datalab-to/omni_parse_bench

Omni Parse Bench

Choosing a document parser usually starts with a leaderboard: one number per parser. But nobody parses an average document, and the parser that wins on average can be the one that fails on yours. Omni Parse Bench is built to answer the question you actually have: which parser is best on documents like mine? It is:

• clear: every test is a yes/no question about one page (is this content in the output, in this order, in a table cell under these headings, in a layout block of this kind?), every score is the share of tests passed, and every score comes with the number of tests and documents behind it, so a thin slice says it is thin;
• auditable: every test records how its answer was established, and the scorer is open code with no model judging the outputs; and
• explorable: every test is tagged by what its content is (a table cell, handwriting, a rotated page, Thai), so any slice of the benchmark gets its own score.

It has 16,288 tests on 2,937 pages from 2,343 documents over 22 suites, in 92 languages. Omni Parse Bench is developed by Datalab.

Read more in our blog post. Also see the GitHub for the scorer and the harness that runs parsers on the benchmark.

The design

The headline is the mean of three family scores: text, tables and layout. A family is a group of test types:

test type	checks	family	tests
present	this text, number or equation appears	text	7,275
order	text A comes before text B	text	1,189
repeat	this line appears exactly N times	text	35
table_cell	this cell sits under these headings and beside these neighbors	tables	5,386
layout_kind	the block holding this line is a heading, text or table	layout	2,403

A test checks one or more args, each of which is the content at one place on the page. Each arg is tagged:

• structure: table, math, form and multi_column;
• rendering: handwriting, tiny, rotated and degraded;
• role: heading, caption, footnote, list, code, figure and header_footer;
• script: ISO 15924 codes (Latn, Hani, Deva, ...); and
• language: ISO 639-3 codes (eng, fra, tha, ...).

A test's tags are the union of its args' tags. Tags never decide whether a test passes or what the headline is; they select tests, so any slice of the benchmark can be scored. A test with several tags counts in each, so per-tag scores overlap and don't average to the headline.

Each tag was decided from the page alone: computed where it can be (a script from Unicode, a font size, a rotation we made), from a source where one exists (publisher markup, form widgets), else by two models shown the content boxed on the page, with a third settling their splits by fixed rules; whatever they couldn't settle was decided by a person, who also checked a random sample of 30 tagged pieces of content and found no wrong tag.

Data

Results

parser	weights	headline	text	tables	layout
Datalab accurate	closed	92.9	91.2	92.0	95.6
Gemini 3.8 Flash*	closed	92.9	91.3	95.2	92.2
Datalab balanced	closed	91.6	90.0	89.2	95.5
Reducto	closed	90.8	85.4	91.2	95.8
LlamaParse	closed	90.4	88.2	92.6	90.4
chandra-ocr-2	open	90.1	87.4	87.4	95.4
Mistral OCR	closed	89.5	82.8	91.9	93.8
Claude Sonnet 5.5*	closed	88.7	93.1	95.4	77.5
dots.mocr	open	87.9	82.5	88.6	92.5
GPT-5.6 Sol	closed	87.8	85.4	91.6	86.5
MinerU2.5-Pro	open	87.1	77.4	92.1	91.7
Infinity-Parser2-Pro	open	87.0	83.3	84.1	93.5
Extend	closed	83.3	64.1	92.5	93.4
Surya OCR 2	open	83.1	80.6	74.3	94.3
Azure	closed	83.0	74.6	81.7	92.8
LightOnOCR-3-4B	open	82.9	83.9	70.8	94.0
LightOnOCR-3-0.8B	open	81.6	81.1	72.1	91.7
GLM-OCR	open	81.5	71.0	84.2	89.3
Unlimited-OCR	open	79.3	70.2	76.5	91.1
PaddleOCR-VL-1.6	open	78.7	61.7	83.7	90.7
Infinity-Parser2-Flash	open	78.4	78.4	66.5	90.4
DeepSeek-OCR-2	open	75.2	64.2	70.0	91.4
Indra OCR	open	69.5	60.3	58.1	90.1
HunyuanOCR-1.5	open	64.6	50.5	61.9	81.4
olmOCR-2	open	none	71.3	56.6	unsupported
Nanonets-OCR2-3B	open	none	68.2	62.6	unsupported
Tesseract	open	none	17.6	0.0	unsupported

* Used in creating and verifying at least some tests.

Open-weight models are served on their own host (a GPU, or CPU only for Indra OCR) from omni-parse-bench's server images, with the defaults each vendor recommends; each adapter's docstring lists where it differs.

The headline is the mean of three family scores: text (present, order and repeat tests), tables (table_cell) and layout (layout_kind). Tesseract, olmOCR-2 and Nanonets-OCR2-3B return no layout, so they have no headline.

