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Index › Spend and procurement › Telecom expense mgmt › Genuity vs Mindglobal
Telecom expense management · October 2026 Edition

Genuity vs Mindglobal

Zero of fourteen models named Genuity first on the direct prompt; one named Mindglobal. Genuity was named by nine of the fourteen models and Mindglobal by seven and Genuity carries 14 labels and Mindglobal 13, so the shares are not directly comparable.

Genuity

accepted challenger

Named in one category this edition.

Mindglobal

accepted challenger

Named in one category this edition.

First-choice share19%10%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 8; printed, not drawn.
Labels1413A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Genuity reading right to left. Rank and label count are printed, not drawn.vCom Solutions was named alongside these two in eight of the fourteen direct answers. Genuity vs Lightyear · Genuity vs vCom Solutions · Genuity vs Calero

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 telecom expense 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.
GenuityFirst choices, of fourteen modelsMindglobal
Direct01
Paraphrase03
Comparative00
Budget-constrained90
Scale-constrained01
Negative001 against Genuity
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 Genuity and Mindglobal 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
Genuity Mindglobal first choice named as an alternative argued againstblank: not namedEach cell is one answer, Genuity on the left and Mindglobal on the right.

The direct prompt

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

Mindglobal first, Genuity not the choice

1 of 14 modelsGenuity was named in the answer but not as the choice, or not at all.
DeepSeek V4 FlashMindglobal, vCom Solutions alternatives: Cimpl, Lightyear, Valicom

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 FlashLightyear alternatives: Brightfin, Mindglobal, vCom Solutions, vManager
Grok 4.1 FastSakon, vCom Solutions alternatives: Calero, Genuity, Valicom
GPT-6 LunaClearview alternatives: Brightfin, Genuity
Muse Glimmer 30BLightyear alternatives: Brightfin, Calero, Mindglobal, vCom Solutions

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Lightyear alternatives: Cimpl, Tangoe One Telecom, Valicom's Clearview, vCom Solutions
GPT-5.4 miniCalero alternatives: Sakon, Tangoe One Telecom, Valicom Clearview
Perplexity SonarBrightfin alternatives: Clearview, Lightyear, vCom Solutions
Mistral SmallClearSight TEMs, LINQ Qonnect
Llama 4 MaverickClearSight TEMs alternatives: LINQ Qonnect
Qwen 3.7 FlashBrightfin alternatives: RadiusPoint, vCom Solutions
Kimi K2Calero alternatives: Asignet, Tellennium, vCom Solutions
GLM 4.7 FlashXUpland Cimpl alternatives: Calero, Ginger Telecom, Lightyear, Stratix, Tangoe One Telecom
MiniMax M2.5no 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
Genuity leads by thirty-six points.
Genuity36%#1 of 9
Mindglobal0%#– of 9
The full small business standing →
Mid-marketThe figures above
Genuity leads by eight points.
Genuity19%#1 of 8
Mindglobal10%#4 of 8
The full mid-market standing →
Enterprise
Genuity leads by two points.
Genuity2%#– of 10
Mindglobal0%#– of 10
The full enterprise standing →

What the models said about Genuity

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

“Major red flag: Unexpected 400% price increases — users report being shocked by sudden massive subscription cost hikes” Kimi K2 · negative prompt · hard negative
“Best ultra-low-budget transparent option: Genuity ... it's the only major TEM option with transparent, accessible pricing” Muse Glimmer 30B · budget prompt · first choice
“Genuity stands out as the best telecom expense management (TEM) platform for companies with a limited budget” Mistral Small · budget prompt · first choice
“Genuity is easily the most budget-friendly dedicated technology business management platform on the market.” Gemini 3.5 Flash · budget prompt · first choice

What the models said about Mindglobal

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

“vCom Solutions / Tellennium / Mindglobal: Highly regarded for providing a strong managed service layer specifically tailored to mid-market organizations” Gemini 3.5 Flash · scale prompt · first choice
“MindGlobal and vCom are the strongest all-around choices” DeepSeek V4 Flash · direct prompt · first choice
“I recommend MindGlobal as the top choice” GLM 4.7 FlashX · 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.