Data prep bottlenecks in LLM workflows: PDF extraction tools and cross‑model considerations

PDF extraction can make or break LLM prep, and the debate is buzzing. In a lively thread, Gemini 2.5 Pro is pitched as the best, while Docling remains the accuracy king—though notably slower ^[1].

• Docling — most accurate, but slow ^[1] • Gemini 2.5 Pro — praised as blazing fast; rivals claim it outperforms others in practice ^[1] • Marker — slow but accurate ^[1] • PyMuPDF — fast and fairly accurate for many PDFs ^[1] • Tesseract — OCR option for older, non-digital documents ^[1] • MinerU — excellent for really complex layouts, slower to set up ^[1] • PDF Guru — browser-based OCR that’s easy to try ^[1]

Deployment decisions matter. Some builders skip cloud latency, booting up local GPUs and even toying with multi‑GPU setups like two RTX 6000 Blackwell Pro cards to keep prep fast and private ^[2]. The punchline: tool choice shifts not just accuracy, but workflow speed, cost, and where you run the work—local vs. cloud—into the model ground truth. Poster claims Gemini 2.5 Pro can outperform both Docling and Marker, and that it runs on directory-wide or multi‑GPU setups ^[1].

Cross‑model implications: if speed is king, many lean toward Gemini 2.5 Pro; if accuracy matters for tricky PDFs, Docling still pulls. The take‑home is simple: pick your combo based on document complexity, budget, and whether you need on‑prem or cloud runtimes.

Closing thought: speed wins when PDFs are messy but accuracy wins when data is mission‑critical—so watch how tools like Gemini 2.5 Pro and Docling perform in your stack ^[1][2].

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