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Llamaparse

Reliable PDF-to-Markdown conversion for hybrid reports, with strong hierarchy and table capture but weaker preservation of complex table semantics and embedded visuals.

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Hybrid PDFsScanned OCRTables to MarkdownAPI export

Good end-to-end markdown conversion, but not a perfect visual-preservation parser.

Across hybrid, financial, and scanned PDFs, Llamaparse reliably produced downloadable markdown and kept page-level reading order intact. It handled tables, charts, signatures, and scanned pages better than a basic text extractor, but complex grouped headers lost some semantic clarity and the table of contents flattened into sequential text. It is strongest when you want a hosted PDF-to-markdown pipeline, not when you need every visual element preserved as an image or every nested header relationship fully explicit.

In-Depth Review

Our detailed analysis of Llamaparse — features, performance, and real-world testing.

AD
AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Document Parsing to Markdown
Accepted all three complex PDFs and returned markdown exports without manual cleanup.
▾
Test Summary
Feature tested: Document Parsing to Markdown
Result: Partial — Accepted all three complex PDFs and returned markdown exports without manual cleanup.

Feature tested: Document Parsing to Markdown

Result: Partial

Verdict: Accepted all three complex PDFs and returned markdown exports without manual cleanup.

Expected behavior: LlamaParse converts mixed PDFs and scanned documents into downloadable markdown, preserving readable order and headings on the hybrid earnings report, table-heavy financial report, and scanned research paper inputs.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Input — Hybrid-Earnings-PDF.pdf

Observed output: Output artifact (Text/code file): Accepted the 84-page hybrid annual report with native text, tables, charts, and a scanned signature page, and returned a markdown export. — llamaparse_target_earnings_output.md

Input artifact: Input artifact (PDF document): Input — Hybrid-Earnings-PDF.pdf

Output artifact: Output artifact (Text/code file): Accepted the 84-page hybrid annual report with native text, tables, charts, and a scanned signature page, and returned a markdown export. — llamaparse_target_earnings_output.md

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Input — Sumitomo Financial PDF.pdf

Observed output: Output artifact (Text/code file): Accepted the table-heavy Sumitomo financial report and returned a markdown export. — llamaparse_financial_pdf_output.md

Input artifact: Input artifact (PDF document): Input — Sumitomo Financial PDF.pdf

Output artifact: Output artifact (Text/code file): Accepted the table-heavy Sumitomo financial report and returned a markdown export. — llamaparse_financial_pdf_output.md

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Input — Scanned Research PDF.pdf

Observed output: Output artifact (Text/code file): Accepted the scanned research paper with multi-column text, tables, and charts and returned a markdown export. — llamaparse_scanned_pdf_output.md

Input artifact: Input artifact (PDF document): Input — Scanned Research PDF.pdf

Output artifact: Output artifact (Text/code file): Accepted the scanned research paper with multi-column text, tables, and charts and returned a markdown export. — llamaparse_scanned_pdf_output.md

What changed: PDF document transformed into Text/code file

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — earnings_hybrid_pdf_input_page_3.png

Observed output: Output artifact (Image): Retained the Target annual report page's heading, paragraph, and bullet order as a readable text hierarchy instead of flattening it. — llamaparse_hybridInput_hierarchy.png

Input artifact: Input artifact (Image): Input — earnings_hybrid_pdf_input_page_3.png

Output artifact: Output artifact (Image): Retained the Target annual report page's heading, paragraph, and bullet order as a readable text hierarchy instead of flattening it. — llamaparse_hybridInput_hierarchy.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — financialpdf_title_page.png

Observed output: Output artifact (Image): Preserved the Sumitomo report's title, disclaimer, and section ordering in a single reading flow. — llamaparse_financialInput_hierarchy.png

Input artifact: Input artifact (Image): Input — financialpdf_title_page.png

Output artifact: Output artifact (Image): Preserved the Sumitomo report's title, disclaimer, and section ordering in a single reading flow. — llamaparse_financialInput_hierarchy.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — scanned_pdf_multicolumn_section.png

