OpenAI SDKvsUnstructured

How 10 AI models weigh OpenAI SDK against Unstructured on the 1 buying question where both came up.

OpenAI SDK
1%of answers named it
Unstructured
44%of answers named it
Placed higher
1–18times OpenAI SDK vs Unstructured, over 19 model rankings that listed either
Models leaning
1–9toward OpenAI SDK vs Unstructured

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

ModelOpenAI SDKUnstructuredPlaced higherLeans
Claude Haiku 5.50%38%0–2Unstructured
GPT-6 Luna0%75%0–2Unstructured
Gemini 4 Argon0%75%0–3Unstructured
Qwen 3.8 Flash0%13%0–1Unstructured
DeepSeek V4 Pro0%75%0–3Unstructured
GLM-5.30%50%0–2Unstructured
Kimi K30%63%0–3Unstructured
MiniMax M30%13%0–1Unstructured
Mistral Large 40%25%0–1Unstructured
Nemotron 3 Ultra13%13%1–0OpenAI SDK

What the models say about OpenAI SDK

One reason per model, from the shared questions

No reasons given.

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.

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