Every verdict behind these numbers is in results.parquet: one row per parser and test, with the test's page, document, suite, type, derivation and tags, its result (pass, fail, or unsupported when the parser can't produce that output) and why it failed. Browse it as the results subset in the viewer above, or query it; for instance, the share of handwriting tests each parser passes:

select system, round(100 * avg((result = 'pass')::int), 1) as score, count(*) as tests
from 'hf://datasets/datalab-to/omni_parse_bench/results.parquet'
where result in ('pass', 'fail') and list_contains(tags, 'handwriting')
group by system order by score desc;

Layout

manifest.parquet        one row per page
pages/<page_id>.pdf     the page that gets sent: a one-page PDF, or a .png for an image page
gold/<page_id>.json     its tests: {"tests": [...]}
results.parquet         every parser's verdict on every test (Results above)
licenses/               the license, and the credit it requires, of each source
assets/                 the charts on this card

column
page_id	unique; the files carry it
document	the document the page comes from; scores count documents as well as tests
suite	which part of the benchmark the page came from
doc_path	pages/<page_id>.pdf (or .png), relative to the dataset root
gt_path	gold/<page_id>.json, relative to the dataset root
outputs	the output types its tests read (html, blocks): what to predict for the page

A test says what structure of the output it checks (test_type), in which output (output_type), on what content (args), with what else it needs (settings); how its answer was established (derivation); and what its content is (tags):

{
    "id": "p11_PMC12397559.1_t0_head_17_2",
    "test_type": "table_cell",
    "output_type": "html",
    "derivation": "publisher_markup",
    "tags": ["Latn", "eng", "multi_column", "table"],
    "args": {
        "cell": {"text": "32 (40.0)", "max_diffs": 0},
        "headings": [{"text": "No. (%)", "max_diffs": 0}]
    },
    "settings": {}
}

An arg is {"text", "max_diffs"}, {"number"} or {"latex"}, and its kind says how it is compared: text within max_diffs edits, a number digit for digit, LaTeX by what it renders (or by its plain text, when plain text writes it without loss).

Install

uv pip install omni-parse-bench                 # the scorer: score(), no network, no API keys
uv pip install 'omni-parse-bench[harness]'      # + vendor adapters, to predict
uv pip install 'omni-parse-bench[benchmark]'    # + dataset access, to run the benchmark

This revision of the dataset needs omni-parse-bench 0.1.2 or newer. With 0.1.1, use the revision made for it: --dataset hf://datasets/datalab-to/omni_parse_bench@1c35a52.

Run the benchmark

With [benchmark] installed, run parsers on the dataset (5 pages here). It shows the plan and asks before calling any vendor, since calls cost money; each vendor needs its API key in the environment. A run is resumable: stop it, and run the same command again to carry on.

opb benchmark --out runs/ --limit 5 --providers datalab openai/gpt-5.6-sol

Each setting of a provider is its own run; here, two:

opb benchmark --out runs/ --limit 5 --providers datalab   --options '{"datalab": [{"mode": "balanced"}, {"mode": "accurate"}]}'

--dataset picks the dataset: a folder, or hf://datasets/datalab-to/omni_parse_bench@<revision> (this one, at main, by default). Corrections land on main as new commits; pin a revision to reproduce a result. Each run's scores.jsonl has one line per test, with what the test is beside its verdict, so a run can be compared by tag, suite or test type: see docs/API.md.

Score

Use in your own code: one page with three tests, written inline so the gold file's shape is visible (gold.read("gold/<page_id>.json") reads a file).

from omni_parse_bench import gold, score, summarize

tests = gold.parse("example-p1", {"tests": [
    {
        "id": "t1",
        "test_type": "present",
        "output_type": "html",
        "derivation": "text_layer",
        "tags": ["Latn", "eng"],
        "args": {"content": {"text": "Quarterly revenue rose 12%", "max_diffs": 1}},
        "settings": {},
    },
    {
        "id": "t2",
        "test_type": "order",
        "output_type": "html",
        "derivation": "text_layer",
        "tags": ["Latn", "eng", "heading"],
        "args": {
            "before": {"text": "Summary", "max_diffs": 0},
            "after": {"text": "Outlook", "max_diffs": 0},
        },
        "settings": {},
    },
    {
        "id": "t3",
        "test_type": "table_cell",
        "output_type": "html",
        "derivation": "publisher_markup",
        "tags": ["table"],
        "args": {
            "cell": {"text": "4.2", "max_diffs": 0},
            "top_heading": {"text": "Q3", "max_diffs": 0},
        },
        "settings": {},
    },
]})

outputs = {
    "html": (
        "<h2>Summary</h2>"
        "<p>Quarterly revenue rose 12% on the year.</p>"
        "<table>"
        "<tr><th>Q2</th><th>Q3</th></tr>"
        "<tr><td>3.9</td><td>4.1</td></tr>"
        "</table>"
        "<h2>Outlook</h2>"
    ),
}
verdicts = score(outputs, tests)

>>> verdicts
[Verdict(id='t1', result='pass', why=''),
 Verdict(id='t2', result='pass', why=''),
 Verdict(id='t3', result='fail', why="no cell matching '4.2' in any table")]

>>> s = summarize(verdicts, tests)["html"]
>>> s["score"], s["per_test_type"], s["per_tag"]["table"]
(0.6666666666666666, {'present': 1.0, 'order': 1.0, 'table_cell': 0.0}, {'score': 0.0, 'tests': 1, 'pages': 1})

A verdict is pass, fail, unsupported (the parser can't produce that output) or error (grading raised: a bug in the scorer, not the parser's fault), with why saying what went wrong. unsupported and error verdicts are left out of every score.

