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There is no verified basis here for claiming a first-hand test of seven document-parsing APIs, or for publishing that test’s code, provider list, settings, or scores. The available evidence does support a useful, narrower answer: one 2026 scanned-contract benchmark reports a leader on a specific downstream question-answering task, while a separate small seven-tool comparison illustrates why mixed-document tests should not be treated as a general leaderboard. To choose an API for your PDFs, compare it on your own document types and measure extraction quality, operational performance, and deployment constraints separately.

What results are actually available for scanned PDFs?

The clearest large, task-specific result in the available evidence comes from Openbenchmarks’ 2026 scanned-contract evaluation. It reports that each measured configuration received the same workload: 94 image-only contracts and 1,500 questions. The publisher rendered the contracts at 200 dpi and wrapped the resulting page images back into PDFs without a text layer, embedded fonts, or a structure tree. In that setup, Datalab Track Changes achieved 79.7% answer accuracy and led the reported table on that metric.

That 79.7% is downstream question-answering accuracy for this benchmark, not a general OCR score or a prediction of performance on invoices, receipts, handwritten forms, or other languages. Openbenchmarks also reports latency and measured parser cost separately; the available evidence does not provide their numeric values, so they cannot be compared here. The publisher says its benchmark runner and data are open, but its reported results have not been independently reproduced here.

A separate seven-tool comparison is a smoke test, not a leaderboard

A 2026 Reddit post by latentnoise_ describes a different comparison using 11 PDFs, 35 pages, and 119 API calls. Its author says the tools received the same schema and instructions, timeout, and default mode, and reports measures including field accuracy, row F1, hallucinations, missing values, and median latency. Different systems led on different measures. The post also notes manual correction of bad labels in public datasets, and commenters questioned whether 11 mixed documents could support broad conclusions.

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This small exercise can help reveal obvious failures or suggest questions for a larger evaluation. It does not establish which API is best overall. The available account does not identify enough detail here to reproduce its provider-by-provider results, so those figures should not be combined with the Openbenchmarks contract results.

Why “accuracy” needs a task attached to it

A scanned PDF without a usable text layer is fundamentally a set of page images: a parser must recognize words and infer how they relate to the page’s layout. A system can transcribe visible text well yet still scramble reading order, split a table incorrectly, or return a plausible value where the document contains none. Conversely, a parser that preserves useful structure for a downstream question-answering system may not lead on character-level OCR.

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OmniDocBench’s evaluation framing separates end-to-end and task-specific work such as layout detection, OCR, table recognition, and formula parsing across varied PDF types. Run-llama’s ParseBench project describes evaluating whether output preserves structure and meaning for agent workflows on human-verified enterprise pages, with approximately 2,000 pages from insurance, finance, and government documents. These projects offer ways to think about coverage; they do not validate the title’s implied seven-API experiment.

Match the metric to the failure that matters

  • Text fidelity: Compare recognized text against a human-checked transcription, with a defined unit such as characters or words. This detects recognition errors, not necessarily broken layout.
  • Reading order and layout: Check whether headings, paragraphs, labels, and columns are assigned to the right regions and presented in a usable sequence.
  • Tables: Score cell or row recovery against verified ground truth. Row F1, for example, evaluates recovered rows rather than ordinary text similarity; define how a row counts as a match before comparing systems.
  • Fields: Measure exact field accuracy for values that should be present, and separately count incorrect invented values, omitted values, and correctly returned nulls or abstentions.
  • Downstream tasks: If users will ask questions of parsed documents, score answers against verified answers and report that result as question-answering performance—not OCR accuracy.

Keep each result visible. A single composite score can conceal an important weakness; if you use one, publish its weights and the per-metric scores alongside it.

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How to run a comparison that answers your use case

Start with the documents and decisions your application actually handles. A benchmark made entirely of contracts will not tell you how an API handles faint receipts, multi-page invoices, or handwritten forms. Keep a held-out set of representative files and human-verified expected outputs so each API is evaluated against the same ground truth.

  1. Define the input set. Record the number of documents and pages per document category, whether each PDF is native or image-only, scan quality and resolution, languages, and any pages with rotation or unusual layout.
  2. Write down expected outputs. For each file, identify what must be recovered: exact text, reading order, table cells or rows, named fields, and values that should be absent. Have people verify the labels before scoring.
  3. Freeze the test configuration. Record provider, model and API version, region, SDK, request settings, prompts, output schema, timeout, and test date. Apply equivalent instructions and output requirements where the services allow it.
  4. Run the same workload on each service. Preserve the original PDFs, requests, raw responses, errors, and any normalized outputs. Note whether preprocessing or a text layer changed the input; do not quietly give one system a different task.
  5. Score by task and document category. Report text, layout, table, field, hallucination, missing-value, and abstention results separately where relevant. Show sample counts so a result on a small category cannot look as conclusive as one on a large set.
  6. Measure operations on the same runs. Report median and tail latency, failures and timeouts, and observed cost for the workload. Distinguish actual measured spend from published list pricing.
  7. Publish enough detail to reproduce the comparison. Provide the code, dataset or a permitted description of it, ground-truth process, scoring definitions, and per-system settings. If privacy or licensing prevents sharing documents, explain the limitation and share what can be reproduced.

A compact results layout

For each API and document category, use one row per metric rather than folding unlike outcomes together. A practical report includes sample count, text fidelity, table or field score as applicable, incorrect and missing values, latency percentiles, failures, and measured cost. Put version, region, input preparation, and request settings next to the run information so a score cannot be mistaken for a timeless property of the product.

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How do the product capability notes affect a deployment choice?

Performance evidence and product documentation answer different questions. A vendor’s statement that a feature exists does not show that it performs better than another API on your documents; likewise, a benchmark score does not establish regional availability or data-handling suitability.

Google Cloud Document AI

Google Cloud release notes say image and table annotations for its layout parser reached general availability on May 27, 2026. The notes also describe a layout-parser model powered by Gemini 3 Flash that was available in Preview in February 2026. That specific preview model uses a global endpoint and is not compliant with Data Residency standards. Because availability and processor versions can change, verify the current processor version, region, and status before designing a deployment around it.

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Adobe PDF Services

Adobe’s PDF Services documentation describes PDF Extract as able to extract text, images, and tables from native and scanned PDFs into structured JSON. It also describes table output in CSV or XLSX and image output in PNG. These are capability statements from Adobe, not independent comparative accuracy findings.

How to decide which API is best for your PDFs

Choose a service by the task and constraints that matter in production, not by a universal ranking. If the main job is extracting tables, weight verified row and cell recovery. If users need document Q&A, evaluate answer accuracy on your documents and check whether wrong or fabricated values create unacceptable risk. Then weigh latency, failures, actual workload cost, version stability, and data-residency requirements against the quality results.

The Openbenchmarks result is a useful reference point for image-only contracts and downstream questions, not a substitute for a matched test on your own files. The 11-PDF comparison is a useful example of measuring multiple dimensions, not evidence that its winners will generalize. A credible API decision comes from reporting those dimensions separately and making the test conditions reproducible.

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