Observed output: Output artifact (Image): Turned a scanned two-column page into coherent single-column prose with the section flow intact. — llamaparse_scannedInput_hierarchy.png

Input artifact: Input artifact (Image): Input — scanned_pdf_multicolumn_section.png

Output artifact: Output artifact (Image): Turned a scanned two-column page into coherent single-column prose with the section flow intact. — llamaparse_scannedInput_hierarchy.png

What changed: Image transformed into Image

Why it matters / Conclusion: Strong at ingesting mixed PDF types end-to-end; the tool consistently produced a usable markdown result.

LlamaParse converts mixed PDFs and scanned documents into downloadable markdown, preserving readable order and headings on the hybrid earnings report, table-heavy financial report, and scanned research paper inputs.

INPUT
Hybrid-Earnings-PDF.pdf
↓
OUTPUT
llamaparse_target_earnings_output.md
Loading file...
Accepted the 84-page hybrid annual report with native text, tables, charts, and a scanned signature page, and returned a markdown export.
INPUT
Sumitomo Financial PDF.pdf
↓
OUTPUT
llamaparse_financial_pdf_output.md
Loading file...
Accepted the table-heavy Sumitomo financial report and returned a markdown export.
INPUT
Scanned Research PDF.pdf
↓
OUTPUT
llamaparse_scanned_pdf_output.md
Loading file...
Accepted the scanned research paper with multi-column text, tables, and charts and returned a markdown export.
INPUT
Input artifact for "Document Parsing to Markdown" test: Input, earnings_hybrid_pdf_input_page_3.png
↓
OUTPUT
Output artifact for "Document Parsing to Markdown" test: Retained the Target annual report page's heading, paragraph, and bullet order as a readable text hierarchy instead of flattening it., llamaparse_hybridInput_hierarchy.png
Retained the Target annual report page's heading, paragraph, and bullet order as a readable text hierarchy instead of flattening it.
INPUT
Input artifact for "Document Parsing to Markdown" test: Input, financialpdf_title_page.png
↓
OUTPUT
Output artifact for "Document Parsing to Markdown" test: Preserved the Sumitomo report's title, disclaimer, and section ordering in a single reading flow., llamaparse_financialInput_hierarchy.png
Preserved the Sumitomo report's title, disclaimer, and section ordering in a single reading flow.
INPUT
Input artifact for "Document Parsing to Markdown" test: Input, scanned_pdf_multicolumn_section.png
↓
OUTPUT
Output artifact for "Document Parsing to Markdown" test: Turned a scanned two-column page into coherent single-column prose with the section flow intact., llamaparse_scannedInput_hierarchy.png
Turned a scanned two-column page into coherent single-column prose with the section flow intact.
Bottom Line
Strong at ingesting mixed PDF types end-to-end; the tool consistently produced a usable markdown result.
From our researchConvert a Complex PDF into Clean Markdown with an API
Table Extraction
Produces readable tables from financial, scanned, and nested table inputs, but grouped header semantics can drift.
▾
Test Summary
Feature tested: Table Extraction
Result: Partial — Produces readable tables from financial, scanned, and nested table inputs, but grouped header semantics can drift.

Feature tested: Table Extraction

Result: Partial

Verdict: Produces readable tables from financial, scanned, and nested table inputs, but grouped header semantics can drift.

