Mindglobal vs Calero
One of fourteen models named Mindglobal first on the direct prompt; two named Calero. Mindglobal was named by seven of the fourteen models and Calero by fourteen and Mindglobal carries 13 labels and Calero 46, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
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
MindglobalFirst choices, of fourteen modelsCalero
Across every category in the October 2026 Edition, Mindglobal and Calero were named in the same answer twenty-one times, of the 25 answers naming Mindglobal and the 132 naming Calero. In those answers Calero took the first choice one time and Mindglobal three.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| 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 | | | |
Mindglobal Calero first choice named as an alternative argued againstblank: not namedEach cell is one answer, Mindglobal on the left and Calero on the right.The direct prompt
Mindglobal first, Calero not the choice
1 of 14 modelsCalero was named in the answer but not as the choice, or not at all.Calero first, Mindglobal not the choice
2 of 14 modelsMindglobal was named in the answer but not as the choice, or not at all.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.Neither was named
7 of 14 modelsThe answer made no first choice from these two in this category.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
Mindglobal10%#4 of 8Calero6%#5 of 8Calero12%#2 of 10Mindglobal0%#– of 10What 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
What the models said about Calero
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Users report slow data reporting, delayed updates when requesting changes, and poor customer support” Kimi K2 · negative prompt · hard negative
“What to Avoid for Mid-Market” DeepSeek V4 Flash · direct prompt · hard negative
“Traditional giants like Tangoe or Calero are often built for massive enterprises ... making them overkill and too expensive for smaller setups” Qwen 3.7 Flash · paraphrase prompt · soft negative
“Top picks: Calero (unified IT/TEM), Tangoe (enterprise-grade), Cimpl (broad tech mgmt), Lightyear (AI-heavy), Temforce (workflow-focused).” Grok 4.1 Fast · scale prompt · first choice
“Choose Calero or Tangoe if you are a large enterprise managing thousands of devices and complex contracts” GLM 4.7 FlashX · comparative prompt · first choice
“Start with Tangoe, Calero, Sakon, and Cimpl for a broad enterprise TEM comparison” GPT-6 Luna · comparative prompt · first choice
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.