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Index › Finance › Planning and analysis › Corporate performance ma › Pigment vs Datarails
Corporate performance management · November 2026 Edition

Pigment vs Datarails

One of fourteen models named Pigment first on the direct prompt; zero named Datarails. Pigment was named by eleven of the fourteen models and Datarails by eight and Pigment carries 23 labels and Datarails 12, so the shares are not directly comparable.

Pigment

accepted challenger

Named in eight categories this edition.

Datarails

criticized challenger

Named in ten categories this edition.

First-choice share6%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate9%25%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#7A position in a field of 16; printed, not drawn.
Labels2312A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Pigment reading right to left. Rank and label count are printed, not drawn.Planful was named alongside these two in ten of the fourteen direct answers. Planful vs Pigment · Planful vs Datarails · Vena vs Pigment

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 corporate performance management page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PigmentFirst choices, of fourteen modelsDatarails
Direct10
Paraphrase10
Comparative001 against Datarails
Budget-constrained021 against Pigment
Scale-constrained101 against Datarails
Negative001 against Pigment · 1 against Datarails
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.

Across every category in the November 2026 Edition, Pigment and Datarails were named in the same answer 106 times, of the 230 answers naming Pigment and the 406 naming Datarails. In those answers Datarails took the first choice nine times and Pigment seventeen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Pigment 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
Pigment Datarails first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pigment on the left and Datarails on the right.

The direct prompt

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

Pigment first, Datarails not the choice

1 of 14 modelsDatarails was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashPigment alternatives: Cube, Mosaic, Planful, Vena, Workday Adaptive Planning

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Perplexity SonarPlanful alternatives: Drivetrain, Pigment, Prophix, Vena, Workday Adaptive Planning
DeepSeek V4 FlashPlanful alternatives: Datarails, Prophix, Vena, Workday Adaptive Planning
Kimi K2Cube, Vena alternatives: Datarails, Planful, Prophix
MiniMax M2.5Vena alternatives: OneStream, Pigment, Planful, Workday Adaptive Planning
GPT-6 LunaPlanful alternatives: Pigment, Vena, Workday Adaptive Planning

Neither was named

8 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 miniWorkday Adaptive Planning alternatives: Oracle Fusion Cloud EPM, Prophix
Grok 4.20Planful alternatives: Drivetrain, OneStream, Prophix
Mistral SmallAnaplan, Empower alternatives: Oracle Fusion Cloud EPM, SAP Analytics Cloud
Llama 4 Maverickno first choice
Qwen 3.7 FlashWorkday Adaptive Planning alternatives: Anaplan, Cube, OneStream, Planful
GLM 4.7 FlashXPlanful, Vena alternatives: Board, Oracle Fusion Cloud EPM, Workday Adaptive Planning
Muse Glimmer 30BPlanful, Vena alternatives: Prophix, deFacto Global

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
Datarails leads by five points.
Datarails5%#5 of 14
Pigment0%#11 of 14
The full small business standing →
Mid-marketThe figures above
The order flips: Pigment leads at mid-market.
Pigment6%#5 of 16
Datarails4%#7 of 16
The full mid-market standing →
Enterprise
Pigment leads by four points.
Pigment4%#– of 13
Datarails0%#– of 13
The full enterprise standing →

What the models said about Pigment

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

“Best For: Mid-market to upper mid-market B2B tech/SaaS businesses that need flexible multidimensional modeling.” Gemini 3.5 Flash · direct 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. One of one in this category shown.

“Choose tools like Cube, Datarails, or Centage, which can be configured internally or with minimal vendor onboarding support.” Gemini 3.5 Flash · budget prompt · first choice
Also compared
Planful vs PigmentPlanful vs DatarailsVena vs PigmentVena vs DatarailsWorkday Adaptive Planning vs PigmentWorkday Adaptive Planning vs DatarailsCube vs PigmentCube vs DatarailsPigment vs JedoxPigment vs ProphixJedox vs DatarailsDatarails vs ProphixAll twenty-eight head-to-head pages and the full corporate performance ma 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.