# Lightyear vs Calero: which do AI models recommend for telecom expense mgmt, October 2026

Finance AI Recommendation Index, October 2026 Edition, Telecom expense management. Three of fourteen models named Lightyear first on the direct prompt; two named Calero. Page: https://finance-ai-index.com/spend/telecom-expense-management/lightyear-vs-calero/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Lightyear | 12% | #2 of 8 | 3% | 30 | 13 of 14 |
| Calero | 6% | #5 of 8 | 39% | 46 | 14 of 14 |

## The direct prompt, model by model

- Claude Haiku 4.5: lightyear first (first choices: Lightyear) (alternatives: Cimpl, Tangoe One Telecom, Valicom's Clearview, vCom Solutions)
- Gemini 3.5 Flash: lightyear first (first choices: Lightyear) (alternatives: Brightfin, Mindglobal, vCom Solutions, vManager)
- Muse Glimmer 30B: lightyear first (first choices: Lightyear) (alternatives: Brightfin, Calero, Mindglobal, vCom Solutions)
- GPT-5.4 mini: calero first (first choices: Calero) (alternatives: Sakon, Tangoe One Telecom, Valicom Clearview)
- Kimi K2: calero first (first choices: Calero) (alternatives: Asignet, Tellennium, vCom Solutions)
- Perplexity Sonar: neither first, one named (first choices: Brightfin) (alternatives: Clearview, Lightyear, vCom Solutions)
- Grok 4.1 Fast: neither first, one named (first choices: Sakon, vCom Solutions) (alternatives: Calero, Genuity, Valicom)
- DeepSeek V4 Flash: neither first, one named (first choices: Mindglobal, vCom Solutions) (alternatives: Cimpl, Lightyear, Valicom)
- GLM 4.7 FlashX: neither first, one named (first choices: Upland Cimpl) (alternatives: Calero, Ginger Telecom, Lightyear, Stratix, Tangoe One Telecom)
- Mistral Small: neither named (first choices: ClearSight TEMs, LINQ Qonnect)
- Llama 4 Maverick: neither named (first choices: ClearSight TEMs) (alternatives: LINQ Qonnect)
- Qwen 3.7 Flash: neither named (first choices: Brightfin) (alternatives: RadiusPoint, vCom Solutions)
- MiniMax M2.5: neither named
- GPT-6 Luna: neither named (first choices: Clearview) (alternatives: Brightfin, Genuity)

## What the models said about Lightyear

- "it has far fewer reviews, so I'd treat it as a newer option rather than the safest default" (GPT-5.4 mini, direct prompt, soft negative)
- "Highly rated for mid-market and SMB companies, offering robust telecom expense management (TEM) with low service tiers that make it accessible for mid-sized businesses." (Mistral Small, paraphrase prompt, first choice)
- "I would recommend Lightyear if you want a TEM/wireless expense management platform that is explicitly positioned for *SMB and mid-market* organizations" (Perplexity Sonar, paraphrase prompt, first choice)
- "Lightyear is a strong choice because it connects procurement, inventory tracking, and expense management in a unified platform" (Claude Haiku 4.5, direct prompt, first choice)

## What the models said about Calero

- "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)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.