{"id":58665,"date":"2025-01-30T22:39:22","date_gmt":"2025-01-30T22:39:22","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/what-is-mistral-small-3-all-you-need-to-know\/"},"modified":"2025-01-30T22:39:22","modified_gmt":"2025-01-30T22:39:22","slug":"what-is-mistral-small-3-all-you-need-to-know","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=58665","title":{"rendered":"What is Mistral Small 3? All You Need to Know"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>Mistral AI has launched its latest and most efficient small language model (SLM) \u2013 Mistral Small 3. It\u2019s a 24-billion-parameter language model designed for high efficiency and low latency. The model aims to deliver robust performance across various AI tasks while maintaining rapid response times. Here\u2019s all you need to know about Mistral Small 3 \u2013 its features, applications, how to access it, and how it compares with Qwen2.5, Llama-3.3, and more.<\/p>\n<figure class=\"wp-block-embed is-type-rich is-provider-twitter wp-block-embed-twitter\">\n<div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"twitter-tweet\" data-width=\"500\" data-dnt=\"true\">\n<p lang=\"en\" dir=\"ltr\">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 so great base for anything reasoning \u2013 happy building!<a href=\"https:\/\/t.co\/8WfgzuuwRk\">https:\/\/t.co\/8WfgzuuwRk<\/a><\/p>\n<p>\u2014 Mistral AI (@MistralAI) <a href=\"https:\/\/twitter.com\/MistralAI\/status\/1884968836606136636?ref_src=twsrc%5Etfw\">January 30, 2025<\/a><\/p><\/blockquote>\n<\/div>\n<\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-mistral-small-3\">What is Mistral Small 3?<\/h2>\n<p>Mistral Small 3 is a latency-optimized language model that balances performance and efficiency. Despite its 24B parameter size, it competes with larger models like Llama 3.3 70B Instruct and Qwen2.5 32B Instruct, offering comparable capabilities with significantly reduced computational demands.<\/p>\n<p>Small 3, launched as a base model, allows developers train it further, using reinforcement learning or reinforcement fine tuning. It features a 32,000 tokens context window and generates responses at 150 tokens per second processing speed. This design makes it suitable for applications requiring swift and accurate language processing.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-key-features-of-mistral-small-3\">Key Features of Mistral Small 3<\/h2>\n<ul class=\"wp-block-list\">\n<li><strong>Multilingual: <\/strong>The model supports multiple languages including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.<\/li>\n<li><strong>Agent-Centric: <\/strong>It offers best-in-class agentic capabilities with native function calling and JSON outputting.<\/li>\n<li><strong>Advanced Reasoning: <\/strong>The model features state-of-the-art conversational and reasoning capabilities.<\/li>\n<li><strong>Apache 2.0 License: <\/strong>Its open license allows developers and organizations, use and modify the model, for both commercial and non-commercial purposes.<\/li>\n<li><strong>System Prompt:<\/strong> It maintains a strong adherence and great support for system prompts.<\/li>\n<li><strong>Tokenizer:<\/strong> It utilizes a Tekken tokenizer with a 131k vocabulary size.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-mistral-small-3-vs-other-models-performance-benchmarks\">Mistral Small 3 vs Other Models: Performance Benchmarks<\/h2>\n<p>Mistral Small 3 has been evaluated across several key benchmarks to assess its performance in various domains. Let\u2019s see how this new model has performed against gpt-4o-mini, Llama 3.3 70B Instruct, Qwen2.5 32B Instruct, and Gemma 2 27b.<\/p>\n<p><em>Also Read: <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/01\/phi-4-vs-gpt-4o-mini\/\" target=\"_blank\" rel=\"noreferrer noopener\">Phi 4 vs GPT 4o-mini: Which is Better?<\/a><\/em><\/p>\n<p><strong>1. Massive Multitask Language Understanding (MMLU) Pro (5-shot)<\/strong><\/p>\n<p>The MMLU benchmark evaluates a model\u2019s proficiency across a wide range of subjects, including humanities, sciences, and mathematics, at an undergraduate level. In the 5-shot setting, where the model is provided with five examples before being tested, Mistral Small 3 achieved an accuracy exceeding 81%. This performance is notable, especially considering that Mistral 7B Instruct, an earlier model, scored 60.1% in a similar 5-shot scenario.<\/p>\n<p><strong>2. General Purpose Question Answering (GPQA) Main<\/strong><\/p>\n<p>GPQA assesses a model\u2019s ability to answer a broad spectrum of questions that require general world knowledge and reasoning. Mistral Small 3 outperformed Qwen2.5-32B-Instruct, gpt-4o-mini, and Gemma-2 in GPQA, proving its strong capability in handling diverse question-answering tasks.<\/p>\n<p><strong>3. HumanEval<\/strong><\/p>\n<p>The HumanEval benchmark measures a model\u2019s coding abilities by requiring it to generate correct code solutions for a given set of programming problems. Mistral Small 3\u2019s performance in this test is almost as good as Llama-3.3-70B-Instruct.