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FP&A platforms · October 2026 Edition

Vena vs Drivetrain

Four of fourteen models named Vena first on the direct prompt; two named Drivetrain. Vena was named by fourteen of the fourteen models and Drivetrain by nine and Vena carries 60 labels and Drivetrain 15, so the shares are not directly comparable.

Vena

accepted challenger

Named in nine categories this edition.

Drivetrain

accepted challenger

Named in five categories this edition.

First-choice share13%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate20%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 18; printed, not drawn.
Labels6015A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Vena reading right to left. Rank and label count are printed, not drawn.Planful was named alongside these two in nine of the fourteen direct answers. Planful vs Vena · Planful vs Drivetrain · Fathom vs Vena

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 FP&A platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
VenaFirst choices, of fourteen modelsDrivetrain
Direct42
Paraphrase21
Comparative00
Budget-constrained003 against Vena
Scale-constrained10
Negative009 against Vena
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 October 2026 Edition, Vena and Drivetrain were named in the same answer fifty-nine times, of the 398 answers naming Vena and the 103 naming Drivetrain. In those answers Drivetrain took the first choice six times and Vena six.

Every model, every framing

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

The direct prompt

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

Vena first, Drivetrain an alternative

4 of 14 modelsDrivetrain was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashVena alternatives: Aleph, Bob Finance, Cube, Pigment, Planful
Grok 4.1 FastVena alternatives: Centage, Cube, Datarails, Planful
Qwen 3.7 FlashVena alternatives: Aleph, Cube, Planful
MiniMax M2.5Vena alternatives: Drivetrain, Parse

Drivetrain first, Vena an alternative

2 of 14 modelsVena was named in the answer but not as the choice, or not at all.
DeepSeek V4 FlashDatarails, Drivetrain alternatives: Cube, Jirav, Planful, Prophix, Workday Adaptive Planning
GLM 4.7 FlashXAbacum, Drivetrain alternatives: Cube, Vena

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniPlanful alternatives: Pigment, Vena
Perplexity SonarAbacum, Planful alternatives: Vena, Workday Adaptive Planning
Mistral SmallCentage alternatives: Abacum, Drivetrain, Planful, Prophix, Vena
Kimi K2Cube alternatives: Aleph, Datarails, Drivetrain, Jirav, Vena
GPT-6 LunaAbacum alternatives: Planful, Vena
Muse Glimmer 30BPigment alternatives: Abacum, Aleph, Centage, Cube, Datarails, Planful, Vena

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Aleph, Cube alternatives: Pigment, Prophix, Workday Adaptive Planning
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
Drivetrain leads by two points.
Drivetrain4%#– of 16
Vena2%#10 of 16
The full small business standing →
Mid-marketThe figures above
The order flips: Vena leads at mid-market.
Vena13%#3 of 18
Drivetrain6%#6 of 18
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Vena0%#10 of 14
Drivetrain0%#11 of 14
The full enterprise standing →

What the models said about Vena

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

“Strong negative quotes (e.g., "would rather eat a bullet than use it again"); 16-58% negative labels” Grok 4.1 Fast · negative prompt · hard negative
“Options to Avoid on a Limited Budget ... Vena — Median contract ~$34,000/year” DeepSeek V4 Flash · budget prompt · hard negative
“#1 in G2 Mid-Market Grid for Budgeting/Forecasting & Financial Analysis (Summer 2024)... For comprehensive planning: Vena or Planful (top G2 mid-market leaders).” Grok 4.1 Fast · direct prompt · first choice
“Best For: Mid-market companies ($50M–$500M+ revenue) that are deeply committed to Excel and need enterprise-grade security and workflows.” Gemini 3.5 Flash · direct prompt · first choice

What the models said about Drivetrain

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

“Drivetrain and Abacum stand out for their advanced features, scalability, and ease of implementation” Mistral Small · 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.