When to pay for Reducto. When to run MinerU, Docling, or Marker yourself. Real comparison of cost, accuracy, latency, and privacy tradeoffs for document parsing.
Reducto is a commercial API for document parsing. It combines vision-language models with “agentic OCR” — meaning it reviews and corrects its own outputs. The alternatives are open-source tools you run yourself: MinerU, Docling, Marker, Unstructured, DocTR, PaddleOCR.
| Factor | Reducto | MinerU | Docling | Marker |
|---|---|---|---|---|
| Deployment | API | Self-hosted | Self-hosted | Self-hosted |
| Layout Detection | VLM-based | 97.5 mAP | 93.1% | Surya-based |
| Table Extraction | Excellent | Excellent | Good | Good |
| Structured Extraction | Built-in | Manual | Manual | Manual |
| GPU Required | No | Recommended | No | Yes |
| Languages | 100+ | 40+ | 20+ | 90+ |
| Cost Model | Per page | Free (AGPL) | Free (Apache) | Free (Apache) |
| Data Privacy | Cloud (SOC2) | On-premise | On-premise | On-premise |
Get the full OCR comparison spreadsheet
30+ models × 8 benchmarks, accuracy + price per page. We email it and keep it current.
This is where the decision usually gets made. Real math.
Break-even typically at 50,000-100,000 pages depending on your engineer’s hourly rate.
This matters more than benchmark numbers.
| Failure Mode | Reducto | MinerU | Docling |
|---|---|---|---|
| Complex nested tables | Handles well | Handles well | Sometimes fails |
| Handwritten annotations | Supported | Limited | Poor |
| Low-quality scans (fax, 150dpi) | Agentic retry | Degrades | Degrades |
| Multi-column layouts | Good | 97.5 mAP | Good |
| Stamps/watermarks/overprints | Variable | Variable | Variable |
| Non-Latin scripts | 100+ langs | Good CJK | Limited |
97.5 mAP on layout detection. Surpasses GPT-5.4 and Gemini 2.5 Pro on OmniDocBench with only 1.2B parameters. Best choice when accuracy matters more than speed.
from mineru import MinerU
# Local - full control, GPU recommended
miner = MinerU()
result = miner.extract('invoice.pdf')
for page in result:
for block in page.blocks:
print(f"[{block.category}] {block.text}")
if block.category == 'table':
print(block.to_markdown())License: AGPL-3.0. Commercial use requires releasing your code if distributed.
IBM’s document parser. 97.9% accuracy on complex table extraction in some evaluations. No GPU required. Apache 2.0 license — safe for commercial use.
from docling import Docling
# Local - fast, no GPU required
docling = Docling()
result = docling.parse('invoice.pdf')
for page in result.pages:
for element in page.elements:
print(f"{element.type}: {element.text}")License: Apache 2.0. Commercial-friendly.
Built on Surya (90+ languages). Converts PDFs directly to Markdown, JSON, or HTML. 122 pages/min on H100. Good balance of speed and accuracy.
from marker import convert_pdf
# Local - Surya-based, GPU accelerated
result = convert_pdf("invoice.pdf")
markdown = result.markdown
json_output = result.jsonLicense: Apache 2.0. Commercial-friendly.
If your priority is time-to-production:
Use Reducto. API is live in hours, not weeks. Built-in structured extraction saves pipeline work.
If your priority is accuracy on complex documents:
Use MinerU. 97.5 mAP is state-of-the-art. Accept the AGPL license or the setup complexity.
If your priority is cost at scale:
Use Docling or Marker. Once setup, per-page cost approaches zero. Break-even vs APIs at ~50K pages.
If your priority is data sovereignty:
Any open source option. No data leaves your infrastructure. Required for some compliance regimes.
If your priority is commercial licensing:
Use Docling or Marker (Apache 2.0). Avoid MinerU (AGPL) if you distribute software.
Reducto is a good choice when: engineering time is expensive, volume is moderate (<100K pages/month), you need structured extraction without building pipelines, or enterprise features matter.
Open source wins when: volume is high, data privacy is non-negotiable, you have GPU infrastructure, or you need to customize model behavior.
There’s no universal answer. The right choice depends on your constraints.
import reducto
# API-based - simple but requires internet
client = reducto.Client(api_key="your-key")
result = client.parse("invoice.pdf")
# Structured extraction
data = client.extract(
"invoice.pdf",
schema={"vendor": str, "total": float, "items": list}
)Get the full OCR comparison spreadsheet
30+ models × 8 benchmarks, accuracy + price per page. We email it and keep it current.