Expected behavior: LlamaParse reconstructs tables from digital and scanned documents, including financial tables, multi-level segment tables, and nested stand-data tables, with markdown-style table output.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — earnings_hybridInput_table.png

Observed output: Output artifact (Image): Preserved the financial summary table's rows, columns, and yearly values in a readable table. — Llamaparse_hybridInput_table_retention.png

Input artifact: Input artifact (Image): Input — earnings_hybridInput_table.png

Output artifact: Output artifact (Image): Preserved the financial summary table's rows, columns, and yearly values in a readable table. — Llamaparse_hybridInput_table_retention.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — financial_pdf_multilevel_table.png

Observed output: Output artifact (Image): Preserved the grouped segment-results table and its year-over-year columns. — financialpdf_parsed_multilevel_table.png

Input artifact: Input artifact (Image): Input — financial_pdf_multilevel_table.png

Output artifact: Output artifact (Image): Preserved the grouped segment-results table and its year-over-year columns. — financialpdf_parsed_multilevel_table.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — scanned_pdf_multicolumn_table.png

Observed output: Output artifact (Image): Cleaned up the scanned harvest-diameter table while keeping treatment rows and before/after values aligned. — llamaparse_scannedInput_table_retention.png

Input artifact: Input artifact (Image): Input — scanned_pdf_multicolumn_table.png

Output artifact: Output artifact (Image): Cleaned up the scanned harvest-diameter table while keeping treatment rows and before/after values aligned. — llamaparse_scannedInput_table_retention.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — scanned_pdf_nested_table.png

Observed output: Output artifact (Image): Reconstructed the nested stand-data table with grouped treatment columns for 7-inch through clearcut cuts. — llamaparse_scannedInput_nested_table_outputmd.png

Input artifact: Input artifact (Image): Input — scanned_pdf_nested_table.png

Output artifact: Output artifact (Image): Reconstructed the nested stand-data table with grouped treatment columns for 7-inch through clearcut cuts. — llamaparse_scannedInput_nested_table_outputmd.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — financialInput_complex_table.png

Observed output: Output artifact (Image): Preserved the segment sales table values, but the grouped column semantics became less explicit in the rebuilt version. — llamaparse_financialInput_parsed_table.png

Input artifact: Input artifact (Image): Input — financialInput_complex_table.png

Output artifact: Output artifact (Image): Preserved the segment sales table values, but the grouped column semantics became less explicit in the rebuilt version. — llamaparse_financialInput_parsed_table.png

What changed: Image transformed into Image

Why it matters / Conclusion: Readable table recovery is a strength, but complex grouped headers can lose some of their original structure and meaning.

LlamaParse reconstructs tables from digital and scanned documents, including financial tables, multi-level segment tables, and nested stand-data tables, with markdown-style table output.

INPUT
Input artifact for "Table Extraction" test: Input, earnings_hybridInput_table.png
↓
OUTPUT
Output artifact for "Table Extraction" test: Preserved the financial summary table's rows, columns, and yearly values in a readable table., Llamaparse_hybridInput_table_retention.png
Preserved the financial summary table's rows, columns, and yearly values in a readable table.
INPUT
Input artifact for "Table Extraction" test: Input, financial_pdf_multilevel_table.png
↓
OUTPUT
Output artifact for "Table Extraction" test: Preserved the grouped segment-results table and its year-over-year columns., financialpdf_parsed_multilevel_table.png
Preserved the grouped segment-results table and its year-over-year columns.
INPUT
Input artifact for "Table Extraction" test: Input, scanned_pdf_multicolumn_table.png
↓
OUTPUT
Output artifact for "Table Extraction" test: Cleaned up the scanned harvest-diameter table while keeping treatment rows and before/after values aligned., llamaparse_scannedInput_table_retention.png
Cleaned up the scanned harvest-diameter table while keeping treatment rows and before/after values aligned.
INPUT
Input artifact for "Table Extraction" test: Input, scanned_pdf_nested_table.png
↓
OUTPUT
Output artifact for "Table Extraction" test: Reconstructed the nested stand-data table with grouped treatment columns for 7-inch through clearcut cuts., llamaparse_scannedInput_nested_table_outputmd.png
Reconstructed the nested stand-data table with grouped treatment columns for 7-inch through clearcut cuts.
INPUT
Input artifact for "Table Extraction" test: Input, financialInput_complex_table.png
↓
OUTPUT
Output artifact for "Table Extraction" test: Preserved the segment sales table values, but the grouped column semantics became less explicit in the rebuilt version., llamaparse_financialInput_parsed_table.png
Preserved the segment sales table values, but the grouped column semantics became less explicit in the rebuilt version.
Bottom Line
Readable table recovery is a strength, but complex grouped headers can lose some of their original structure and meaning.
From our researchConvert a Complex PDF into Clean Markdown with an API
OCR and Visual Content Transcription
Recovers text from scans and transcribes charts/signatures, but does not keep visuals as visuals.
▾
Test Summary
Feature tested: OCR and Visual Content Transcription
Result: Partial — Recovers text from scans and transcribes charts/signatures, but does not keep visuals as visuals.