<\/p>\n<p><strong>4. Math Instruct<\/strong><\/p>\n<p>Math Instruct evaluates a model\u2019s proficiency in solving mathematical problems and following mathematical instructions. Despite it\u2019s small size and design, Mistral Small 3 shows promising results in this test as well.<\/p>\n<p>Mistral Small 3 demonstrated performance on par with larger models such as Llama 3.3 70B instruct, while being more than three times faster on the same hardware. It outperformed most models, particularly in language understanding and reasoning tasks. These results show Mistral Small 3 to be a competitive model in the landscape of AI language models.<\/p>\n<p><em>Also Read: <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2025\/01\/qwen2-5-vl-vision-model\/\" target=\"_blank\" rel=\"noreferrer noopener\">Qwen2.5-VL Vision Model: Features, Applications, and More<\/a><\/em><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-applications-of-mistral-small-3\">Applications of Mistral Small 3<\/h2>\n<p>Mistral Small 3 is versatile and well-suited for various applications, such as:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Fast-Response Conversational Assistance:<\/strong> Ideal for virtual assistants and chatbots where quick, accurate responses are essential.<\/li>\n<li><strong>Low-Latency Function Calling:<\/strong> Efficient in automated workflows requiring rapid function execution.<\/li>\n<li><strong>Domain-Specific Fine-Tuning:<\/strong> Can be customized for specialized fields like legal advice, medical diagnostics, and technical support, enhancing accuracy in these domains.<\/li>\n<li><strong>Local Inference:<\/strong> When quantized, it can run on devices like a single RTX 4090 or a MacBook with 32GB RAM, benefiting users handling sensitive or proprietary information.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-real-life-use-cases-of-mistral-small-3\">Real-life Use Cases of Mistral Small 3<\/h2>\n<p>Here are some real-life use cases of Mistral Small 3 across industries:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>Fraud Detection in Financial Services: <\/strong>Banks and financial institutions can use Mistral Small 3 to detect fraudulent transactions. The model can analyze patterns in transaction data and flag suspicious activities in real time.<\/li>\n<li><strong>AI-Driven Patient Triage in Healthcare: <\/strong>Hospitals and telemedicine platforms can leverage the model for automated patient triaging. The model can assess symptoms from patient inputs and direct them to appropriate departments or care units.<\/li>\n<li><strong>On-Device Command and Control for Robotics &amp; Automotive: <\/strong>Manufacturers can deploy Mistral Small 3 for real-time voice commands and automation in robotics, self-driving cars, and industrial machines.<\/li>\n<li><strong>Virtual Customer Service Assistants: <\/strong>Businesses across industries can integrate the model into chatbots and virtual agents to provide instant, context-aware responses to customer queries. This can significantly reduce wait times.<\/li>\n<li><strong>Sentiment and Feedback Analysis: <\/strong>Companies can use Mistral Small 3 to analyze customer reviews, social media posts, and survey responses, extracting key insights on user sentiment and brand perception.<\/li>\n<li><strong>Automated Quality Control in Manufacturing: <\/strong>The model can assist in real-time monitoring of production lines. It can analyse logs, detect anomalies, and predict potential equipment failures to prevent downtime.<\/li>\n<\/ol>\n<h2 class=\"wp-block-heading\" id=\"h-how-to-access-mistral-small-3\">How to Access Mistral Small 3?<\/h2>\n<p>Mistral Small 3 is available under the Apache 2.0 license, allowing developers to integrate and customize the model within their applications. As per official reports, the model can be downloaded from Mistral AI\u2019s official website or accessed through the following platforms:<\/p>\n<p>Here\u2019s how you can access and utilize the Mistral-Small-24B model on Kaggle:<\/p>\n<p>First install Kagglehub.<\/p>\n<pre class=\"wp-block-code\"><code>pip install kagglehub<\/code><\/pre>\n<p>Then put in this code to get started.<\/p>\n<pre class=\"wp-block-code\"><code>from transformers import AutoModelForCausalLM, AutoTokenizer\nimport kagglehub\nmodel_name = kagglehub.model_download(\"mistral-ai\/mistral-small-24b\/transformers\/mistral-small-24b-base-2501\")\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_name,\n    torch_dtype=\"auto\",\n    device_map=\"auto\"\n)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nprompt = \"Give me a short introduction to Mistral- AI company\"\n# Tokenize the input\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n\n# Generate text\ngeneration_output = model.generate(**inputs, \n                                   max_new_tokens=100,  \n                                   temperature=0.7,     # Controls randomness (higher = more random)\n                                   top_p=0.9,           # Nucleus sampling (higher = more diverse)\n                                   do_sample=True)      # Enables sampling\n\n# Decode the generated output\ngenerated_text = tokenizer.decode(generation_output[0], skip_special_tokens=True)\n\nprint(\"Generated Text (Base Model):\")\nprint(generated_text)\n<\/code><\/pre>\n<p>You can integrate the Small 3 model into your existing applications using Together AI\u2019s OpenAI-compatible APIs. Additionally, Mistral AI offers deployment options via <a href=\"https:\/\/console.mistral.ai\/\" target=\"_blank\" rel=\"nofollow noopener\">La Plateforme<\/a>, providing market-leading availability, speed, and quality control.