MoE vs Dense Models for Structured Data Extraction — Who Wins?

2026-04-27 · Source: Andrej Baranovskij · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

A comparative analysis of Mixture of Experts (MoE) and dense large language models (LLMs) for structured data extraction from large tables reveals that dense models significantly outperform MoE architectures for this specific task. Tests were conducted on a Mac mini M4 Pro with 64GB RAM using MLX VLM, comparing Qwen 3.6 MoE (3 billion active parameters) and Gemma 4 MoE (4 billion active parameters) against Gemma 4 dense (31 billion parameters) and Qwen 3.6 dense (27 billion parameters, 8-bit quantization). Both MoE models failed to extract data completely or correctly. The Gemma 4 dense model extracted most records but truncated some text and missed one empty-valued record. The Qwen 3.6 dense model provided the most accurate and complete extraction, including previously missed records and full text, despite having fewer parameters than Gemma 4 dense.

Key takeaway

For AI Engineers and Machine Learning Engineers tasked with structured data extraction from large tables, you should favor dense LLM architectures over Mixture of Experts models. Specifically, consider Qwen 3.6 dense (27B parameters) as it demonstrated superior accuracy and completeness compared to Gemma 4 dense and both MoE variants in local tests, even with fewer parameters than some alternatives. This choice can significantly improve data integrity and reduce post-processing efforts.

Key insights

Dense LLMs are superior to MoE models for extracting large volumes of structured data from tables.

Principles

• Task suitability dictates model architecture choice.
• More parameters do not always mean better performance.

Method

Compare MoE and dense LLMs on structured data extraction from large tables, evaluating completeness and accuracy of output using specific models like Qwen 3.6 and Gemma 4.

In practice

• Prioritize dense LLMs for large-scale table data extraction.
• Consider Qwen 3.6 dense for high-accuracy structured data tasks.

Topics

• Mixture-of-Experts
• Dense Models
• Structured Data Extraction
• Gemma 4
• Qwen 3.6

Best for: Machine Learning Engineer, AI Engineer

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