Amazon TextractvsUnstructured
How 10 AI models weigh Amazon Textract against Unstructured on the 1 buying question where both came up.
- Amazon Textract
- 18%of answers named it
- Unstructured
- 44%of answers named it
- Placed higher
- 2–17times Amazon Textract vs Unstructured, over 19 model rankings that listed either
- Models leaning
- 0–7toward Amazon Textract vs Unstructured; 3 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
| Model | Amazon Textract | Unstructured | Placed higher | Leans |
|---|---|---|---|---|
| Claude Haiku 5.5 | 25% | 38% | 0–2 | Unstructured |
| GPT-6 Luna | 25% | 75% | 0–2 | Unstructured |
| Gemini 4 Argon | 25% | 75% | 0–3 | Unstructured |
| Qwen 3.8 Flash | 0% | 13% | 0–1 | Unstructured |
| DeepSeek V4 Pro | 0% | 75% | 0–3 | Unstructured |
| GLM-5.3 | 38% | 50% | 1–1 | Even |
| Kimi K3 | 13% | 63% | 0–3 | Unstructured |
| MiniMax M3 | 13% | 13% | 0–1 | Unstructured |
| Mistral Large 4 | 38% | 25% | 1–1 | Even |
| Nemotron 3 Ultra | 0% | 13% | 0–0 | Even |
What the models say about Amazon Textract
One reason per model, from the shared questions
- Claude Haiku 5.5
Reliable OCR and table and form extraction, scales well inside AWS workflows.
- GPT-6 Luna
Battle-tested form and table extraction, especially compelling for AWS-centric agent workflows.
- Gemini 4 Argon
Battle-tested scalability for high-volume extraction of structured forms, tables, and handwriting.
- GLM-5.3
Reliable, cheap at volume, seamless inside AWS agent stacks.
- Mistral Large 4
AWS-native extraction for forms, tables, and handwriting with tight AWS integration.
What the models say about Unstructured
One reason per model, from the shared questions
- Claude Haiku 5.5
Purpose-built document parsing for LLM agents across PDFs and Office formats
- GPT-6 Luna
Strong document ingestion and parsing for extracting structured content for AI.
- Gemini 4 Argon
Industry standard for ingesting, chunking, and preprocessing Office and PDF files for agent workflows.
- DeepSeek V4 Pro
Purpose-built for agentic document parsing with chunking, OCR, and clean markdown output.
- GLM-5.3
Broadest format coverage and mature pipelines for RAG and agent ingestion.
- Kimi K3
Purpose-built ingestion SDK; best Office/PDF parsing with element metadata.
- MiniMax M3
Broad format coverage and solid ETL pipeline integration
- Mistral Large 4
Best-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
| Question | Amazon Textract | Unstructured |
|---|---|---|
| Best SDKs for AI agents that handle Office and PDF files | 18% #14 | 44% #1 |
| Best Python libraries for PowerPoint files | · | 4% #33 |