<\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"872\" height=\"467\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/together-ai-small-3.webp\" alt=\"Mistral Small 3 on together.ai\" class=\"wp-image-218635\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/together-ai-small-3.webp 872w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/together-ai-small-3-300x161.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/together-ai-small-3-768x411.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/together-ai-small-3-150x80.webp 150w\" sizes=\"auto, (max-width: 872px) 100vw, 872px\"\/><\/figure>\n<p>Mistral AI also has plans of launching it soon on NVIDIA NIM, Amazon SageMaker, Groq, Databricks and Snowflake.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-hands-on-testing-of-mistral-small-3\">Hands-on Testing of Mistral Small 3<\/h2>\n<p>Now, let\u2019s try out the capabilities of this new Mistral model. For this, we\u2019ll try out 3 different prompts on Mistral Small 3 on the together Playground and see what responses we get.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-task-1-coding\">Task 1: Coding<\/h3>\n<p><strong>Prompt: <\/strong><em>\u201cGiven a dataset containing customer transactions with columns \u2018CustomerID\u2019, \u2018ProductID\u2019, \u2018PurchaseAmount\u2019, and \u2018PurchaseDate\u2019, write a Python function to:<br \/>1. Calculate the total spend for each customer.<br \/>2. Find the customer who spent the most.<br \/>3. Determine the most popular product based on the total sales amount.<br \/>You can assume that the data is a Pandas DataFrame.\u201d<\/em><\/p>\n<p><strong>Output:<\/strong><\/p>\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\ndef analyze_customer_transactions(df):\n    # Calculate the total spend for each customer\n    total_spend_per_customer = df.groupby('CustomerID')['PurchaseAmount'].sum().reset_index()\n    total_spend_per_customer.columns = ['CustomerID', 'TotalSpend']\n\n    # Find the customer who spent the most\n    max_spender = total_spend_per_customer.loc[total_spend_per_customer['TotalSpend'].idxmax()]\n    max_spender_customer_id = max_spender['CustomerID']\n    max_spender_amount = max_spender['TotalSpend']\n\n    # Determine the most popular product based on the total sales amount\n    total_sales_per_product = df.groupby('ProductID')['PurchaseAmount'].sum().reset_index()\n    total_sales_per_product.columns = ['ProductID', 'TotalSales']\n    most_popular_product = total_sales_per_product.loc[total_sales_per_product['TotalSales'].idxmax()]\n    most_popular_product_id = most_popular_product['ProductID']\n    most_popular_product_sales = most_popular_product['TotalSales']\n\n    return total_spend_per_customer, max_spender_customer_id, max_spender_amount, most_popular_product_id, most_popular_product_sales\n\n# Example usage:\n# df = pd.DataFrame({\n#     'CustomerID': [1, 2, 1, 3, 2, 1],\n#     'ProductID': [101, 102, 101, 103, 102, 101],\n#     'PurchaseAmount': [100, 150, 200, 50, 250, 300],\n#     'PurchaseDate': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06']\n# })\n\n# total<\/code><\/pre>\n<h3 class=\"wp-block-heading\" id=\"h-task-2-mathematical-reasoning\">Task 2: Mathematical Reasoning<\/h3>\n<p><strong>Prompt: <\/strong><em>\u201cSolve this problem step by step: A tank can be filled by two pipes in 10 minutes and 15 minutes, respectively. If both pipes are opened together, how long will it take to fill the tank?\u201d<\/em><br \/><strong>Output:<\/strong><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"728\" height=\"697\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-math.webp\" alt=\"Mathematical reasoning\" class=\"wp-image-218666\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-math.webp 728w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-math-300x287.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-math-150x144.webp 150w\" sizes=\"auto, (max-width: 728px) 100vw, 728px\"\/><\/figure>\n<h3 class=\"wp-block-heading\" id=\"h-task-3-sentiment-analysis\">Task 3: Sentiment Analysis<\/h3>\n<p><strong>Prompt: <\/strong><em>\u201cAnalyze the sentiment of the following customer review:<br \/>\u2018I was really excited about this product, but it broke within two days. Customer service was unhelpful, and I regret my purchase.&#8217;\u201d<\/em><br \/><strong>Output:<\/strong><\/p>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"872\" height=\"375\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-sentiment-analysis-1.webp\" alt=\"mistral sentiment analysis\" class=\"wp-image-218669\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-sentiment-analysis-1.webp 872w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-sentiment-analysis-1-300x129.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-sentiment-analysis-1-768x330.