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Data FormatExtension: .csvMIME: text/csv; charset=utf-8

Download Sample Comma Separated Values (CSV) Files

CSV files are the backbone of data imports and exports. Our sample CSV files feature authentic 2026 AI model benchmark datasets (Model Name, Developer, Architecture, Parameters, Benchmark Scores, Pricing) sized up to 10MB to test ETL pipelines, spreadsheet software, and bulk import APIs.

Standardized File Headers
5 Size Variants Available
Magic Bytes: ASCII / UTF-8 delimiter table
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All Available Sizes for Comma Separated Values (CSV)

Compare sizes, check precise byte counts, copy links, or download directly.

File & SizeExact BytesAction
100 KB(sample-100kb.csv)
100 KB
102,400 B
500 KB(sample-500kb.csv)
500 KB
512,000 B
1 MB(sample-1mb.csv)
1 MB
1,048,576 B
5 MB(sample-5mb.csv)
5 MB
5,242,880 B
10 MB(sample-10mb.csv)
10 MB
10,485,760 B

Individual Comma Separated Values (CSV) Downloads

Comma.csv

100 KBcsv

Download an authentic 2026 AI model benchmark dataset in CSV format 100 KB in size, with reasoning scores, token costs, and model architectures.

100 KB.csv
Comma.csv

500 KBcsv

Download an authentic 2026 AI model benchmark dataset in CSV format 500 KB in size, with reasoning scores, token costs, and model architectures.

500 KB.csv
Comma.csv

1 MBcsv

Download an authentic 2026 AI model benchmark dataset in CSV format 1 MB in size, with reasoning scores, token costs, and model architectures.

Comma.csv

5 MBcsv

Download an authentic 2026 AI model benchmark dataset in CSV format 5 MB in size, with reasoning scores, token costs, and model architectures.

Comma.csv

10 MBcsv

Download an authentic 2026 AI model benchmark dataset in CSV format 10 MB in size, with reasoning scores, token costs, and model architectures.

Common Testing Scenarios for Comma Separated Values (CSV)

QA engineers and software developers use our .csv dummy files for the following test cases:

Bulk user / product import functions in SaaS applications
Streaming CSV parsers (PapaParse, csv-parser) benchmark
Database batch inserts with PostgreSQL / MySQL COPY commands
Data science pipelines with Pandas, Polars, and DuckDB

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