pandasvstablesaw

How 10 AI models weigh pandas against tablesaw on the 1 buying question where both came up.

pandas
100%of answers named it
tablesaw
1%of answers named it
Placed higher
40–0times pandas vs tablesaw, over 40 model rankings that listed either
Models leaning
10–0toward pandas vs tablesaw

Which models prefer which

Share of each model's answers that named each product, and how often its ranking put one above the other (a product listed beats one left off) · On the 1 shared question, every wording

ModelpandastablesawPlaced higherLeans
Claude Haiku 5.5100%0%4–0pandas
GPT-6 Luna100%0%4–0pandas
Gemini 4 Argon100%0%4–0pandas
Qwen 3.8 Flash100%0%4–0pandas
DeepSeek V4.1 Flash100%0%4–0pandas
GLM-5.3100%0%4–0pandas
Kimi K3100%0%4–0pandas
MiniMax M3100%0%4–0pandas
Mistral Large 4100%0%4–0pandas
Nemotron 3.5 Super VL100%13%4–0pandas

What the models say about pandas

One reason per model, from the shared questions

  • Claude Haiku 5.5Best for data analysis; reads and writes Excel via openpyxl or xlsxwriter engines.
  • GPT-6 LunaExcellent for tabular imports, transformations, and exports, especially alongside openpyxl.
  • Gemini 4 ArgonUnmatched data analysis capabilities with effortless reading and writing via unified Excel wrappers.
  • Qwen 3.8 FlashMost common way to read Excel into dataframes; edit via export.
  • DeepSeek V4.1 FlashFast bulk read/write to DataFrames; the analysis workhorse.
  • GLM-5.3Great for data analysis; converts Excel to dataframes easily.
  • Kimi K3Best for data analysis workflows; read_excel/to_excel cover most practical needs.
  • MiniMax M3Excel I/O built-in, ideal for data analysis workflows.
  • Mistral Large 4Essential for Excel data analysis; read_excel and to_excel are incredibly convenient.
  • Nemotron 3.5 Super VLBest for data analysis and tabular Excel operations

What the models say about tablesaw

One reason per model, from the shared questions

Over time

Share of answers that named each, on the questions where both came up, each time the panel answered

Panel runpandastablesaw
October 26, 2026100%1%
October 25, 2026100%0%

Question by question

Share of all answers that named each, every wording pooled, with its place among every product named · Both-named questions first