Who makes Magistral Small 1.0?

Magistral Small 1.0 is developed by Mistral AI.

What can Magistral Small 1.0 do?

Magistral Small 1.0 specializes in text generation, summarization, translation, conversation.

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Magistral Small 1.0

Name: Magistral Small 1.0
Author: Mistral AI

by Mistral AI

AI Model

Low Confidence

Magistral Small 1.0 is a compact, efficient large language model (LLM) developed by Mistral AI, designed for deployment in resource-constrained environments while maintaining strong performance on natural language processing tasks. It is part of Mistral AI's suite of open-source and proprietary models, optimized for applications requiring lower computational overhead without significant trade-offs in accuracy or capability. Magistral Small 1.0 is suitable for tasks such as text generation, summarization, translation, and conversational AI, particularly in edge computing or on-premise deployments.

Transparency Score

60%

Moderate transparency

Supply Chain

20/20

Compliance

10/20

Policy

10/25

Technical

15/25

Ethical & Operational

5/10

Capabilities

Text Generation

Summarization

Translation

Conversation

Vendor Information

Complete information about the vendor/provider of this AI application

Company Name

Mistral AIView all products →

Legal Entity

Mistral AI

Contact Information

Website

mistral.ai/

support@mistral.ai

Registered Address

15 rue des Halles, Paris, 75001, France

EU AI Act Provider Information

Verification Status:

Unverified

Legal Entity

Mistral AI

Registered Address

15 rue des Halles, Paris, 75001, France

Contact

support@mistral.ai

EU Authorized Representative

Mistral AI

15 rue des Halles, Paris, France

Compliance Documents

CE Marking: Not Applicable

Supply Chain Network

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Subprocessors

Third-party vendors and subprocessors used by this vendor

Twilio

subprocessor

Phone number verification

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Google, LLC

subprocessor

Cloud Infrastructure

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Policies & Documents

Legal, privacy, and compliance documentation

Legal & Terms

Acceptable Use Policy

Compliance & Risk

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Data Categories

Types of data commonly processed by this application

Pii

User Content

Conversation Logs

Code Snippets

Known Limitations

Mistral models have a finite context window (e.g., 32k tokens for some versions). This means they may struggle with very long documents or conversations, potentially losing track of earlier details. While strong at many tasks, the models can make logical errors or oversimplify nuanced reasoning, especially in highly technical or abstract domains. Mistral models are trained on data up to a specific cutoff date (e.g., November 2024 for some versions). They may not have real-time or post-cutoff knowledge unless fine-tuned or augmented with external tools. While multilingual, performance is generally stronger in high-resource languages (e.g., English, French) compared to low-resource languages.

Magistral Small 1.0

Legal & Terms

EU Alternatives

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Privacy & Security

Compliance

Other