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Corporate performance management · November 2026 Edition

Planful vs Datarails

Six of fourteen models named Planful first on the direct prompt; zero named Datarails. Planful was named by fourteen of the fourteen models and Datarails by eight and Planful carries 42 labels and Datarails 12, so the shares are not directly comparable.

Planful

accepted challenger

Named in ten categories this edition.

Datarails

criticized challenger

Named in ten categories this edition.

First-choice share27%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate5%25%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#7A position in a field of 16; printed, not drawn.
Labels4212A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Planful reading right to left. Rank and label count are printed, not drawn.Workday Adaptive Planning was named alongside these two in eight of the fourteen direct answers. Planful vs Vena · Planful vs Workday Adaptive Planning · Planful vs Cube

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.
PlanfulFirst choices, of fourteen modelsDatarails
Direct60
Paraphrase60
Comparative001 against Datarails
Budget-constrained122 against Planful
Scale-constrained001 against Datarails
Negative001 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, Planful and Datarails were named in the same answer 150 times, of the 402 answers naming Planful and the 406 naming Datarails. In those answers Datarails took the first choice twenty times and Planful ten.

Every model, every framing

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

The direct prompt

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

Planful first, Datarails an alternative

6 of 14 modelsDatarails was named in the answer but not as the choice, or not at all.
Perplexity SonarPlanful alternatives: Drivetrain, Pigment, Prophix, Vena, Workday Adaptive Planning
Grok 4.20Planful alternatives: Drivetrain, OneStream, Prophix
DeepSeek V4 FlashPlanful alternatives: Datarails, Prophix, Vena, Workday Adaptive Planning
GLM 4.7 FlashXPlanful, Vena alternatives: Board, Oracle Fusion Cloud EPM, Workday Adaptive Planning
GPT-6 LunaPlanful alternatives: Pigment, Vena, Workday Adaptive Planning
Muse Glimmer 30BPlanful, Vena alternatives: Prophix, deFacto Global

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashPigment alternatives: Cube, Mosaic, Planful, Vena, Workday Adaptive Planning
Qwen 3.7 FlashWorkday Adaptive Planning alternatives: Anaplan, Cube, OneStream, Planful
Kimi K2Cube, Vena alternatives: Datarails, Planful, Prophix
MiniMax M2.5Vena alternatives: OneStream, Pigment, Planful, Workday Adaptive Planning

Neither was named

4 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
Mistral SmallAnaplan, Empower alternatives: Oracle Fusion Cloud EPM, SAP Analytics Cloud
Llama 4 Maverickno first choice

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 three points.
Datarails5%#5 of 14
Planful2%#8 of 14
The full small business standing →
Mid-marketThe figures above
The order flips: Planful leads at mid-market.
Planful27%#1 of 16
Datarails4%#7 of 16
The full mid-market standing →
Enterprise
Planful leads by four points.
Planful4%#5 of 13
Datarails0%#– of 13
The full enterprise standing →

What the models said about Planful

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

“Options to AVOID on a Limited Budget ... | Planful | ~$60K–$250K+/year” DeepSeek V4 Flash · budget prompt · hard negative
“Choose Planful if you want a browser-based, structured platform with strong multi-entity consolidation, financial close and rolling forecasts.” Muse Glimmer 30B · direct prompt · first choice
“Choose Planful if you want a unified planning, reporting and consolidation platform with stronger native modeling and self-service” Muse Glimmer 30B · paraphrase 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

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.