Gemma 4 12B vs Ministral 14B: Who Wins at Structured Table Extraction?

2026-06-10 · Source: Andrej Baranovskij · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

A comparative test evaluated Gemma 4 12B and Mistral 14B models for structured table extraction, specifically tasked with fetching "instrument name" and "relation" as a JSON array from a five-record sample table. Gemma 4 12B failed to produce the requested array, returning only one record with incorrect description text at 8-bit quantization, and a single correct record at BF16 quantization. In contrast, Mistral 14B, utilizing 8-bit quantization, successfully extracted all five records as a correctly formatted JSON array, including accurate descriptions and valuations like 23,643. This demonstrates Mistral 14B's superior performance for this specific data extraction task, despite being only 2 billion parameters larger than Gemma 4 12B.

Key takeaway

For AI Engineers selecting smaller LLMs for structured table extraction, prioritize Mistral 14B over Gemma 4 12B. Your projects requiring reliable JSON array output from tabular data will likely see better results with Mistral, even when using 8-bit quantization. This comparison suggests Mistral 14B offers superior instruction following for complex data formatting tasks.

Key insights

Mistral 14B significantly outperforms Gemma 4 12B in structured table extraction requiring JSON array output.

Principles

• Model performance varies by task, not just size.
• Quantization impacts LLM output quality.
• Specific instruction following is critical for structured data.

Method

LLMs were tested for structured table extraction by querying for "instrument name" and "relation" from a five-record table, requesting JSON array output, and evaluating completeness and accuracy.

In practice

• Evaluate Mistral 14B for JSON array extraction.
• Test 8-bit vs. BF16 quantization for specific tasks.
• Verify LLM instruction following for structured data.

Topics

• Gemma 4 12B
• Mistral 14B
• Structured Table Extraction
• LLM Performance
• Quantization
• JSON Output

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

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