GTM AI Index
Index Marketing Proximity › Radar vs PlotProjects
Retail, proximity and IoT marketing · September 2026 Edition

Radar vs PlotProjects

Five of twelve models named Radar first on the direct prompt; zero named PlotProjects. Radar was named by ten of the twelve models and PlotProjects by nine and Radar carries 30 labels and PlotProjects 20, so the shares are not directly comparable.

Radar

accepted challenger

New York City, United States, founded 2016. Named in one category this edition.

PlotProjects

accepted challenger

Named in one category this edition.

First-choice share19%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 8; printed, not drawn.
Labels3020A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Radar reading right to left. Rank and label count are printed, not drawn.Radar vs GroundTruth · Radar vs Foursquare · Radar vs Kontakt.io

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
RadarFirst choices, of twelve modelsPlotProjects
Direct50
Paraphrase30
Comparative10
Budget-constrained132 against Radar
Scale-constrained00
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Radar and PlotProjects were named in the same answer thirty-three times, of the 83 answers naming Radar and the 39 naming PlotProjects. In those answers PlotProjects took the first choice eight times and Radar five.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Radar and PlotProjects stood in it.
ModelDirectPLParaphrasePLComparativePLBudget-constrainedPLScale-constrainedPLNegativePL
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 FastPLPLPLPL
Mistral SmallPLPL
DeepSeek V4 FlashPLPL
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2PLPL
GLM 4.7 FlashXPLPL
MiniMax M2.5PLPL
RadarPL PlotProjects first choice named as an alternative argued againstblank: not namedEach cell is one answer, Radar on the left and PlotProjects on the right.

The direct prompt

The plain question, one answer per model, grouped by where Radar and PlotProjects stood in it.

Radar first, PlotProjects an alternative

5 of 12 modelsPlotProjects was named in the answer but not as the choice, or not at all.
Mistral SmallFoursquare, Radar alternatives: CleverTap, Insider, Kontakt.io, Kumulos, PlotProjects
Qwen 3.7 FlashRadar alternatives: Foursquare, Kontakt.io
Kimi K2Radar alternatives: AdRoll ABM, Phunware, PlotProjects
GLM 4.7 FlashXRadar alternatives: InMarket
MiniMax M2.5Radar

Neither was the first choice, one was named

3 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashUniqode alternatives: Purple, Radar
Grok 4.1 FastCleverTap alternatives: PlotProjects, Simpli.fi
DeepSeek V4 FlashGroundTruth, StackAdapt alternatives: Hey Sid, InZynk, Radar

Neither was named

4 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Gimbal, GroundTruth, Simpli.fi
GPT-5.4 miniFoursquare Proximity alternatives: 6sense, Demandbase
Perplexity SonarPropensity alternatives: Estimote, InMarket
Llama 4 MaverickInMarket alternatives: Apollo.io, Foursquare

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
PlotProjects leads by seven points.
PlotProjects10%#2 of 7
Radar2%#5 of 7
The full small business standing →
Mid-marketThe figures above
The order flips: Radar leads at mid-market.
Radar19%#1 of 8
PlotProjects6%#4 of 8
The full mid-market standing →
Enterprise
Radar leads by twenty-four points.
Radar24%#1 of 9
PlotProjects0%#– of 9
The full enterprise standing →

What the models said about Radar

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.

“Avoid: Enterprise tools like Kontakt.io, Radar ($500+/mo)” Grok 4.1 Fast · budget prompt · hard negative
“Quote-based ($0.02-0.04 per user) | Enterprise-grade but scales with usage” Kimi K2 · budget prompt · soft negative
“If you have to invest in just one approach, start with Radar. It is the most versatile geolocation platform for mid-market businesses.” Gemini 3.5 Flash · paraphrase prompt · first choice
“Often regarded as the industry-leading "Location OS," Radar is a developer-friendly platform providing highly accurate geofencing” Gemini 3.5 Flash · comparative prompt · first choice
“Radar appears to be a leading proximity marketing platform mentioned in the results” MiniMax M2.5 · direct prompt · first choice

What the models said about PlotProjects

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Start with PlotProjects (free tier) if you need full beacon and geofencing capabilities” Kimi K2 · budget prompt · first choice
“the best proximity marketing platform is likely PlotProjects' free Developer plan” Grok 4.1 Fast · budget prompt · first choice
“PlotProjects — Best Overall for Budget-Conscious Teams” GLM 4.7 FlashX · budget prompt · first choice
Also compared

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.