MinerU Online · No install, open and go

Make every document
machine-readable

Turn PDFs, papers, and web pages into clean Markdown, HTML, and structured JSON. Tuned for scientific layouts, chemical formulas, and complex tables — built for AI agents and RAG pipelines.

Pages parsed daily
1.2M+
Pages parsed daily
Supported formats
30+
File & web formats
Service uptime
99.9%
Service uptime

MinerU Workbench

Upload · parse · export, all in one place

Drop a file here, or click to browse

Batch upload · up to 50 MB per file · original layout preserved

Output

Tasks

3 tasks
List of parsing tasks
Task Status Type Model Created Actions
NeurIPS-2024-paper.pdf 2.4 MB · 38 pages
Completed Document MinerU XL 2026/09/17 14:24
arxiv.org/abs/2409.xxxxx Web snapshot
Completed Web MinerU HTML 2026/09/17 13:50
organic-chemistry-lab-manual.pdf 18.7 MB · 214 pages
Parsing Document MinerU XL 2026/09/17 14:58
polymer-characterization-report.docx 6.1 MB · 62 pages
Completed Document MinerU XL 2026/09/17 12:07
nature.com/articles/s41586-024-07… Web snapshot · 14 formulas
Completed Web MinerU HTML 2026/09/17 09:32

Capabilities

A parsing engine built for complex documents

Beyond text extraction. MinerU understands the structure, semantics, and hierarchy of scientific layouts.

Chemistry · math · tables

01 / SCIENCE

Scientific layout understanding

Purpose-built for chemical formulas, mathematical notation, complex tables, and multi-column layouts — preserving reading order and logical structure.

Chemical formulas LaTeX math Spanning tables
{ "type": "title", "level": 1 "bbox": [72, 96, 540], "text": "3. Methods" }, "type": "table", "html": "<table>…" }, …] Semantics · bbox · hierarchy

02 / AGENTS

Native to AI agents

Structured JSON with semantic tags for headings, paragraphs, tables, figures, and formulas — plug directly into RAG, knowledge bases, and agent workflows.

Structured JSON RAG-ready Semantic tags
Layout 0.42s Formulas 0.61s Tables 0.77s 100 pages · 1.8s total

03 / SPEED

High-speed elastic compute

Elastic cloud scheduling parses hundred-page documents in seconds, while batch jobs run in parallel to meet enterprise throughput.

Sub-second jobs Parallel batches Elastic scaling

Who uses MinerU

Built for research, engineering, and business teams

From one person's reading list to an organisation-wide knowledge base, MinerU sits at the first gate where documents enter your systems.

A scientific researcher using MinerU online to parse PDF papers and literature into structured text

Researchers

Scientific researchers

Turn papers, patents, and lab notes into searchable structured text in bulk. Formulas and chemical notation survive intact, so literature reviews stop being manual transcription.

A developer calling the MinerU document parsing API and wiring the results into an application

Developers

AI application developers

One line of code. Get semantically tagged JSON straight into your RAG pipeline, knowledge base, or agent workflow — no parsing pipeline of your own to maintain.

A team reviewing document parsing analytics and dashboards on a large meeting room screen

Enterprise

Enterprise & institutional teams

Batch-ingest research reports, contracts, and archives. Pair it with on-premise deployment and usage reporting to make document assets genuinely searchable.

Parsing in action

See what comes back from a real page

Handed the same document, a generic OCR gives you a wall of text. MinerU keeps the structure, the chemistry, and the math.

Case 01 · Chemical structures

Molecular diagram → searchable, computable chemistry

Structures are no longer thrown away as images. MinerU reads ring systems and substituents, then emits SMILES, molecular formulas, and LaTeX.

