gptpdf vs docling
PDF and document parsing projects compared
gptpdf converts PDF pages into Markdown using a vision-capable model, while docling processes a wider range of document formats into structured content and exports. The main difference is gptpdf’s focused, model-API-based PDF approach versus docling’s broader toolkit, which can also run locally.
gptpdf: Convert PDFs into Markdown with Vision Models
gptpdf turns PDF pages into Markdown using a vision-capable model, aiming to preserve layouts such as tables, formulas, and figures. It suits developers who need an API-based PDF parsing component and can provide access to a compatible model.
docling: Parse Documents for AI Workflows
Docling converts documents from formats such as PDF, Office files, images, and audio into structured representations and exports. It suits developers building document-processing and generative AI pipelines that need format coverage, OCR, or local execution.
| gptpdf | docling | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 3.6k | 68.3k |
| Forks | 261 | 5k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- gptpdf focuses on PDFs; docling handles PDFs, Office files, images, audio, video, email, EPUB, and HTML.
- gptpdf sends page content for interpretation through an OpenAI-compatible multimodal model API; docling provides multiple parsing pipelines, including OCR and visual language model workflows.
- gptpdf outputs Markdown and paths to extracted images; docling offers a unified document representation and exports including Markdown, HTML, and lossless JSON.
- gptpdf depends on compatible model API access and may incur per-page costs; docling can run locally, including in air-gapped environments.
- Both are Python libraries under the MIT license, while docling also provides a CLI, MCP server, and API server.
- docling has 68.3k stars and 5k forks, compared with gptpdf’s 3.6k stars and 261 forks.
Choose gptpdf if you…
- need a focused PDF-to-Markdown component that uses a vision-capable model to interpret page content.
- want to configure the model API, prompts, and worker threads for PDF parsing.
- can provide compatible model API access and accept potential per-page costs.
Choose docling if you…
- need to process varied formats such as PDFs, Office files, images, and audio through one tool.
- want OCR, structured document representations, or multiple export formats for AI workflows.
- need local processing or interfaces such as a CLI, Python API, MCP server, or API server.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.