Feature tested: OCR and Visual Content Transcription

Result: Partial

Verdict: Recovers text from scans and transcribes charts/signatures, but does not keep visuals as visuals.

Expected behavior: LlamaParse transcribes visual content from PDFs, turning charts into structured tables or text sequences and recognizing blurry signatures/stamps instead of dropping them.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — hybridearnings_pdf_waterfall_chart.png

Observed output: Output artifact (Image): Converted the SG&A waterfall chart into a text sequence of rates and contributing changes instead of leaving it as an image. — llamaparse_hybrid_earningspdf_parsed_waterfall_chart.png

Input artifact: Input artifact (Image): Input — hybridearnings_pdf_waterfall_chart.png

Output artifact: Output artifact (Image): Converted the SG&A waterfall chart into a text sequence of rates and contributing changes instead of leaving it as an image. — llamaparse_hybrid_earningspdf_parsed_waterfall_chart.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — hybrid_earningspdf_blurry_stamp.png

Observed output: Output artifact (Image): Detected the Ernst & Young LLP signature line from the blurry stamp and emitted it as extracted text rather than dropping it. — llamaparse_hybrid_earningspdf_parsed_blurry_stamp.png

Input artifact: Input artifact (Image): Input — hybrid_earningspdf_blurry_stamp.png

Output artifact: Output artifact (Image): Detected the Ernst & Young LLP signature line from the blurry stamp and emitted it as extracted text rather than dropping it. — llamaparse_hybrid_earningspdf_parsed_blurry_stamp.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — scanned_pdf_chart.png

Observed output: Output artifact (Image): Converted the scanned mortality chart into a structured year-by-treatment table. — llamaparse_scannedInput_parsed_chart.png

Input artifact: Input artifact (Image): Input — scanned_pdf_chart.png

Output artifact: Output artifact (Image): Converted the scanned mortality chart into a structured year-by-treatment table. — llamaparse_scannedInput_parsed_chart.png

What changed: Image transformed into Image

Why it matters / Conclusion: OCR and transcription coverage is good, but the output is text-centric rather than image-preserving.

LlamaParse transcribes visual content from PDFs, turning charts into structured tables or text sequences and recognizing blurry signatures/stamps instead of dropping them.

INPUT
Input artifact for "OCR and Visual Content Transcription" test: Input, hybridearnings_pdf_waterfall_chart.png
↓
OUTPUT
Output artifact for "OCR and Visual Content Transcription" test: Converted the SG&A waterfall chart into a text sequence of rates and contributing changes instead of leaving it as an image., llamaparse_hybrid_earningspdf_parsed_waterfall_chart.png
Converted the SG&A waterfall chart into a text sequence of rates and contributing changes instead of leaving it as an image.
INPUT
Input artifact for "OCR and Visual Content Transcription" test: Input, hybrid_earningspdf_blurry_stamp.png
↓
OUTPUT
Output artifact for "OCR and Visual Content Transcription" test: Detected the Ernst & Young LLP signature line from the blurry stamp and emitted it as extracted text rather than dropping it., llamaparse_hybrid_earningspdf_parsed_blurry_stamp.png
Detected the Ernst & Young LLP signature line from the blurry stamp and emitted it as extracted text rather than dropping it.
INPUT
Input artifact for "OCR and Visual Content Transcription" test: Input, scanned_pdf_chart.png
↓
OUTPUT
Output artifact for "OCR and Visual Content Transcription" test: Converted the scanned mortality chart into a structured year-by-treatment table., llamaparse_scannedInput_parsed_chart.png
Converted the scanned mortality chart into a structured year-by-treatment table.
Bottom Line
OCR and transcription coverage is good, but the output is text-centric rather than image-preserving.
From our researchConvert a Complex PDF into Clean Markdown with an API
Hosted API Access
The product is set up as a hosted service with API key management and a cloud results workflow.
▾
Test Summary
Feature tested: Hosted API Access
Result: Passed — The product is set up as a hosted service with API key management and a cloud results workflow.

