Genuity vs Lightyear
Zero of fourteen models named Genuity first on the direct prompt; three named Lightyear. Genuity was named by nine of the fourteen models and Lightyear by thirteen and Genuity carries 14 labels and Lightyear 30, 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
GenuityFirst choices, of fourteen modelsLightyear
Across every category in the October 2026 Edition, Genuity and Lightyear were named in the same answer twenty-seven times, of the 62 answers naming Genuity and the 71 naming Lightyear. In those answers Lightyear took the first choice seven times and Genuity thirteen.
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 | | |
Genuity Lightyear first choice named as an alternative argued againstblank: not namedEach cell is one answer, Genuity on the left and Lightyear on the right.The direct prompt
Lightyear first, Genuity not the choice
3 of 14 modelsGenuity was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 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
Genuity36%#1 of 9Lightyear12%#2 of 9Lightyear4%#7 of 10Genuity2%#– of 10What 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 Lightyear
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“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
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.