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Financial modeling and scenario planning · September 2026 Edition

Cube vs Workday Adaptive Planning

Two of twelve models named Cube first on the direct prompt; zero named Workday Adaptive Planning. Cube was named by nine of the twelve models and Workday Adaptive Planning by eleven and Cube carries 18 labels and Workday Adaptive Planning 17, so the shares are not directly comparable.

Cube

accepted challenger

Named in nine categories this edition.

Workday Adaptive Planning

criticized challenger

Named in seven categories this edition.

First-choice share11%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#3#7A position in a field of 8; printed, not drawn.
Labels1817A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Cube 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 Cube · 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.
CubeFirst choices, of twelve modelsWorkday Adaptive Planning
Direct20
Paraphrase321 against Workday Adaptive Planning
Comparative00
Budget-constrained003 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.

Across every category in the September 2026 Edition, Cube and Workday Adaptive Planning were named in the same answer 130 times, of the 311 answers naming Cube and the 386 naming Workday Adaptive Planning. In those answers Workday Adaptive Planning took the first choice zero times and Cube nineteen.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Cube and Workday Adaptive Planning stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Cube Workday Adaptive Planning first choice named as an alternative argued againstblank: not namedEach cell is one answer, Cube on the left and Workday Adaptive Planning on the right.

The direct prompt

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

Cube first, Workday Adaptive Planning not the choice

2 of 12 modelsWorkday Adaptive Planning was named in the answer but not as the choice, or not at all.
Grok 4.1 FastCube, Datarails alternatives: Drivetrain, Limelight, Pigment, Vena
Qwen 3.7 FlashCube alternatives: Fathom, Float, Jedox, Jirav, Planful, Vena

Neither was the first choice, one was named

7 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashMosaic, Pigment alternatives: Centage Planning Maestro, Cube, Datarails, Planful, 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
Kimi K2Datarails alternatives: Aleph, Centage Planning Maestro, Cube
GLM 4.7 FlashXDatarails alternatives: Cube, Drivetrain, Mosaic, Planful
MiniMax M2.5Datarails alternatives: Cube, Planful

Neither was named

3 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
Perplexity SonarDatarails, Drivetrain alternatives: Anaplan, 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
Cube leads by six points.
Cube6%#4 of 13
Workday Adaptive Planning0%#– of 13
The full small business standing →
Mid-marketThe figures above
Cube leads by six points.
Cube11%#3 of 8
Workday Adaptive Planning4%#7 of 8
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Cube3%#– of 7
Workday Adaptive Planning3%#4 of 7
The full enterprise standing →

What the models said about Cube

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

“Cube: This is currently one of the fastest-growing tools in this space.... | Loves Excel and hates change | Cube |” Qwen 3.7 Flash · direct prompt · first choice
“For a general mid-sized B2B company, Cube gives the best cost/value balance and adoption ease.” DeepSeek V4 Flash · paraphrase 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

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.