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Financial modeling and scenario planning · October 2026 Edition

Datarails vs Google Sheets

Six of fourteen models named Datarails first on the direct prompt; zero named Google Sheets. Datarails was named by thirteen of the fourteen models and Google Sheets by twelve and Datarails carries 20 labels and Google Sheets 13, so the shares are not directly comparable.

Datarails

accepted challenger

Named in eight categories this edition.

Google Sheets

accepted challenger

Named in fifteen categories this edition.

First-choice share11%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#5A position in a field of 14; printed, not drawn.
Labels2013A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Datarails reading right to left. Rank and label count are printed, not drawn.Cube was named alongside these two in nine of the fourteen direct answers. Microsoft Excel vs Datarails · Microsoft Excel vs Google Sheets · Cube vs Datarails

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.
DatarailsFirst choices, of fourteen modelsGoogle Sheets
Direct60
Paraphrase00
Comparative00
Budget-constrained06
Scale-constrained10
Negative002 against Google Sheets
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 Datarails and Google Sheets 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
GPT-6 Luna
Muse Glimmer 30B
Datarails Google Sheets first choice named as an alternative argued againstblank: not namedEach cell is one answer, Datarails on the left and Google Sheets on the right.

The direct prompt

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

Datarails first, Google Sheets not the choice

6 of 14 modelsGoogle Sheets was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Datarails alternatives: Cube, Drivetrain, Planful, Prophix One
GPT-5.4 miniDatarails, Planful alternatives: Anaplan, Vena
Perplexity SonarDatarails, Limelight alternatives: Abacum, Workday Adaptive Planning
Grok 4.1 FastCube, Datarails, Drivetrain, Jirav alternatives: Abacum
GLM 4.7 FlashXDatarails alternatives: Abacum, Cube, Lucanet xP&A, Mosaic
Muse Glimmer 30BDatarails alternatives: Cube, Drivetrain, Mosaic, Planful

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.
Mistral SmallAbacum alternatives: Cube, Datarails
DeepSeek V4 FlashCube alternatives: Datarails, Jirav, Planful, Vena
MiniMax M2.5Drivetrain alternatives: Abacum, Cube, Datarails, Pigment

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashAleph, Cube alternatives: Abacum, Bob Finance, Pigment, Vena
Llama 4 Maverickno first choice
Qwen 3.7 FlashVena, Workday Adaptive Planning alternatives: Fathom, PlanGuru
Kimi K2Drivetrain alternatives: Mosaic, Pigment
GPT-6 LunaWorkday Adaptive Planning alternatives: Cube, 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
Google Sheets leads by fifteen points.
Google Sheets19%#1 of 10
Datarails3%#6 of 10
The full small business standing →
Mid-marketThe figures above
The order flips: Datarails leads at mid-market.
Datarails11%#4 of 14
Google Sheets9%#5 of 14
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Datarails0%#– of 12
Google Sheets0%#– of 12
The full enterprise standing →

What the models said about Datarails

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

“I would choose Datarails for an Excel-heavy mid-market B2B finance team, and Limelight for a team that wants a more modern standalone modeling platform” Perplexity Sonar · direct prompt · first choice
“"For most mid-market finance teams, especially those managing multiple entities in Excel, Datarails is the best financial modeling software in 2026."” Muse Glimmer 30B · direct prompt · first choice
“For most mid-market finance teams, especially those managing multiple entities in Excel, Datarails is the best financial modeling software in 2026.” Claude Haiku 4.5 · direct prompt · first choice

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.

“starting with Google Sheets or Excel and upgrading to a dedicated tool ... is the most cost-effective approach” Kimi K2 · budget prompt · first choice
“Best low-budget choice: Google Sheets for a small company building budgets, forecasts, and scenario models” GPT-6 Luna · budget prompt · first choice
“start with Microsoft Excel or Google Sheets if you need a simple, flexible solution” Mistral Small · budget 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.