Meta AI Spreadsheet Reading: Supported Formats, File Size and Table Limits, API Workflows, and Prompting Techniques for Reliable Analysis

Graziano Stefanelli
19 hours ago
3 min read

Meta AI reads spreadsheets in CSV, TSV, and XLSX formats, offering users the ability to upload, summarize, clean, and explain data tables directly in chat or via the Llama API.

Performance and feature support vary across Meta.ai on the web, the mobile app, chat integrations (Messenger, WhatsApp, Instagram), and developer workflows, but every workflow is shaped by clear size, row, and column boundaries.

Knowing these boundaries is key to achieving reliable, accurate spreadsheet analysis at any scale.

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Meta AI accepts CSV and TSV natively, with XLSX supported up to recommended size limits.

CSV and TSV are the most stable formats for upload and parsing, as they are interpreted line-by-line as plain text.

XLSX files, which are binary and support formulas and multiple sheets, are also accepted, but converting large or formula-heavy files to CSV can reduce parsing errors and accelerate model processing.

Image-based tables from scanned or photographed sources are processed through Meta AI’s vision stack, primarily in mobile and web chat interfaces.

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File size boundaries: twenty-five megabytes per file in chat, up to fifty megabytes via the Llama API.

Meta does not officially state a hard maximum for spreadsheet uploads in consumer chat, but practical tests show uploads over 25 MB often fail or time out.

For larger data jobs, the Llama API enables uploads up to 50 MB per file, supporting even larger datasets or multi-sheet workbooks, especially when pre-processed to CSV.

Exceeding these limits requires splitting files by sheet, trimming unused columns, or sampling data to keep the size manageable.

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Spreadsheet File Limits

Workflow	Max File Size	Notes
Web or mobile chat	25 MB	1 file per message
Llama API	50 MB	Up to 10 files per call

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Row and column ceilings: up to one million rows or ten thousand columns per table for best results.

Meta AI’s chat and API remain responsive when tables are ≤ 1,000,000 rows and ≤ 10,000 columns.

Larger uploads risk partial reading, truncation, or slow performance, so big files should be chunked by time period, region, or logical grouping.

Sparse wide tables (many empty columns) also run more efficiently if unnecessary columns are dropped before upload.

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Spreadsheet Size Guidelines

Metric	Recommended Ceiling	Advice
File size (chat)	≤ 25 MB	Use CSV for largest files
File size (API)	≤ 50 MB	Batch up to 10 at a time
Rows	≤ 1,000,000	Chunk above this
Columns	≤ 10,000	Remove unused columns

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Effective spreadsheet prompting: clear goals, labeled columns, and formula reference improve outcomes.

Prompt Meta AI with an explicit task, such as “Summarize quarterly sales by product” or “Explain all formulas in column H.”

Including a short schema or sample header (“Columns: Date, Product, Sales, Cost”) accelerates comprehension and avoids hallucination.

For formula explanations or debugging, anchor the prompt by referencing specific cells (“Explain the VLOOKUP in cell D42”).

When uploading multiple sheets or files via API, prepend a #GOAL or #GUIDELINES block in the prompt so Meta AI knows how to organize and return outputs.

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Platform differences: web and mobile chats are single-file; the Llama API supports up to ten files in a single prompt.

Consumer-facing chats (Meta.ai web, Messenger, WhatsApp, Instagram) allow uploading one spreadsheet at a time.

For multi-table analysis, upload each file in sequence and chain requests in a threaded conversation.

The Llama API enables batch uploads (up to 10 files), making it suitable for research, analytics, or ETL workloads where cross-sheet calculations or comparisons are needed.

Meta’s cloud SDK also supports staging large volumes of files before model invocation.

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Enterprise workflows: chunking, indexing, and retrieval-augmented prompts for large-scale spreadsheet analysis.

Break very large XLSX or CSV files into logical pieces by year, business unit, or region to stay within file-size and row limits.

Batch files through the Llama API, tagging each with clear metadata in the prompt.

Construct index prompts listing each file’s topic and instruct Meta AI to “select the correct spreadsheet and range before answering.”

Refine responses by iteratively asking for pivots, anomaly detection, forecasts, or aggregations to guide step-by-step analysis.

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Meta AI’s spreadsheet reading unlocks structured insights from raw tables when uploads follow size, column, and prompt structure guidance.

Keeping files below fifty megabytes, rows under one million, and columns under ten thousand ensures smooth parsing and accurate results in both chat and API workflows.

Clear prompts, explicit schemas, and chunked files allow teams to clean data, audit finance sheets, or build analytics pipelines without leaving Meta’s environment.

These practices support scalable, multi-step spreadsheet analysis for both consumers and enterprise users.

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DATA STUDIOS

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[datastudios.org]