Which AI Chatbots Can Extract Tables From PDF Files
- Graziano Stefanelli
- Sep 19
- 4 min read
PDF table extraction has become a core feature across leading AI chatbots in 2025, enabling users to convert tabular content into editable formats like CSV, Markdown, or Google Sheets with a simple prompt. While all major platforms now support this functionality, the accuracy, export formats, and degree of control vary significantly depending on the model and ecosystem. This article compares how ChatGPT, Claude, Gemini, Grok, Perplexity AI, and Microsoft Copilot handle table extraction from PDF files.
ChatGPT extracts tables using Python-powered analysis in ADA.
In ChatGPT, table extraction from PDFs is available through the Advanced Data Analysis (ADA) environment across GPT-4o and GPT-5 preview models. Users can drag and drop a PDF file, then enter a prompt such as:
“Extract all tables as CSV files and describe each briefly.”
Behind the scenes, ChatGPT uses pandas to parse the PDF and return the results as downloadable CSV files. Multiple tables are bundled into a ZIP archive, and a short narrative explains their content or structure.
ChatGPT (GPT-4o / GPT-5 preview) | |
Max file size (chat) | 512 MB |
Export formats | CSV, ZIP, summary text |
Scanned PDF handling | Vision-enabled, partial cell alignment |
Best use case | Financial reports with structured tables |
Scanned tables or image-based PDFs are also supported using GPT-4o’s multimodal capabilities, but the quality may degrade for complex layouts or multi-span headers.
Claude delivers clean Markdown or CSV output with persistent file reuse.
Claude (Sonnet 4 and Opus 4.1) supports native PDF parsing with structured table recognition. When you upload a PDF and request something like:
“Extract all tables from pages 4 to 10 and normalize column names,”
Claude identifies and isolates tabular blocks, then renders them as clean Markdown tables or CSV output. It’s especially effective when page ranges are specified in the prompt.
Claude's Files API allows persistent storage of large PDF files—up to 500 MB per file—which can be reused across multiple prompts without needing re-upload.
Claude (Sonnet 4 / Opus 4.1) | |
Max file size (chat) | 30 MB per file, up to 20 files |
Files API | 500 MB per file, 100 GB org-wide |
Export formats | Markdown, CSV |
Best use case | Legal and academic documents with tables |
Claude does not support scanned PDFs with embedded images, but for structured text-based files, it consistently returns accurate column alignment.
Gemini extracts tables at scale and integrates directly with Google Sheets.
Gemini’s Document-understanding API is optimized for structured data extraction, including full support for table detection, header inference, and cell mapping. The model outputs results as structured JSON arrays, which can then be pushed to Sheets with one click or parsed in code.
In Workspace environments, opening a PDF in Google Drive automatically triggers Gemini’s summarization layer, which generates summary cards that list detected tables. Users can immediately click “Copy to Sheets” to begin working with the data.
Gemini (2.5 Flash / Pro) | |
Max file size (chat) | 100 MB per file (10 files per prompt) |
Export formats | JSON, Sheets, Drive-integrated cards |
Best use case | Enterprise batch-processing of PDFs |
Benchmarks across over 1,000 financial documents show Gemini outperforming GPT-4o by approximately 8 percentage points in header detection and table completeness.
Grok supports lightweight table extraction through its Python sandbox.
Grok (Grok 4 and Grok 4 Heavy) can process PDF uploads and extract tables using a more limited code-execution environment than ChatGPT. After uploading a document, users can prompt:
“Extract all tables and return them as CSVs or a bar chart of totals.”
Grok uses a Python environment with support for pandas and basic visualization libraries. It can return downloadable CSVs or charts, though its parsing accuracy is slightly lower on wide tables or unusual cell layouts.
Grok (xAI) | |
Max file size (chat) | 25–30 MB |
Files API | 500 MB per file |
Export formats | CSV, PNG, in-chat table view |
Best use case | Trend visualization from reports |
Grok does not support direct evaluation of Excel formulas and has no current spreadsheet editor, though a cell-editing layer is expected in late 2025.
Perplexity extracts tables via Markdown, best suited for quick lookups.
Perplexity AI allows PDF upload via URL input and can identify tables in simple documents. It returns the result as Markdown tables, viewable directly in the chat. However, it does not support CSV export, so users must copy and paste results into a spreadsheet manually.
Perplexity AI | |
Upload method | PDF URL only (no local upload) |
Export format | Markdown (copy/paste only) |
Best use case | Lightweight queries, document previews |
Accuracy is reliable for single-table pages or standardized formats, but there’s no support for image-based PDFs, batch export, or embedded diagrams.
Microsoft Copilot integrates table extraction with Excel and Power Automate.
Microsoft Copilot now offers direct PDF table extraction within Excel, Outlook, and Copilot Studio. For example, a user can open a PDF in OneDrive and ask:
“Extract all tables and insert them into a new worksheet.”
Copilot uses Graph connectors and OneDrive APIs to locate tabular blocks, then generates editable Excel sheets. In enterprise environments, Power Automate flows can schedule PDF table extraction actions to feed SharePoint dashboards or Excel trackers.
Microsoft Copilot | |
Upload method | OneDrive / Outlook attachments |
Export format | Native Excel sheet |
Integration options | Power Automate, Graph API |
Best use case | Workflow automation inside Microsoft 365 |
While extremely convenient in Microsoft environments, its accuracy depends on layout simplicity, and it's not designed for advanced table interpretation or chart generation.
Comparative summary
AI chatbot | Native table detection | Max file size (chat) | Export format(s) | Ideal use case |
ChatGPT | Yes (via pandas) | 512 MB | CSV, ZIP, summary text | Multi-table financial reports |
Claude | Yes (Markdown/CSV) | 30 MB × 20 files | Markdown, CSV | Legal docs with dense table content |
Gemini | Yes (JSON + Sheets) | 100 MB | JSON, Google Sheets | Bulk extraction and data pipeline use |
Grok | Yes (limited libs) | 25–30 MB | CSV, PNG | Exploratory trend analysis from PDFs |
Perplexity | Yes (Markdown) | N/A (URL-based only) | Markdown (copy/paste) | Fast previews or reading simple tables |
Copilot | Yes (M365 only) | N/A (via OneDrive/Outlook) | Excel sheet | Microsoft 365 workflows and automations |
Every leading AI assistant in 2025 can extract tables from PDFs, but the best choice depends on the format, desired export method, and platform integration. ChatGPT and Claude are strong general-purpose tools for CSV output. Gemini excels at scale, especially for Google Workspace users. Copilot offers seamless workflows for Excel users. Grok and Perplexity provide lightweight or code-driven options when deeper analysis or quick results are needed.
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