Mistral Small 3

Name: Mistral Small 3 Introduction Video
Uploaded: 2025-04-26T07:47:04Z
Duration: 1 min 33 s
Description: Efficient, open-source AI model rivaling larger competitors with lower resource requirements.

About Mistral Small 3

Launched Feb 05, 2025

Introduction Video

Description

Efficient, open-source AI model rivaling larger competitors with lower resource requirements.

Mistral Small 3 is a 24B-parameter language model released under the Apache 2.0 license. It offers performance comparable to larger models like Llama 3.3 70B while being more than 3x faster on the same hardware. Designed for local deployment, it excels in tasks requiring robust language and instruction following with very low latency. The model can be quantized to run on a single RTX 4090 or a Macbook with 32GB RAM

Mistral Small 3 Key Features

24B parameters
Apache 2.0 license
Low latency (150 tokens/s)
81% accuracy on MMLU
32k context window
Multilingual support
Function calling capabilities
Optimized for quantization

Mistral Small 3 Use Cases

Fast-response conversational assistance
Low-latency function calling
Fine-tuning for subject matter experts
Local inference for sensitive data
Fraud detection in financial services
Customer triaging in healthcare
On-device command and control in robotics and manufacturing
Virtual customer service
Sentiment and feedback analysis

Pros

Efficient performance with 24B parameters, rivaling larger models like Llama 3.3 70B.
Open-source availability under the Apache 2.0 license, encouraging community use and development.
More than three times faster than larger models on the same hardware, enhancing user experience.
Designed for local deployment, ensuring privacy and control over data.
Excellent language and instruction following capabilities with low latency.
Quantization capability allows it to run on consumer-grade hardware like an RTX 4090 or a Macbook with 32GB RAM.

Cons

Potentially less support and community resources compared to more well-known models.
May not be as robust in handling highly complex tasks as larger models in every scenario.
Documentation and best practices for deployment may need further development for easier accessibility.