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/01\/mistral-sentiment-analysis-1-150x65.webp 150w\" sizes=\"auto, (max-width: 872px) 100vw, 872px\"\/><\/figure>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>Mistral Small 3 represents a significant advancement in AI model development, offering a blend of efficiency, speed, and performance. Its size and latency makes it suitable for deployment on devices with limited computational resources, such as a single RTX 4090 GPU or a MacBook with 32GB RAM. Moreover, its open-source availability under the Apache 2.0 license encourages widespread adoption and customization. On the whole, Mistral Small 3, seems to be a valuable tool for developers and organizations aiming to implement high-performance AI solutions with reduced computational overhead.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1738263318434\"><strong class=\"schema-faq-question\">Q1. What is Mistral Small 3?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Mistral Small 3 is a 24-billion-parameter language model optimized for low-latency, high-efficiency AI tasks.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1738263329828\"><strong class=\"schema-faq-question\">Q2. How does Mistral Small 3 compare to other models?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Mistral Small 3 competes with larger models like Llama 3.3 70B Instruct and Qwen2.5 32B Instruct, offering similar performance but with significantly lower computational requirements.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1738263338329\"><strong class=\"schema-faq-question\">Q3. How can I access and use Mistral Small 3?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. You can access Mistral Small 3 through:<br \/>\u2013 Mistral AI\u2019s official website (for downloading the model).<br \/>\u2013 Platforms like Hugging Face, Together AI, Ollama, Kaggle, and Fireworks AI (for cloud-based usage).<br \/>\u2013 La Plateforme by Mistral AI for enterprise-grade deployment.<br \/>\u2013 APIs from Together AI and other providers for seamless integration.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1738263370653\"><strong class=\"schema-faq-question\">Q4. What are the key features of Mistral Small 3?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Here are the key features of Mistral Small 3:<br \/>\u2013 32,000-token context window for handling long conversations.<br \/>\u2013 150 tokens per second processing speed.<br \/>\u2013 Multilingual support (English, French, Spanish, German, Chinese, etc.).<br \/>\u2013 Function calling and JSON output support for structured AI applications.<br \/>\u2013 Optimized for low-latency inference on consumer GPUs.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1738263394265\"><strong class=\"schema-faq-question\">Q5. What are the real-life use cases of Mistral Small 3?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Here are some real-life use cases of Mistral Small 3:<br \/>\u2013 Fraud detection in financial services.<br \/>\u2013 AI-driven patient triage in healthcare.<br \/>\u2013 On-device command and control in robotics, automotive, and manufacturing.<br \/>\u2013 Virtual customer service assistants for businesses.<br \/>\u2013 Sentiment and feedback analysis for brand reputation monitoring.<br \/>\u2013 Automated quality control in industrial applications.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1738263426161\"><strong class=\"schema-faq-question\">Q6. Can I fine-tune Mistral Small 3 for specific applications?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Yes, Small 3 can be fine-tuned using reinforcement learning or reinforcement fine-tuning to adapt it for specific industries or tasks. It is released under the Apache 2.0 license, allowing free usage, modification, and commercial applications without major restrictions.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/sabreena\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_GIbx21k.webp\" width=\"48\" height=\"48\" alt=\"K.C. Sabreena Basheer\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Sabreena Basheer is an architect-turned-writer who&#8217;s passionate about documenting anything that interests her. She&#8217;s currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><script async src=\"\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><br \/>\n<br \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mistral AI has launched its latest and most efficient small language model (SLM) \u2013 Mistral Small 3. It\u2019s a 24-billion-parameter language model designed for high efficiency and low latency. The model aims to deliver robust performance across various AI tasks while maintaining rapid response times. Here\u2019s all you need to know about Mistral Small 3 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":58666,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[33005,709],"dealstore":[],"offerexpiration":[],"class_list":["post-58665","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-mistral","tag-small"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is Mistral Small 3? 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