Radar vs Kontakt.io
One of fourteen models named Radar first on the direct prompt; one named Kontakt.io. Radar was named by eleven of the fourteen models and Kontakt.io by ten and Radar carries 29 labels and Kontakt.io 16, so the shares are not directly comparable.
New York City, United States, founded 2016. Named in two categories 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 retail, proximity and iot marketing page.
By framing
RadarFirst choices, of fourteen modelsKontakt.io
Across every category in the October 2026 Edition, Radar and Kontakt.io were named in the same answer twenty-one times, of the 102 answers naming Radar and the 46 naming Kontakt.io. In those answers Kontakt.io took the first choice one time and Radar nine.
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 | | | | | |
Radar Kontakt.io first choice named as an alternative argued againstblank: not namedEach cell is one answer, Radar on the left and Kontakt.io on the right.The direct prompt
Radar first, Kontakt.io an alternative
1 of 14 modelsKontakt.io was named in the answer but not as the choice, or not at all.Kontakt.io first, Radar not the choice
1 of 14 modelsRadar was named in the answer but not as the choice, or not at all.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
9 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
What the models said about Radar
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Best for: Sensor-fusion geofencing at scale ... Excellent for retailers and venues needing scalable proximity event ingestion and campaign trigger rules” Kimi K2 · comparative prompt · first choice
“I'd usually recommend Radar if you want one platform that can handle location-based triggers, geofencing, and in-store/proximity experiences” GPT-5.4 mini · paraphrase prompt · first choice
“Or Radar — if you already have an app and want precise geofencing, the $0.02/user cost gives you extreme flexibility.” DeepSeek V4 Flash · budget prompt · first choice
What the models said about Kontakt.io
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“"Complete Scam": Some reviewers explicitly label the vendor as a scam and report terrible customer service.” Qwen 3.7 Flash · negative prompt · hard negative
“can make them a poor fit if you want a simple plug-and-play marketing tool” Perplexity Sonar · negative prompt · soft negative
“Kontakt.io is the go-to for multi-location teams that want beacon-driven triggers with controlled deployment operations” Muse Glimmer 30B · scale prompt · first choice
“Kontakt.io is the most frequently recommended mid-market beacon platform for operational scale and API extensibility.” Muse Glimmer 30B · direct prompt · first choice
“Recommendation: Kontakt.io This is the industry leader for Proximity Marketing.” Qwen 3.7 Flash · paraphrase 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.