Feature tested: Hosted API Access

Result: Passed

Verdict: The product is set up as a hosted service with API key management and a cloud results workflow.

Expected behavior: LlamaParse exposes project API keys, a results dashboard, and fully automated parsing through API calls, supporting cloud/API-backed workflows.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The settings page shows Project API Keys, existing keys, and a Generate New Key control for the hosted service. — llamaparse_apikey.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The settings page shows Project API Keys, existing keys, and a Generate New Key control for the hosted service. — llamaparse_apikey.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The web app shows Docs, Configs, History, Parse/Extract/Split/Classify navigation plus source-page thumbnails and result controls, confirming a hosted workflow. — llamaparse_downloadable_visual_assets.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The web app shows Docs, Configs, History, Parse/Extract/Split/Classify navigation plus source-page thumbnails and result controls, confirming a hosted workflow. — llamaparse_downloadable_visual_assets.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Well suited to API-backed pipelines, with visible credential management and a cloud results interface.

LlamaParse exposes project API keys, a results dashboard, and fully automated parsing through API calls, supporting cloud/API-backed workflows.

INPUT
Hosted parse workflow with API keys and downloadable results
↓
OUTPUT
Output artifact for "Hosted API Access" test: The settings page shows Project API Keys, existing keys, and a Generate New Key control for the hosted service., llamaparse_apikey.png
The settings page shows Project API Keys, existing keys, and a Generate New Key control for the hosted service.
INPUT
Post-run cloud results interface for parsed documents
↓
OUTPUT
Output artifact for "Hosted API Access" test: The web app shows Docs, Configs, History, Parse/Extract/Split/Classify navigation plus source-page thumbnails and result controls, confirming a hosted workflow., llamaparse_downloadable_visual_assets.png
The web app shows Docs, Configs, History, Parse/Extract/Split/Classify navigation plus source-page thumbnails and result controls, confirming a hosted workflow.
Bottom Line
Well suited to API-backed pipelines, with visible credential management and a cloud results interface.
From our researchConvert a Complex PDF into Clean Markdown with an API
Resume Parsing
Excellent — most structurally rich output of all tools tested, CGPA and certifications fully structured
10/10
▾
Test Summary
Feature tested: Resume Parsing
Result: Passed (10/10) — Excellent — most structurally rich output of all tools tested, CGPA and certifications fully structured

Feature tested: Resume Parsing

Result: Passed (10/10)

Verdict: Excellent — most structurally rich output of all tools tested, CGPA and certifications fully structured

Expected behavior: LlamaParse extracts structured fields from resumes across clean single-column, multi-column, and messy formats, returning rich JSON with skills, certifications, languages, projects, education, and related fields.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): input-1-clean-resume-rugved.pdf — Llamaparse input.1.pdf

Observed output: Output artifact (Text/code file): Full JSON output — LlamaParse parsing clean resume — llama output.1.txt

Input artifact: Input artifact (PDF document): input-1-clean-resume-rugved.pdf — Llamaparse input.1.pdf

Output artifact: Output artifact (Text/code file): Full JSON output — LlamaParse parsing clean resume — llama output.1.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): nput-2-multicolumn-resume-priya.pdf — Llamaparse input.2.pdf

