DeepSeek Spreadsheet Reading: File Limits, Tabular Reasoning, and Data-Analysis Performance for Late 2025/2026

DeepSeek models have evolved into reliable spreadsheet-analysis engines, capable of reading large CSV files, dissecting Excel workbooks, performing numeric reasoning, detecting structural anomalies, and generating multi-step insights across datasets.

The system behaves differently depending on whether the user runs DeepSeek-V3, DeepSeek-V3.2-Exp, or DeepSeek-R1, but all three models share a common architecture for file ingestion, tabular extraction, and token-efficient summarization.

This article examines how DeepSeek processes spreadsheets, what limits apply, how tokens interact with dataset size, and which strategies produce the most accurate results in late 2025/2026.

····················

DeepSeek models accept CSV, XLSX, TSV, and text-table formats, with file sizes up to thirty to fifty megabytes depending on the model tier.

The standard DeepSeek file pipeline supports CSV, XLSX, TSV, and plain-text tables.

DeepSeek-V3 handles uploads up to 25–50 MB, while DeepSeek-V3.2-Exp maintains stable performance up to 30 MB for structured spreadsheets.

DeepSeek-R1 supports 10–20 MB uploads, prioritizing reasoning fidelity over large-file ingestion speed.

Macro-enabled Excel files (XLSM) are not interpreted as executable spreadsheets and are treated as static data.

··········

·····Supported Spreadsheet Formats and Size Limits

Format	Max Size (Typical)	Notes
CSV	25–50 MB	Best performance and fastest parsing
XLSX	20–30 MB	Auto-converted internally to CSV
TSV	20–40 MB	Stable for large numeric tables
TXT / Markdown tables	10 MB	Suitable for lightweight rows
Multi-sheet Excel files	Reads first sheet	Secondary sheets require manual extraction

····················

Spreadsheet ingestion consumes context window space, making token budgeting essential for large datasets.

DeepSeek models do not treat spreadsheets as external references.

Each row and each cell consumes tokens once parsed, causing large files to occupy extremely large fractions of the model’s context.

One row often expands to 10–30 tokens, depending on column count and formatting.

A dataset of 100 000 rows may translate to 1.2–2.4 million tokens, far exceeding the usable window and forcing DeepSeek to summarize, compress, or sample rows automatically.

Users must therefore scope questions carefully or split files into manageable segments.

··········

·····Token Consumption Examples

Dataset	Approx. Token Cost	Remaining Room for Prompts
20 000-row CSV	240 000–400 000	Medium
60 000-row CSV	720 000–1 200 000	Low
100 000-row CSV	1.2–2.4 M	Requires summarization

····················

DeepSeek provides structured column statistics, anomaly detection, and detailed formula interpretation.

DeepSeek automatically identifies numeric columns, applies descriptive statistics, computes averages, medians, distributions, and detects outliers based on column variance.

It performs correlation analysis between columns, flags duplicated values, identifies inconsistent formatting, and maps missing values across the dataset.

For Excel formulas, DeepSeek does not execute logic directly but generates accurate plain-language explanations of formulas such as VLOOKUP, XLOOKUP, IF, INDEX/MATCH, SUMIFS, and multi-conditional logic.

The output includes step-by-step reasoning, dependency tracing, and formula de-composition for troubleshooting large spreadsheets.

··········

·····Core Analytical Capabilities

Capability	Description
Column statistics	Mean, median, quartiles, outliers
Correlation analysis	Detects linear and nonlinear relationships
Formula explanation	Plain-language reconstruction of logic
Missing-value mapping	Identifies patterns of nulls or blanks
Data cleaning suggestions	Type correction, deduplication, normalization
Anomaly detection	Pattern breaks, outlier rows, inconsistent date formats

····················

DeepSeek-V3.2-Exp and DeepSeek-R1 excel at multi-step reasoning across complex spreadsheet structures.

DeepSeek-V3.2-Exp specializes in long-chain reasoning and can explain why models behave unexpectedly, detect misaligned ranges, and highlight inconsistent column types.

It is particularly strong with financial spreadsheets, forecasting models, and multi-sheet exports converted to CSV.

DeepSeek-R1—trained through reinforcement—handles logic-heavy tasks such as identifying flawed assumptions in a dataset, tracing erroneous calculations, and analyzing row-by-row rule violations.

Both models can detect hidden encoding issues (e.g., numeric strings containing letter characters), contradictory category values, and irregular time-series structure.

····················

Spreadsheet limitations include lack of formula execution, partial multi-sheet support, and slower processing on wide tables.

DeepSeek models do not execute formulas, evaluate macros, or reconstruct Excel’s calculation engine.

Workbooks with more than fifty columns slow ingestion speed and increase token footprint significantly.

Files containing multiple sheets must be manually split or exported as separate CSV files; DeepSeek reads only the first sheet of XLSX files.

Vector-heavy numeric tables may trigger fallback to sampling mode, reducing accuracy when handling highly granular financial data.

····················

Best practices maximize DeepSeek’s accuracy and prevent token wastage.

Convert multi-sheet Excel files into separate CSV files to preserve clarity and prevent incomplete parsing.

Split very large datasets into segments below fifty thousand rows and analyze iteratively.

Provide context such as column definitions or business logic to avoid misinterpretation and reduce token consumption.

Ask DeepSeek to summarize the structure before running detailed analysis to build an internal schema.

Run anomaly detection early to diagnose hidden encoding or formatting issues before deeper reasoning steps.

··········

FOLLOW US FOR MORE

··········

··········

DATA STUDIOS

··········

[datastudios.org]