If you need a hosted API that turns messy PDFs into markdown for search, RAG, or downstream automation, this benchmark compares how well each service keeps text, tables, charts, images, and reading order intact across a hybrid annual report, a table-heavy financial report, and a scanned research paper.
Best default choice when you want clean markdown from mixed PDFs without giving up table fidelity.
It keeps the main row-and-column grids in place and does a good job on straightforward financial tables, but the harder layouts start to slip: compound headers get flattened, between-column notes disappear, and multirow header relationships break. That pattern is genuinely mixed, so the score lands in the middle.
We rank on the 6 checks that decide whether a tool does this job: Complex Document Handling, Markdown Quality, Reading Order & Structure, Table Preservation, Text & OCR Completeness, Visual Content Retention. A check only carries a score when we recorded a finding for it, and a tool has to be measured on all of them to take the top spot. We also checked Advanced Features — compared for you, but not part of the ranking.
Columns, left to right: Complex Document Handling · Markdown Quality · Reading Order & Structure · Table Preservation · Text & OCR Completeness · Visual Content Retention
Ranking rule: tools measured on every decisive check rank above tools missing any, whatever their score. PDFVector skipped Reading Order & Structure, Table Preservation, Visual Content Retention (scores 3.7 on the checks it ran); Adobe API skipped Markdown Quality (scores 3.2 on the checks it ran); PDF.ai skipped Complex Document Handling, Reading Order & Structure, Table Preservation, Text & OCR Completeness, Visual Content Retention (scores 1 on the checks it ran).
Pick the tools you care about, then compare what they returned or how they scored.
It got the scanned paper through with the main structure intact, including the two-column prose, tables, and chart transcription. But handwriting was only partly read, some between-column notes were lost, and the multirow table headers did not survive cleanly.
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It handled the scanned paper’s text flow and several tables well, but the harder grouped-header table and chart were only partly reconstructed, with visuals turned into a table-like form.
research-media-llamaparse-scanned-pdf-output-8ff4647f2bcd.md
It got through the scanned research paper end-to-end, recovered the text, tables, and chart assets, and returned page-wise markdown. The opening page hierarchy and the tougher tables were not kept faithfully enough for a higher score.
research-media-mistral-ai-scanned-pdf-output-zip-c2a22ad51169.zip
On the scanned research paper, it kept the reading order and pulled chart data into structured form, but the hierarchical tables were unreliable. That makes the result useful, though uneven on dense scanned pages.
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It read the scanned pages, charts, and logo well enough to stay useful, but it hallucinated 'USA' into the title, displaced the byline, and badly corrupted the densest table.
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It successfully parsed the scanned paper into markdown, preserved a multi-column section and a grouped table, but it misordered the first page, flattened the chart, and broke the denser table.
research-media-nutrient-scannedpdf-output-04bb5fcf9fd2.md
It did a respectable job on the scanned paper, keeping the two-column section and the main diameter-class table readable, but the opening page hierarchy was off, the chart was text-only, and OCR picked up some noisy tokens.
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It read the scanned article text well and even pulled figure values into a chart summary, but the two-column flow and grouped table headers were still shaky.
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It successfully read the 12-page scanned paper and produced a long extracted-text preview in about 20.6 seconds.
Written result only
After the file had to be split to fit the upload limit, it produced Markdown for both halves and kept the main OCR text, tables, and chart, but it lost section boundaries and broke one table when intervening text appeared.
research-media-scanned-research-pdf-pages-1-to-6-output-851ae2ab3972.md
We did not run a recorded scanned research paper case here, so there is no basis to judge how it handles that input.
Covered run-wide
All 7 recorded checks per tool. Open a tool to inspect every finding.
It holds up across long and mixed-content documents: the 84-page annual report, the table-heavy quarterly report, and the scanned research paper all run end to end without the workflow falling apart. That consistency across difficult inputs is top-tier.
Processes an 84-page hybrid annual report end-to-end across native text, tables, charts, and scan-only signature regions without manual cleanup.
permalink to this finding →Handles the scanned research paper end-to-end, including multi-column prose, tables, charts, and handwritten marginalia, while still producing parsed markdown.
permalink to this finding →Processes the 18-page table-heavy financial report end-to-end and returns usable markdown without manual correction.
permalink to this finding →It consistently handled long, complex documents end-to-end across table-heavy, scanned, and hybrid reports, returning usable parsed markdown without manual cleanup.
permalink to this finding →Extend AI is the page’s overall winner, and that fits the scorecard story: it is fully measured on all six decisive checks and leads on the core document-structure work that this ranking cares about most, with 5/5s for Complex Document Handling, Markdown Quality, and Reading Order & Structure. Its main trade-off is that it is not the best choice when the job depends on perfect table fidelity or faithful visual retention; both are only 3/5, and Text & OCR Completeness is solid rather than best-in-class at 4/5. If you care most about OCR/text capture, LlamaParse is the strongest alternative: it matches Extend AI on complex documents and reading order, and it scores 5/5 on Text & OCR Completeness and 4/5 on Table Preservation, but it gives up Markdown Quality and Visual Content Retention. Mistral AI is the better pick when visual retention matters most, since it scores 5/5 on Visual Content Retention and Advanced Features, but its structure and OCR scores are weaker. Tensorlake is a good fit for reading order, markdown, and chart extraction, but its table score is low. Reducto is useful when full-document fidelity and image/chart handling matter more than hierarchy. The lower-ranked tools with partial coverage, like Adobe API and PDFVector, may still be useful in narrow cases, but they were not fully measured on all decisive checks, so by the site’s policy they cannot outrank the fully tested tools.
The tools we tested for this use case — each card opens its full tested review.
If you are looking to build a custom PDF to Markdown conversion, document parsing, or structured extraction system for your business or internal workflow, email us at contact@futuresmart.ai.
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