From the command line, with the outputs in a JSON file:

opb score --pred outputs.json --gold gold/<page_id>.json

Predict

With [harness] installed, predict with our provider adapters:

from omni_parse_bench import harness

record = harness.predict("datalab", "pages/<page_id>.pdf", {"html", "blocks"}, mode="balanced")
record["outputs"]       # {"html": "...", "blocks": [...]}
record["errors"]        # {output: why} for anything that failed
record["calls"]         # what was sent, cost, wall time

score(record["outputs"], gold.read("gold/<page_id>.json"))

From the command line:

opb predict --provider openai/gpt-5.6-sol --page pages/<page_id>.pdf --wants html
opb providers        # every provider, and its options

How the tests were built

What a test is made of, where each part of it comes from (its derivation says which source gave its answer), and how tags are decided: docs/building-tests.md and docs/tagging.md in the repository.

License

Each source's license, and the credit it requires, is a file in licenses/; each PubMed Central article's credit is a row of licenses/pmc_articles.csv.

The pages are public documents, pages of public datasets, and copies of them we filled in or rebuilt. A dataset's license covers what its makers could license; the documents it collected keep their owners' rights, and are credited here to their source. Pages whose own license forbids redistribution or commercial use (a PubMed Central article under CC BY-NC, for one) are left out. Two sources ask for non-commercial use and are kept, as fair use, with our reasons in their files: the Ohio Department of Transportation drawings (ohio-dot-LICENSE.txt), and the three Copiste d'un jour pages, whose writers consented to non-commercial use (htr-united-LICENSE.txt).

The tests, and what we added to pages (form fills, handwriting), are Datalab's, under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode). Tests converted from olmOCR-bench and fr-bench-pdf2md keep their licenses (ODC-BY 1.0 and MIT), and the tests on the three Copiste d'un jour pages, which quote Wikipedia, are CC BY-SA 4.0 (htr-united-LICENSE.txt).

If you hold rights in a page and want it removed, open an issue at https://github.com/datalab-to/omni_parse_bench/issues.

The scorer and harness are Apache 2.0.

suites	pages	license file
olmo_arxiv_math, olmo_long_tiny_text, olmo_multi_column, olmo_old_scans, olmo_old_scans_math, olmo_table_tests	974	olmocr-bench-LICENSE.txt
tables_derived	528	pmc-LICENSE.txt, with each article's credit in pmc_articles.csv
docparsing_images	71	docparsingbench-LICENSE.txt
parsebench_docs	81	parsebench-LICENSE.txt
realdoc_pdfs, realdoc_pages	210	realdoc-bench-LICENSE.txt
multiling_finepdfs	158	finepdfs-LICENSE.txt
rotation_skew	142	the file of the page it is a rotated copy of: pmc-LICENSE.txt, parsebench-LICENSE.txt or realdoc-bench-LICENSE.txt
forms_filled	34	us-federal-forms-LICENSE.txt and cerfa-LICENSE.txt
forms_federal	31	us-federal-forms-LICENSE.txt
multilingual	42	cerfa-LICENSE.txt and nta-LICENSE.txt
frbench_handwriting	29	fr-bench-pdf2md-LICENSE.txt and cerfa-LICENSE.txt
forms_documentcloud	17	documentcloud-LICENSE.txt
large_format, tiny_plans	87	ohio-dot-LICENSE.txt
handwriting_samples	39	loc-catt-papers-LICENSE.txt, nls-marjory-fleming-LICENSE.txt and htr-united-LICENSE.txt
synth_copies	494	the file of the page it is rebuilt from: realdoc-bench-LICENSE.txt, parsebench-LICENSE.txt, docparsingbench-LICENSE.txt, olmocr-bench-LICENSE.txt or finepdfs-LICENSE.txt

Citation

@misc{omniparsebench2026,
  title        = {Omni Parse Bench: A Document-Parsing Benchmark},
  author       = {{Datalab}},
  year         = {2026},
  howpublished = {\url{https://github.com/datalab-to/omni_parse_bench}},
}

Please also cite the upstream datasets whose pages or tests this benchmark uses:

• olmOCR-bench (Ai2): Poznanski J. et al. (2025). olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models;
• ParseBench (LlamaIndex): Zhang B. et al. (2026). ParseBench: A Document Parsing Benchmark for AI Agents;
• DocParsingBench (SoMark): SoMark (2026). DocParsingBench. https://huggingface.co/datasets/SoMarkAI/DocParsingBench;
• fr-bench-pdf2md (pulsia): pulsia (2026). Benchmarking Vision-Language Models for French PDF-to-Markdown Conversion;
• RealDoc-Bench and RealDoc-Bench-Layout (Extend AI): https://huggingface.co/datasets/Extend-AI/RealDoc-Bench; and
• FinePDFs (Hugging Face): https://huggingface.co/datasets/HuggingFaceFW/finepdfs.