Handles many input formats (PDF, images, DOCX, PPTX, XLSX, HTML, EPUB) and outputs Markdown, JSON, HTML, or chunks sized for RAG
Preserves structure: tables, equations as LaTeX, code blocks, reading order, and image extraction, with headers/footers removed
Selective OCR keeps it fast by only invoking the vision model on bad or scanned pages, with a CPU-only no-OCR path available
Optional --use_llm hybrid mode works with Gemini, Claude, OpenAI-compatible, Azure, Vertex, OpenRouter, or local Ollama models
Apache-2.0 code with CLI, Python API, batch processing, and a simple API server
Verifiable citations reduce hallucination risk
Over 10,000 citation styles
Imports from Zotero and Mendeley
Free tier with 20 daily AI queries
Collaborative libraries on Team plan
Cons
Model weights are not freely commercial: a modified OpenRAIL-M license caps free use at under $5M funding/revenue and requires paid licensing above that
Best accuracy needs a GPU plus a local inference server (vLLM via Docker, or llama.cpp on CPU/Apple Silicon), adding setup
Very complex layouts with nested tables and forms may still convert poorly
The built-in API server is explicitly basic and not intended for large-scale production use
Advanced models require paid plans
No dedicated mobile app noted
Overkill for casual note-takers
Free tier limited to standard models
No public API
Comparison generated from each tool's listing. Add or remove tools above to change it.