Observed output: Output artifact (Text/code file): Full JSON output — LlamaParse parsing multi-column resume — llama output.2.txt

Input artifact: Input artifact (PDF document): nput-2-multicolumn-resume-priya.pdf — Llamaparse input.2.pdf

Output artifact: Output artifact (Text/code file): Full JSON output — LlamaParse parsing multi-column resume — llama output.2.txt

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): input-3-messy-resume-john.pdf — Llamaparse input.3.pdf

Observed output: Output artifact (Text/code file): Full JSON output — LlamaParse parsing messy resume — llama output.3.txt

Input artifact: Input artifact (PDF document): input-3-messy-resume-john.pdf — Llamaparse input.3.pdf

Output artifact: Output artifact (Text/code file): Full JSON output — LlamaParse parsing messy resume — llama output.3.txt

What changed: PDF document transformed into Text/code file

Why it matters / Conclusion: Excellent output on clean resumes — most structurally rich of all tools tested. CGPA captured as dedicated standalone field. All 5 skill categories correctly structured. Both certifications as fully structured objects. Main weakness is job title missing the AI prefix and languages field absent since no spoken languages section was in the resume.

LlamaParse extracts structured fields from resumes across clean single-column, multi-column, and messy formats, returning rich JSON with skills, certifications, languages, projects, education, and related fields.

PDF
Llamaparse input.1.pdf
↓
PDF
llama output.1.txt
Loading file...
PDF
Llamaparse input.2.pdf
↓
PDF
llama output.2.txt
Loading file...
PDF
Llamaparse input.3.pdf
↓
PDF
llama output.3.txt
Loading file...
Bottom Line
Excellent output on clean resumes — most structurally rich of all tools tested. CGPA captured as dedicated standalone field. All 5 skill categories correctly structured. Both certifications as fully structured objects. Main weakness is job title missing the AI prefix and languages field absent since no spoken languages section was in the resume.
From our researchearlier research

Pricing & Access

TESTED
Free
$0
10,000 free credits on signup, no credit card required. 1 page costs 1 credit on Fast tier, 3 credits on Cost Effective, 10 credits on Agentic. Sufficient for initial testing across all resume inputs.
Basic
$3/mo
6,000 credits per month, all parsing tiers included, API access, JSON and Markdown export
Premium
$7/mo
14,000 credits per month, all Basic features plus priority processing and higher rate limits
Business
Custom
High volume credits, dedicated support, enterprise SLA, custom integrations. Contact LlamaIndex sales for pricing.

Pricing checked May 2026. We re-check quarterly. Credits are consumed per page based on selected parse tier. Visit llamaparse.ai for current plans.

✓ Use This If
●You need a hosted API that turns hybrid, scanned, and table-heavy PDFs into downloadable markdown.
●You need section hierarchy and reading order preserved across multi-column pages.
●You need tables reconstructed into readable markdown, even when headers are multi-level.
●You need charts, signatures, or stamps transcribed into text rather than dropped.
✕ Skip This If
●You need every grouped-table header relationship to stay explicit; some complex tables lose semantic clarity.
●You need charts and logos preserved as visual assets inside the output rather than transcribed into text or tables.
●You need a structured table of contents instead of sequential extracted text.
developer-toolsapistext
Yes. In this research it accepted an 84-page hybrid earnings report and a scanned research paper, and returned downloadable markdown for both.
It preserved simple financial tables, multi-level financial tables, scanned harvest-diameter tables, and nested treatment tables, but grouped header semantics became less explicit in the hardest table case.
Not in these tests. The SG&A waterfall and the mortality chart were converted into structured text or tables, and the blurry Ernst & Young signature was transcribed as text.
Mostly yes. The hybrid earnings report and the scanned two-column paper both kept readable hierarchy, but the table of contents was extracted as sequential text rather than a structured TOC.
Yes. Each of the three document tests returned a markdown file that can be consumed downstream.
Yes. The app includes an API Keys page with project keys and a Generate New Key button.

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