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Value selling tools · November 2026 Edition

ValueCore vs ValueNavigator

Six of fourteen models named ValueCore first on the direct prompt; zero named ValueNavigator. ValueCore was named by twelve of the fourteen models and ValueNavigator by nine and ValueCore carries 27 labels and ValueNavigator 10, so the shares are not directly comparable.

ValueCore

accepted challenger

Named in one category this edition.

ValueNavigator

accepted challenger

Named in one category this edition.

First-choice share13%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate4%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 13; printed, not drawn.
Labels2710A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, ValueCore reading right to left. Rank and label count are printed, not drawn.ValueCore vs Minoa · ValueCore vs The ROI Shop · ValueCore vs Cuvama

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 value selling tools page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ValueCoreFirst choices, of fourteen modelsValueNavigator
Direct60
Paraphrase13
Comparative10
Budget-constrained001 against ValueCore
Scale-constrained01
Negative00
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 ValueCore and ValueNavigator stood in it.
ModelDirectVAVAParaphraseVAVAComparativeVAVABudget-constrainedVAVAScale-constrainedVAVANegativeVAVA
Claude Haiku 4.5VA
GPT-5.4 mini
Gemini 3.5 FlashVAVA
Perplexity SonarVAVAVA
Grok 4.20VAVAVAVA
Mistral SmallVAVA
DeepSeek V4 FlashVAVAVAVA
Llama 4 MaverickVA
Qwen 3.7 Flash
Kimi K2VAVA
GLM 4.7 FlashXVAVAVA
MiniMax M2.5VA
GPT-6 LunaVA
Muse Glimmer 30BVAVAVA
VA ValueCoreVA ValueNavigatorVA first choiceVA named as an alternativeVA argued againstblank: not namedEach cell is one answer, ValueCore on the left and ValueNavigator on the right.

The direct prompt

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

ValueCore first, ValueNavigator not the choice

6 of 14 modelsValueNavigator was named in the answer but not as the choice, or not at all.
Perplexity SonarValueCore alternatives: Consensus, Cuvama, HubSpot Sales Hub
Grok 4.20ValueCore alternatives: DecisionLink, Ecosystems, Mediafly Value, Minoa, Zebrafi
Mistral SmallValueCore alternatives: Consensus, Mediafly
DeepSeek V4 FlashValueCore alternatives: Symbe
Llama 4 MaverickValueCore
GLM 4.7 FlashXValueCore alternatives: Cuvama, DealHub, HubSpot Sales Hub, Minoa, Symbe, ValueVisualizer

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.
Gemini 3.5 FlashMinoa alternatives: Mediafly, Shark Finesse, The ROI Shop, ValueCore
MiniMax M2.5Ecosystems, ValueSelling Framework alternatives: HubSpot Sales Hub, ValueNavigator, Zebrafi
Muse Glimmer 30BMinoa alternatives: Ecosystems, The ROI Shop, ValueCore

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 minino first choice
Qwen 3.7 FlashWinWin Solutions alternatives: Outmatch, PandaDoc, Proposify, Seismic
Kimi K2Mediafly Value, Zebrafi alternatives: Cuvama
GPT-6 LunaZoho CRM alternatives: HubSpot Sales Hub

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
Level: the same share of first choices.
ValueCore4%#6 of 13
ValueNavigator4%#7 of 13
The full small business standing →
Mid-marketThe figures above
ValueCore leads by six points.
ValueCore13%#1 of 13
ValueNavigator8%#4 of 13
The full mid-market standing →
Enterprise
Level: the same share of first choices.
ValueCore2%#5 of 13
ValueNavigator2%#9 of 13
The full enterprise standing →

What the models said about ValueCore

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

“ValueCore | Not published | Requires scheduling a call just to get pricing” DeepSeek V4 Flash · budget prompt · hard negative
“I recommend ValueCore (formerly VisualizeROI) as the top choice for an ROI calculator and business case platform.” Grok 4.20 · paraphrase prompt · first choice
“ValueCore is widely recognized as the best value selling tool specifically for mid-market B2B companies” Mistral Small · direct prompt · first choice
“For most mid-market B2B companies, ValueCore (formerly VisualizeROI) is the best value-selling tool.” GLM 4.7 FlashX · direct prompt · first choice

What the models said about ValueNavigator

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

“If you want reps to own ROI creation with light oversight: The ROI Shop / ValueNavigator — strong governance with field usability.” Muse Glimmer 30B · paraphrase prompt · first choice
“I'd recommend starting with ValueNavigator or LeveragePoint if you need the most defensible business cases” Claude Haiku 4.5 · paraphrase prompt · first choice
“The market leaders in this space right now are Mediafly, Cuvama, and ValueNavigator (ROI Selling)” DeepSeek V4 Flash · scale 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.