMD GPUs and Huawei Ascend NPUs for flexible integration
- •Cloud deployment ready
- •Local inference support
- •Multiple hardware platforms
- •Optimized serving options
Advanced Coding Capabilities
Superior performance in programming tasks, excelling in both competitive coding and real-world development scenarios
- •Multi-language support
- •Code completion
- •Bug detection
- •Code optimization
Enterprise-Ready Security
Comprehensive security measures and compliance features for enterprise deployment and integration
- •Access control
- •Data encryption
- •Audit logging
- •Compliance ready
Extensive Training Data
Pre-trained on 14.8T diverse and high-quality tokens, ensuring broad knowledge and capabilities
- •Diverse data sources
- •Quality-filtered content
- •Multiple domains
- •Regular updates
Innovation Leadership
Pioneering advancements in AI technology through open collaboration and continuous innovation
- •Research leadership
- •Open collaboration
- •Community driven
- •Regular improvements
Mistral in the Media
Breaking new ground in open-source AI development
Breakthrough Performance
Mistral outperforms both open and closed AI models in coding competitions, particularly excelling in Codeforces contests and Aider Polyglot tests.
Massive Scale
Built with 671 billion parameters and trained on 14.8 trillion tokens, making it 1.6 times larger than Meta's Llama 3.1 405B.
Cost-Effective Development
Trained in just two months using Nvidia H800 GPUs, with a remarkably efficient development cost of $5.5 million.
Mistral in Action
Watch how Mistral revolutionizes open-source AI capabilities
Mistral: Revolutionary Open Source AI
An in-depth look at Mistral's capabilities and performance compared to other leading AI models.
Mistral Performance Metrics
Mistral Language Understanding
Mistral Coding
Mistral Mathematics
Technical Specifications
Explore the advanced technical capabilities and architecture that power Mistral
Mistral Architecture Details
Advanced neural architecture designed for optimal performance and efficiency
Mistral Research
Advancing the boundaries of language model capabilities
Novel Architecture
Innovative Mixture-of-Experts (MoE) architecture with auxiliary-loss-free load balancing strategy
Training Methodology
Advanced FP8 mixed precision training framework validated on large-scale model training
Technical Paper
Read our comprehensive technical paper detailing the architecture, training process, and evaluation results of Mistral.
Read the PaperAbout Mistral
Pioneering the future of open-source AI development
Company Background
Backed by High-Flyer Capital Management, Mistral aims to achieve breakthrough advances in AI technology through open collaboration and innovation.
Infrastructure
Utilizing advanced computing clusters including 10,000 Nvidia A100 GPUs, Mistral demonstrates exceptional capabilities in large-scale model training.
Download Mistral Models
Choose between the base and chat-tuned versions of Mistral
Mistral Base Model
The foundation model with 671B parameters (37B activated)
- •Pre-trained on 14.8T tokens
- •128K context length
- •FP8 weights
- •671B total parameters
Mistral Chat Model
Fine-tuned model optimized for dialogue and interaction
- •Enhanced reasoning
- •128K context length
- •Improved instruction following
- •671B total parameters
Installation Instructions
Download using Git LFS (recommended method):
# For Base Model
git lfs install
git clone https://huggingface.co/Mistral-ai/Mistral-V3-Base
# For Chat Model
git lfs install
git clone https://huggingface.co/Mistral-ai/Mistral-V3Mistral Deployment Options
Mistral Local Deployment
Run locally with Mistral-Infer Demo supporting FP8 and BF16 inference
- Simple setup
- Lightweight demo
- Multiple precision options
Mistral Cloud Integration
Deploy on cloud platforms with SGLang and LMDeploy support
- Cloud-native deployment
- Scalable infrastructure
- Enterprise-ready
Mistral Hardware Support
Compatible with NVIDIA, AMD GPUs and Huawei Ascend NPUs
- Multi-vendor support
- Optimized performance
- Flexible deployment
How to Use Mistral
Start chatting with Mistral in three simple steps
Visit Chat Page
Click the "Try Chat" button at the top of the page to enter the chat interface
Enter Your Question
Type your question in the chat input box
Wait for Response
Mistral will quickly generate a response, usually within seconds
FAQ
Learn more about Mistral
What makes Mistral unique?
Mistral features a 671B parameter MoE architecture, incorporating innovations like multi-token prediction and auxiliary-free load balancing, delivering exceptional performance across various tasks.
How can I access Mistral?
You can access Mistral through our online demo platform and API service, or download the model weights for local deployment.
In which tasks does Mistral excel?
Mistral excels in mathematics, programming, reasoning, and multilingual tasks, consistently achieving top scores in benchmark evaluations.
What are the hardware requirements for running Mistral?
Mistral supports various deployment options, including NVIDIA GPUs, AMD GPUs, and Huawei Ascend NPUs, with multiple framework choices for optimal performance.
Is Mistral available for commercial use?
Yes, Mistral is available for commercial use. Please refer to the model license agreement for specific terms of use.
How does Mistral compare to other language models?
Mistral outperforms other open-source models in various benchmarks and achieves performance comparable to leading closed-source models.
Which deployment frameworks does Mistral support?
Mistral can be deployed using various frameworks including SGLang, LMDeploy, TensorRT-LLM, vLLM, and supports FP8 and BF16 inference modes.
What is the context window size of Mistral?
Mistral has a 128K context window, enabling effective processing and understanding of complex tasks and long-form content.
Get Started with Mistral
Try Mistral API
Access Mistral's capabilities through our developer-friendly API platform
Start BuildingTry Mistral Chat
Experience Mistral's capabilities directly through our interactive chat interface
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