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Corporate performance management · November 2026 Edition

Vena vs Jedox

Four of fourteen models named Vena first on the direct prompt; zero named Jedox. Vena was named by thirteen of the fourteen models and Jedox by seven and Vena carries 40 labels and Jedox 10, so the shares are not directly comparable.

Vena

accepted challenger

Named in ten categories this edition.

Jedox

accepted challenger

Named in four categories this edition.

First-choice share12%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate12%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#6A position in a field of 16; printed, not drawn.
Labels4010A 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 ten of the fourteen direct answers. Planful vs Vena · Planful vs Jedox · Vena vs Workday Adaptive Planning

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.
VenaFirst choices, of fourteen modelsJedox
Direct401 against Vena
Paraphrase20
Comparative00
Budget-constrained032 against Vena
Scale-constrained001 against Vena
Negative001 against Vena · 1 against Jedox
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 Vena and Jedox 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
Vena Jedox first choice named as an alternative argued againstblank: not namedEach cell is one answer, Vena on the left and Jedox on the right.

The direct prompt

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

Vena first, Jedox not the choice

4 of 14 modelsJedox was named in the answer but not as the choice, or not at all.
Kimi K2Cube, Vena alternatives: Datarails, Planful, Prophix
GLM 4.7 FlashXPlanful, Vena alternatives: Board, Oracle Fusion Cloud EPM, Workday Adaptive Planning
MiniMax M2.5Vena alternatives: OneStream, Pigment, Planful, 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
Perplexity SonarPlanful alternatives: Drivetrain, Pigment, Prophix, Vena, Workday Adaptive Planning
DeepSeek V4 FlashPlanful alternatives: Datarails, Prophix, Vena, Workday Adaptive Planning
GPT-6 LunaPlanful alternatives: Pigment, Vena, Workday Adaptive Planning

Neither was named

6 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

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
Vena leads by eleven points.
Vena13%#2 of 14
Jedox2%#– of 14
The full small business standing →
Mid-marketThe figures above
Vena leads by six points.
Vena12%#2 of 16
Jedox6%#6 of 16
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Vena0%#10 of 13
Jedox0%#– of 13
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.

“Options to AVOID on a Limited Budget ... | Vena | ~$50K–$150K/year” DeepSeek V4 Flash · budget prompt · hard negative
“Choose Vena if adoption speed and Excel familiarity are critical and you are Microsoft-centric; it leads on operational planning and ease of implementation” Muse Glimmer 30B · direct prompt · first choice
“Frequently recognized as a leader in EPM for mid-market, known for its Excel-like interface and strong financial planning capabilities” MiniMax M2.5 · direct prompt · first choice
“Choose Vena if you need fast adoption, Excel continuity, and a finance-owned planning/consolidation with low change management.” Muse Glimmer 30B · paraphrase prompt · first choice

What the models said about Jedox

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

“Perform especially rigorous reference checks, security reviews, roadmap validation, and integration testing.” Perplexity Sonar · negative prompt · soft negative
“Jedox is the strongest EPM shortlist candidate” Perplexity Sonar · budget prompt · first choice
“🥇 Top Pick: Jedox (~$160–$500/user/year)” DeepSeek V4 Flash · budget prompt · first choice
“the best EPM choice is usually Jedox” GLM 4.7 FlashX · 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.