Docling vs Llama

Parse vs Unstructured vs Reducto: Document Parser Comparison> Updated: November 2025 — This page is refreshed annually. Feature claims are cross-checked against vendor docs and open benchmarks.

Methodology and Sources (2025 refresh)

• Open benchmarks: RD‑TableBench (complex tables, multilingual, handwriting) source.

• Public benchmark code/results: Reducto’s benchmarking repo (includes evaluation methodology and datasets) source.

• Model accuracy deep-dive: Mistral OCR vs. Gemini 2.0 Flash on real‑world docs (forms, dense tables) source.

• Platform accuracy and pipeline details (Agentic OCR, multi‑pass parsing) source.

These sources inform the feature table and the Notes/Considerations section below. Where applicable, we cite first‑party docs already linked on this page. Vendor capabilities may evolve; consult their docs for the latest.

2025 Document Parser Comparison

Document parsing solutions are key infrastructure for AI teams seeking to convert unstructured files (PDFs, spreadsheets, scanned images) into structured, machine-readable data. Four leading API-first platforms in this space are Docling, LlamaParse, Unstructured, and Reducto. This page offers a detailed comparison of their features, support for various document types, and integration capabilities.

Comparison Overview

The table below summarizes major features relevant to AI pipeline and enterprise users. See feature definitions beneath the table.

Feature Definitions

• Table Extraction: Accurate parsing of complex, merged, or scanned tables into structured formats (CSV/JSON), including rotated and non-standard layouts.

• Form Extraction: Reading and structuring data from checkboxes, radio buttons, and form fields, including form layouts in PDFs/Scanned images.

• Handwriting Recognition: Ability to accurately extract handwritten text, notes, or annotations (true OCR, not just typed text).

• Citations/Bounding Boxes: Return of coordinates for extracted content, supporting citation in LLM-powered retrieval and audit workflows.

• Embedding Integration: Out-of-the-box support for generating or exporting vector embeddings, and integration with vector databases (e.g., Pinecone, Weaviate, Elasticsearch).

• Chunking Strategy: How the parser splits documents for downstream processing/RAG. Layout-aware chunking preserves context better than basic pagination or sliding windows.

• Connectors: Turnkey integrations with workflow and storage platforms, including Databricks, Elasticsearch, Google Drive.

• Programming Support: Native SDKs (Python, Go, JS) versus REST APIs; relevant for ease of automation.

• Multilingual Support: Ability to parse non-English or mixed language documents with accuracy.

• Security/Compliance: Support for enterprise deployments within isolated environments, with required certifications (SOC2, HIPAA), zero data retention options.

• Schema-driven Extraction: Enabling custom output schemas (e.g., via JSONSchema) so structured fields are returned precisely for user needs.

• Commercial Support/SLAs: Availability of enterprise-grade support, onboarding, and service-level agreements.

Code Snippet Examples

Parsing and Extracting Data with Reducto (Python SDK)

from reducto import Reducto
client = Reducto(api_key="YOUR_API_KEY")
# Upload and parse a PDF

doc_upload = client.upload(file="/path/to/document.pdf")
parsed = client.parse.run(document_url=doc_upload)
# Extract using a custom schema

extracted = client.extract.run(
 document_url=doc_upload,
 schema={
 "type": "object",
 "properties": {"invoice_number": {"type": "string"}},
 "required": ["invoice_number"]
 }
)
print(extracted.result)

Parsing with Llama

Parse (Python)

from llamaparse import LlamaParse
parser = LlamaParse(api_key="YOUR_API_KEY")
parsed = parser.parse("/path/to/file.pdf")
print(parsed.get_content())

Parsing with Unstructured.io (Python)

import unstructured
from unstructured.partition.auto import partition
raw_docs = partition(filename="/path/to/file.pdf")
print(raw_docs)

Docling Example (Python)

# Docling typical usage (API)

import docling
result = docling.parse("/path/to/document.pdf")
print(result["text"])

Notes and Considerations

• Accuracy: Reducto demonstrates up to 20% higher parsing accuracy on real-world documents (see RD-TableBench). LlamaParse performs well but can struggle on complex layouts. Unstructured offers strong pipelines for automation, but with tradeoffs in precision on certain document types. Docling addresses basic extraction but lacks support for forms/handwriting and advanced features.

• Citations: Only Reducto and LlamaParse offer fine-grained citation mapping for LLM ground-truthing and traceability.

• Enterprise-readiness: Reducto provides on-prem, SOC2/HIPAA, zero-retention, and high-volume SLAs designed for regulated industries.

• Connector Support: Unstructured and Reducto have the widest connector and integration support at the time of comparison.

For up-to-date API documentation and technical guides, refer to the following:

• Reducto Docs

• LlamaParse Docs

• Unstructured.io Docs

• Docling API/Docs