New Ministral 3 14B vs Mistral Small 3.2 24B Review

2025-12-15 · Source: Andrej Baranovskij · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

A comparison between the newly released Ministral 3 (14B parameters) and the older Mistral Small 3.2 (24B parameters) models reveals performance differences in local document processing. Both models, licensed under Apache, were tested on a Mac Mini M4 with 64GB memory using Olama. The Ministral 3 (14B) model was run with 8-bit quantization (Q8), while the Mistral Small 3.2 (24B) model used 4-bit quantization (Q4). Tests on a complex financial statement and a sparse bank statement document showed that the 24B parameter Mistral Small 3.2 consistently outperformed the 14B parameter Ministral 3, which exhibited errors and missing data in its extractions. Despite similar memory utilization (around 90%), the larger parameter count of Mistral Small 3.2 proved more effective for intricate document structures.

Key takeaway

For AI Engineers and ML practitioners deploying local LLMs for document processing, if you are working with complex financial or sparse table documents, you should prioritize using Mistral Small 3.2 (24B parameters) over the newer Ministral 3 (14B parameters). The 24B model, even with Q4 quantization, demonstrates superior accuracy and completeness in data extraction, making it a more reliable choice for critical applications on local hardware like the Mac Mini M4.

Key insights

Larger parameter models often perform better on complex document parsing, even with higher quantization.

Principles

• Model parameter count correlates with performance on complex tasks.
• Quantization level impacts model accuracy and resource usage.

Method

Compare LLM performance on complex document types (financial statements, sparse tables) using different quantization levels on local hardware.

In practice

• Prioritize Mistral Small 3.2 for complex document parsing.
• Consider Q4 quantization for 24B models to optimize memory.
• Use Olama for local LLM deployment and testing.

Topics

• Mistral Models
• Large Language Models
• Document Processing
• Model Quantization
• Local Inference

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• Gemma 4 12B vs Ministral 14B: Who Wins at Structured Table Extraction? — Mistral 14B significantly outperforms Gemma 4 12B in structured table extraction requiring JSON array output.
• Comparing Qwen3-VL AI Models for OCR Task — Quantized larger Qwen3-VL models can outperform smaller, unquantized versions in both speed and accuracy for complex OCR.
• Mistral AI Releases Mistral Small 4: A 119B-Parameter MoE Model that Unifies Instruct, Reasoning, and Multimodal Workloads — Mistral Small 4 unifies diverse AI workloads into a single, efficient MoE model.
• How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions — Smaller LoRA-fine-tuned models can achieve near 8B performance for structured extraction, significantly reducing deployment costs.
• Unlocking Document Understanding with Mistral Document AI in Microsoft Foundry — Mistral Document AI 2512 converts complex unstructured documents into structured data with high accuracy and multilingual support.
• Mistral Small 4: A Good Alternative to Qwen3.5 122B and Nemotron 3 Super? — Mistral Small 4 unifies instruct and reasoning, while NVIDIA's new Nemotron models offer high performance with efficient KV cache.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Andrej Baranovskij.