python-docxvsUnstructured

How 10 AI models weigh python-docx against Unstructured on the 2 buying questions where both came up.

python-docx
29%of answers named it
Unstructured
24%of answers named it
Placed higher
22–18times python-docx vs Unstructured, over 40 model rankings that listed either
Models leaning
6–3toward python-docx vs Unstructured; 1 even

Which models prefer which

Share of each model's answers that named each product, and how often its ranking put one above the other (a product listed beats one left off) · On the 2 shared questions, every wording

Modelpython-docxUnstructuredPlaced higherLeans
Claude Haiku 5.531%19%2–1python-docx
GPT-6 Luna0%38%0–2Unstructured
Gemini 4 Argon0%50%0–4Unstructured
Qwen 3.8 Flash31%6%1–1Even
DeepSeek V4 Pro63%38%5–3python-docx
GLM-5.331%25%1–2Unstructured
Kimi K356%31%5–2python-docx
MiniMax M325%6%3–1python-docx
Mistral Large 413%13%2–1python-docx
Nemotron 3 Ultra38%13%3–1python-docx

What the models say about python-docx

One reason per model, from the shared questions

  • Claude Haiku 5.5Simple, popular Python library for creating and editing Word documents.
  • Qwen 3.8 FlashSibling library often paired for full Office document automation.
  • DeepSeek V4 ProBest Python library for .docx reading and writing, simple API.
  • GLM-5.3Well-documented Python library for Excel and Word automation.
  • Kimi K3Simple, dependable Word document creation and editing for agents.
  • MiniMax M3Clean Python API for generating and editing Word documents programmatically.
  • Mistral Large 4Simple, widely adopted Python library for Word documents without Office dependencies.
  • Nemotron 3 UltraPythonic library for creating/updating Word .docx files programmatically

What the models say about Unstructured

One reason per model, from the shared questions

  • Claude Haiku 5.5Purpose-built document parsing for LLM agents across PDFs and Office formats
  • GPT-6 LunaStrong document ingestion and parsing for extracting structured content for AI.
  • Gemini 4 ArgonIndustry standard for ingesting, chunking, and preprocessing Office and PDF files for agent workflows.
  • DeepSeek V4 ProPurpose-built for agentic document parsing with chunking, OCR, and clean markdown output.
  • GLM-5.3Broadest format coverage and mature pipelines for RAG and agent ingestion.
  • Kimi K3Purpose-built ingestion SDK; best Office/PDF parsing with element metadata.
  • MiniMax M3Broad format coverage and solid ETL pipeline integration
  • Mistral Large 4Best-in-class parsing for PDFs, Office docs, and messy files into LLM-ready data.
  • Nemotron 3 UltraBest for extracting text/tables from any PowerPoint

Tracked since October 25, 2026. Each time the panel answers these questions again, both shares are added here.

Question by question

Share of all answers that named each, every wording pooled, with its place among every product named · Both-named questions first