Mistral Small 3 | How to Access, Features, Performance, and More

Mistral AI's latest small language model (SLM), Mistral Small 3, delivers impressive performance and efficiency. This 24-billion parameter model boasts rapid response times and robust capabilities across diverse AI tasks. Let's explore its features, applications, accessibility, and benchmark comparisons.

Introducing Small 3, our most efficient and versatile model yet! Pre-trained and instructed version, Apache 2.0, 24B, 81% MMLU, 150 tok/s. No synthetic data, making it ideal for reasoning tasks. Happy building!

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Table of Contents

• What is Mistral Small 3?
• Key Features
• Performance Benchmarks
• Accessing Mistral Small 3
• Hands-on Testing
  • Coding
  • Mathematical Reasoning
  • Sentiment Analysis
• Applications
• Real-world Use Cases
• Frequently Asked Questions

What is Mistral Small 3?

Mistral Small 3 prioritizes low latency without sacrificing performance. Its 24B parameters rival larger models like Llama 3.3 70B Instruct and Qwen2.5 32B Instruct, offering comparable functionality with significantly reduced computational needs. Released as a base model, developers can further train it using reinforcement learning or fine-tuning. Its 32,000-token context window and 150 tokens-per-second processing speed make it ideal for applications demanding speed and accuracy.

Key Features

• Multilingual support (English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, Polish)
• Agent-centric capabilities with function calling and JSON output
• Advanced reasoning and conversational skills
• Apache 2.0 license for flexible commercial and non-commercial use
• Strong system prompt support
• Tekken tokenizer with a 131k vocabulary

Performance Benchmarks

Mistral Small 3 excels in various benchmarks, often outperforming larger models in specific areas while maintaining superior speed. Comparisons against gpt-4o-mini, Llama 3.3 70B Instruct, Qwen2.5 32B Instruct, and Gemma 2 27b highlight its strengths.

See also: Phi 4 vs GPT 4o-mini Comparison

1. Massive Multitask Language Understanding (MMLU): Mistral Small 3 achieved over 81% accuracy, demonstrating strong performance across diverse subjects.

2. General Purpose Question Answering (GPQA) Main: It outperformed competitors in answering diverse questions, showcasing robust reasoning abilities.

3. HumanEval: Its coding proficiency is comparable to Llama-3.3-70B-Instruct.

4. Math Instruct: Mistral Small 3 shows promising results in mathematical problem-solving.

Mistral Small 3's speed advantage (more than three times faster than Llama 3.3 70B Instruct on similar hardware) underscores its efficiency.

See also: Qwen2.5-VL Vision Model Overview

Accessing Mistral Small 3

Mistral Small 3 is available under the Apache 2.0 license via Mistral AI's website, Hugging Face, Ollama, Kaggle, Together AI, and Fireworks AI. The Kaggle example below illustrates its integration:

pip install kagglehub
from transformers import AutoModelForCausalLM, AutoTokenizer
import kagglehub
model_name = kagglehub.model_download("mistral-ai/mistral-small-24b/transformers/mistral-small-24b-base-2501")
# ... (rest of the code as provided in the original text)

Together AI offers OpenAI-compatible APIs, and Mistral AI provides deployment options via La Plateforme. Future availability is planned on NVIDIA NIM, Amazon SageMaker, Groq, Databricks, and Snowflake.

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