Genuity vs Sakon
Zero of fourteen models named Genuity first on the direct prompt; zero named Sakon. Genuity was named by nine of the fourteen models and Sakon by ten and Genuity carries 15 labels and Sakon 22, 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 modelsSakon
Across every category in the November 2026 Edition, Genuity and Sakon were named in the same answer fourteen times, of the 52 answers naming Genuity and the 88 naming Sakon. In those answers Sakon took the first choice zero times and Genuity seven.
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 4.20 | | | | | | |
| 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 Sakon first choice named as an alternative argued againstblank: not namedEach cell is one answer, Genuity on the left and Sakon on the right.The direct prompt
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.Neither was named
11 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
Genuity35%#1 of 15Sakon0%#13 of 15Sakon6%#2 of 12Genuity0%#– of 12What 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.
“Reasonable for smaller organizations, but likely to be outgrown by companies with complex, large-scale telecom environments.” Perplexity Sonar · negative prompt · soft negative
“Genuity stands out as the best telecom expense management (TEM) platform for a company with a limited budget” Grok 4.20 · budget prompt · first choice
“the best telecom expense management platform for a company with a limited budget is Genuity” Claude Haiku 4.5 · budget prompt · first choice
“Top Recommendation: Genuity ... most budget-friendly comprehensive TEM solution” GLM 4.7 FlashX · budget prompt · first choice
What the models said about Sakon
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Another platform with a high negative rating (80%), Sakon is criticized for similar reasons, including outdated systems and poor user experiences.” Mistral Small · negative prompt · hard negative
“Sakon lacks an automated RFP process or contract rebidding... users report manual validation work to keep data accurate at a high cost” Claude Haiku 4.5 · negative prompt · soft negative
“Also flagged as a "criticized challenger" in some recent comparative evaluations” Kimi K2 · negative prompt · soft negative
“prioritize modern, API-driven vendors (such as *Brightfin, Sakon, vCom, or Calero*)” Gemini 3.5 Flash · scale prompt · first choice
“Global enterprise focus. Enterprise grade TEM with strong global carrier coverage and multi-country compliance.” Muse Glimmer 30B · comparative prompt · alternative
“Choose Sakon if managing mobile device ordering, logistics, and mobile fleets is your top priority.” Gemini 3.5 Flash · comparative prompt · alternative
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.