Adobe PDF Extractvspython-docx

How 10 AI models weigh Adobe PDF Extract against python-docx on the 1 buying question where both came up.

Adobe PDF Extract
3%of answers named it
python-docx
36%of answers named it
Placed higher
1–13times Adobe PDF Extract vs python-docx, over 14 model rankings that listed either
Models leaning
1–7toward Adobe PDF Extract vs python-docx; 2 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 1 shared question, every wording

ModelAdobe PDF Extractpython-docxPlaced higherLeans
Claude Haiku 5.50%63%0–2python-docx
GPT-6 Luna13%0%1–0Adobe PDF Extract
Gemini 4 Argon0%0%0–0Even
Qwen 3.8 Flash0%38%0–0Even
DeepSeek V4.1 Flash13%25%0–1python-docx
GLM-5.30%63%0–1python-docx
Kimi K30%63%0–3python-docx
MiniMax M30%38%0–2python-docx
Mistral Large 40%25%0–2python-docx
Nemotron 3.5 Super VL0%50%0–2python-docx

What the models say about Adobe PDF Extract

One reason per model, from the shared questions

  • GPT-6 LunaPreserves PDF structure and reading order well for workflows demanding faithful document understanding.

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.
  • DeepSeek V4.1 FlashFree and simple for editing DOCX content, though limited on layout and rendering.
  • 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 3.5 Super VLPython library for creating and updating Microsoft Word documents

Over time

Share of answers that named each, on the questions where both came up, each time the panel answered

Panel runAdobe PDF Extractpython-docx
October 26, 20263%36%
October 25, 20260%40%

Question by question

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