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Financial modeling and scenario planning · September 2026 Edition

Google Sheets vs Workday Adaptive Planning

Zero of twelve models named Google Sheets first on the direct prompt; zero named Workday Adaptive Planning. Google Sheets was named by eight of the twelve models and Workday Adaptive Planning by eleven and Google Sheets carries 10 labels and Workday Adaptive Planning 17, so the shares are not directly comparable.

Google Sheets

accepted challenger

Named in nine categories this edition.

Workday Adaptive Planning

criticized challenger

Named in seven categories this edition.

First-choice share13%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.

Negative rate0%35%Negative labels as a share of the product's labels, 0 to 100.

Rank in category#2#7A position in a field of 8; printed, not drawn.

Labels1017A count; the two differ.

The two percentage rows are drawn on one 0 to 100 track, Google Sheets reading right to left. Rank and label count are printed, not drawn.Datarails was named alongside these two in ten of the twelve direct answers. Datarails vs Google Sheets · Datarails vs Workday Adaptive Planning · Google Sheets vs Cube

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.

Google SheetsFirst choices, of twelve modelsWorkday Adaptive Planning

Direct00

Paraphrase021 against Workday Adaptive Planning

Comparative00

Budget-constrained603 against Workday Adaptive Planning

Scale-constrained002 against Workday Adaptive Planning

Negative00

Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Google Sheets and Workday Adaptive Planning stood in it.

Model	Direct	Paraphrase	Comparative	Budget-constrained	Scale-constrained	Negative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5

Google Sheets

Workday Adaptive Planning

first choice

named as an alternative

argued againstblank: not namedEach cell is one answer, Google Sheets on the left and Workday Adaptive Planning on the right.

The direct prompt

The plain question, one answer per model, grouped by where Google Sheets and Workday Adaptive Planning stood in it.

Neither was named

12 of 12 modelsThe answer made no first choice from these two in this category.

Claude Haiku 4.5no first choice

GPT-5.4 miniDatarails alternatives: Abacum, Anaplan

Gemini 3.5 FlashMosaic, Pigment alternatives: Centage Planning Maestro, Cube, Datarails, Planful, Vena

Perplexity SonarDatarails, Drivetrain alternatives: Anaplan, Planful, Vena

Grok 4.1 FastCube, Datarails alternatives: Drivetrain, Limelight, Pigment, Vena

Mistral SmallDatarails, Limelight alternatives: Cube, Planful, Vena

DeepSeek V4 FlashLimelight alternatives: Abacum, Cube, Datarails, Pigment, Planful

Llama 4 MaverickDatarails alternatives: Abacum, Cube, Lucanet xP&A, Mosaic, Vena

Qwen 3.7 FlashCube alternatives: Fathom, Float, Jedox, Jirav, Planful, Vena

Kimi K2Datarails alternatives: Aleph, Centage Planning Maestro, Cube

GLM 4.7 FlashXDatarails alternatives: Cube, Drivetrain, Mosaic, Planful

MiniMax M2.5Datarails alternatives: Cube, Planful

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

Google Sheets leads by six points.

Google Sheets#8 of 13

Workday Adaptive Planning#– of 13

The full small business standing →

Mid-marketThe figures above

Google Sheets leads by nine points.

Google Sheets#2 of 8

Workday Adaptive Planning#7 of 8

The full mid-market standing →

Enterprise

The order flips: Workday Adaptive Planning leads at enterprise.

Workday Adaptive Planning#4 of 7

Google Sheets#– of 7

The full enterprise standing →

What the models said about Google Sheets

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

“Start with Google Sheets or Excel, and optionally use EasyFinancialModels for templates.” GLM 4.7 FlashX · budget prompt · first choice

“Microsoft Excel or Google Sheets remain the practical sweet spot” Claude Haiku 4.5 · budget prompt · first choice

“includes options like Google Sheets or Excel, which are free or low-cost” Llama 4 Maverick · budget prompt · first choice

What the models said about Workday Adaptive Planning

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

“You generally want to avoid enterprise-grade tools (like Anaplan or Workday) which cost thousands per month.” Qwen 3.7 Flash · budget prompt · hard negative

“I'd recommend Workday Adaptive Planning if you want the best balance of usability, guided finance workflows, and solid scenario modeling” Perplexity Sonar · paraphrase prompt · first choice

Also compared

Datarails vs Google Sheets Datarails vs Workday Adaptive Planning Google Sheets vs Cube Google Sheets vs Microsoft Excel Google Sheets vs Vena Google Sheets vs Planful Cube vs Workday Adaptive Planning Microsoft Excel vs Workday Adaptive Planning Vena vs Workday Adaptive Planning Planful vs Workday Adaptive Planning All twenty-one head-to-head pages and the full financial modeling standing

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.