Docling vs Dolphin
Side-by-side comparison of two AI agent tools
Doclingopen-source
Get your documents ready for gen AI
Dolphinfree
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
Metrics
| Docling | Dolphin | |
|---|---|---|
| Stars | 68.2k | 9.1k |
| Star velocity /mo | 1.9k | 28.71657754010695 |
| Commits (90d) | 357 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.9057410894755452 | 0.31913455879016117 |
Pros
- +Advanced PDF understanding with layout analysis, table structure recognition, and reading order detection
- +Supports wide variety of document formats including office documents, images, audio, and markup languages
- +Unified DoclingDocument representation simplifies integration with AI workflows and downstream processing
- +Universal document parsing capability that handles both digital and photographed documents seamlessly
- +Advanced two-stage architecture with document-type-aware parsing strategies optimized for different document formats
- +Comprehensive 21-element detection including complex elements like formulas, code blocks, and tables with attribute field extraction
Cons
- -Processing complex documents with advanced features may require significant computational resources
- -Limited information available about performance benchmarks and processing speed for large document batches
- -Research-focused tool that may require significant technical expertise to implement and integrate
- -Relatively new release with limited production use cases and community feedback
- -Large model size (3B parameters) may require substantial computational resources for deployment
Use Cases
- •Converting research papers and technical documents into AI-ready formats for RAG applications
- •Extracting structured data from business documents like invoices, contracts, and reports for automation
- •Preparing diverse document collections for training or fine-tuning language models
- •Academic research document digitization and content extraction from PDFs and scanned papers
- •Enterprise document processing for complex reports, invoices, and forms with mixed content types
- •Automated parsing of technical documentation containing code snippets, mathematical formulas, and diagrams