Original PDF page
Scanned PDF page showing the chemical structure of 2,4,6-trinitrophenol (picric acid)
MinerU output
# structure → structured chemistry
{
  "type": "chemical_structure",
  "smiles": "O=[N+]([O-])C1=CC([N+](=O)[O-])=C(O)C([N+](=O)[O-])=C1",
  "formula": "C6H3N3O7",
  "iupac": "2,4,6-trinitrophenol",
  "latex": "\ce{(O2N)3C6H2OH}"
}

# inline Markdown output
2,4,6-trinitrophenol (picric acid, $C_6H_3N_3O_7$, SMILES: O=[N+]([O-])C1=CC([N+](=O)[O-])=C(O)C([N+](=O)[O-])=C1)
Generic OCR The diagram is dropped as an image — all that survives is "see Fig. 2". No search, no computation.
MinerU Recovered as SMILES / formula / LaTeX — ready to index, query, and run property calculations on.
Case 02 · Math

Formulas → clean LaTeX

Subscripts, radicals, fractions and Greek letters survive intact — output compiles as-is.

Original PDF page
MinerU output
# standard LaTeX, renders as-is
\int_{0}^{\infty} e^{-x^{2}}\,dx
= \frac{\sqrt{\pi}}{2}

\frac{\pi^{2}}{6}
= \sum_{n=1}^{N} \frac{1}{n^{2}}

# human-readable text kept too
∫₀^∞ e^(−x²) dx = √π / 2
Case 03 · Complex tables

Merged headers & spanning tables → structured rows

Multi-level headers and tables that continue across pages are stitched back together, with cell coordinates intact.

Original PDF page
Table 3. Yield comparison, control vs. treatment Group Control Treatment Mean SD Mean SD Sample A 12.4 0.83 18.9 1.02 Sample B 14.1 0.91 21.3 1.14 Sample C 15.7 1.05 24.6 1.28 Sample D 17.2 1.11 26.8 1.35
MinerU output
# merged headers flattened to Markdown
| Group | Control mean | Control SD | Treatment mean | Treatment SD |
| --- | ---:| ---:| ---:| ---:|
| Sample A | 12.4 | 0.83 | 18.9 | 1.02 |
| Sample B | 14.1 | 0.91 | 21.3 | 1.14 |
| Sample C | 15.7 | 1.05 | 24.6 | 1.28 |
| Sample D | 17.2 | 1.11 | 26.8 | 1.35 |

# coordinates & merges kept in JSON
{"rows": 5, "cols": 5, "merged": [2, 2]}

API reference

Integrate in one line, call it like a local function

A RESTful API and multi-language SDKs cover upload, status, and download — with clear error codes and webhooks so integration stays painless.

  • RESTful endpoints with cURL / Python / Node.js / Go examples
  • Choose synchronous responses or async jobs
  • Structured error codes with retry guidance
  • Webhook callbacks on completion, no polling needed
Read the full API docs
Python Node.js cURL
# pip install mineru
import mineru

client = mineru.Client(api_key="your-api-key")

job = client.parse.upload(
    file=open("paper.pdf", "rb"),
    output_format="markdown",
    model="mineru-xl"
)

# Wait for the job and save the result
result = job.wait()
result.save("output.md")
print(result.pages, "pages parsed")

Price

Pick by usage, switch anytime

From individual researchers to enterprise teams. Every plan includes online parsing and result export.

Free

Sign up and start right away

$0/ month

No payment method required

  • 50 free pages every month
  • PDF, images, and web links
  • Markdown / HTML export
  • 3 concurrent tasks
  • Community support
Start free

Pro · Monthly

Billed monthly, cancel anytime

$9.9/ month

Monthly billing, cancel anytime

  • 10,000 pages every month
  • Priority access to MinerU XL
  • Structured JSON and full API access
  • Batch jobs, concurrency, and webhooks
  • Email support
Go monthly

On-premise

Your data never leaves your premises

For finance, healthcare, government, and research institutions, MinerU deploys fully on-premise: models and data run entirely inside your own network.

100% Data stays local
Compliant Security framework ready
24/7 Dedicated support
Talk to us about deployment

Customer stories

  • A top-tier securities research institute Private parsing for an internal research knowledge base — 50,000 pages a day with zero data egress.
  • A tertiary hospital research department Structured extraction from medical literature and trial reports, fully compliant with research data rules.
  • A provincial government cloud Digitized archives of official documents powering intelligent search and Q&A.
An IT administrator and a researcher managing document data in an on-premise server room
Models and data stay inside your own network, meeting compliance requirements in finance, healthcare, and government.