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Financial modeling and scenario planning · November 2026 Edition

Microsoft Excel vs Datarails

Zero of fourteen models named Microsoft Excel first on the direct prompt; eight named Datarails. Microsoft Excel was named by thirteen of the fourteen models and Datarails by thirteen and Microsoft Excel carries 19 labels and Datarails 27, so the shares are not directly comparable.

Microsoft Excel

accepted challenger

Named in thirteen categories this edition.

Datarails

accepted challenger

Named in ten categories this edition.

First-choice share19%16%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate16%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#2A position in a field of 11; printed, not drawn.
Labels1927A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Microsoft Excel reading right to left. Rank and label count are printed, not drawn.Vena was named alongside these two in nine of the fourteen direct answers. Microsoft Excel vs Google Sheets · Microsoft Excel vs Pigment · Microsoft Excel vs Workday Adaptive Planning

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the financial modeling and scenario planning page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Microsoft ExcelFirst choices, of fourteen modelsDatarails
Direct081 against Microsoft Excel
Paraphrase001 against Microsoft Excel
Comparative00
Budget-constrained110
Scale-constrained01
Negative001 against Microsoft Excel
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Microsoft Excel and Datarails stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
Microsoft Excel Datarails first choice named as an alternative argued againstblank: not namedEach cell is one answer, Microsoft Excel on the left and Datarails on the right.

The direct prompt

The plain question, one answer per model, grouped by where Microsoft Excel and Datarails stood in it.

Datarails first, Microsoft Excel not the choice

8 of 14 modelsMicrosoft Excel was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Datarails alternatives: Cube, Drivetrain, Pigment, Planful
Grok 4.20Datarails alternatives: Cube, Jirav, Quantrix Modeler
Mistral SmallDatarails alternatives: Cube, Limelight, Vena
Llama 4 MaverickAleph, Datarails, Limelight
Qwen 3.7 FlashDatarails, Limelight alternatives: Aleph, Cube, Jedox, Vena
Kimi K2Cube, Datarails alternatives: Drivetrain, Jirav
GLM 4.7 FlashXDatarails alternatives: OneStream, Pigment, Planful, Vena
Muse Glimmer 30BDatarails alternatives: Aleph, Cube, Limelight, Planful, Prophix One, Vena

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashMosaic alternatives: Cube, Datarails, Pigment, Vena, Workday Adaptive Planning
Perplexity SonarDrivetrain alternatives: Abacum, Datarails, Limelight, Vena
DeepSeek V4 FlashPigment alternatives: Aleph, Cube, Datarails, Prophix One

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniPigment, Workday Adaptive Planning alternatives: Anaplan, Vena
MiniMax M2.5Workday Adaptive Planning alternatives: Cube, Vena
GPT-6 LunaWorkday Adaptive Planning alternatives: Planful, Vena

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Microsoft Excel leads by twenty-two points.
Microsoft Excel22%#1 of 16
Datarails0%#12 of 16
The full small business standing →
Mid-marketThe figures above
Microsoft Excel leads by four points.
Microsoft Excel19%#1 of 11
Datarails16%#2 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Microsoft Excel0%#– of 11
Datarails0%#– of 11
The full enterprise standing →

What the models said about Microsoft Excel

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“For the absolute best value, Microsoft Excel is the top choice due to its low cost (or free if already owned) and unmatched flexibility.” Mistral Small · budget prompt · first choice
“Microsoft Excel is widely regarded as the best financial modeling software for companies with a limited budget.” Grok 4.20 · budget prompt · first choice
“The most cost-effective starting point. Many successful companies use Excel through their early stages.” Kimi K2 · budget prompt · first choice

What the models said about Datarails

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“Datarails is called out as a strong fit for Excel-native finance teams particularly those managing multiple entities thanks to 600+ integrations.” Muse Glimmer 30B · scale prompt · first choice
“For most mid-market finance teams, especially those managing multiple entities in Excel, Datarails is the best financial modeling software” Claude Haiku 4.5 · direct prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.