Docling vs olmocr
Side-by-side comparison of two AI agent tools
Short answer
- olmocr has had no commit in 6 months; Docling is actively maintained (357 commits in the last 90 days).
- Docling is growing faster: +1,850 GitHub stars in the last 30 days vs +413 for olmocr.
- Pick Docling for: get your documents ready for gen AI. Pick olmocr for: toolkit for linearizing PDFs for LLM datasets/training.
From GitHub data refreshed daily.
Doclingopen-source
Get your documents ready for gen AI
olmocropen-source
Toolkit for linearizing PDFs for LLM datasets/training
Metrics
| Docling | olmocr | |
|---|---|---|
| Stars | 68.3k | 19.7k |
| Star velocity /mo | 1.8k | 413.3684210526315 |
| Commits (90d) | 357 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8450261353477664 | 0.3454145003701764 |
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
- +Excellent handling of complex document layouts including equations, tables, handwriting, and multi-column formats with natural reading order preservation
- +Cost-effective processing at under $200 per million pages, making it economical for large-scale dataset creation
- +Continuous model improvements with recent releases showing significant performance gains and reduced hallucinations on blank documents
Cons
- -Processing complex documents with advanced features may require significant computational resources
- -Requires GPU resources due to 7B parameter model, making it computationally intensive and potentially expensive to run
- -May require multiple retries for some documents to achieve optimal results
- -Limited to image-based document formats (PDF, PNG, JPEG) and requires technical expertise for setup and optimization
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
- •Converting academic papers and research documents with complex equations and figures for LLM training datasets
- •Processing legacy document archives with multi-column layouts and mixed content types into searchable text format
- •Creating high-quality training data from technical manuals, textbooks, and scientific publications for domain-specific language models
FAQ
- Which is more popular, Docling or olmocr?
- Docling has more GitHub stars (68,329 vs 19,687).
- Which is more actively developed, Docling or olmocr?
- Docling had more commits in the last 90 days (357 vs 0).
- Should I use Docling or olmocr?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.