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Brand protection platforms · November 2026 Edition

IP Moat vs MarqVision

One of fourteen models named IP Moat first on the direct prompt; zero named MarqVision. IP Moat was named by twelve of the fourteen models and MarqVision by eleven and IP Moat carries 14 labels and MarqVision 15, so the shares are not directly comparable.

IP Moat

accepted challenger

Named in one category this edition.

MarqVision

accepted challenger

Named in one category this edition.

First-choice share23%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#6A position in a field of 11; printed, not drawn.
Labels1415A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, IP Moat reading right to left. Rank and label count are printed, not drawn.BrandShield was named alongside these two in eleven of the fourteen direct answers. IP Moat vs BrandShield · IP Moat vs Red Points · IP Moat vs Bolster AI

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 brand protection platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
IP MoatFirst choices, of fourteen modelsMarqVision
Direct10
Paraphrase00
Comparative01
Budget-constrained101
Scale-constrained00
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the November 2026 Edition, IP Moat and MarqVision were named in the same answer thirteen times, of the 50 answers naming IP Moat and the 50 naming MarqVision. In those answers MarqVision took the first choice zero times and IP Moat nine.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where IP Moat and MarqVision stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
IP Moat MarqVision first choice named as an alternative argued againstblank: not namedEach cell is one answer, IP Moat on the left and MarqVision on the right.

The direct prompt

The plain question, one answer per model, grouped by where IP Moat and MarqVision stood in it.

IP Moat first, MarqVision not the choice

1 of 14 modelsMarqVision was named in the answer but not as the choice, or not at all.
Qwen 3.7 FlashIP Moat, Red Points alternatives: Bolster

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.
Claude Haiku 4.5Bolster alternatives: MarqVision, Podqi, Red Points
Perplexity SonarBolster alternatives: BrandShield, Corsearch, MarqVision, Red Points
Grok 4.20Red Points alternatives: Bolster AI, BrandShield, Cyble, MarqVision

Neither was named

10 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniRed Points alternatives: BrandShield, CTM360, Fortra Brand Protection
Gemini 3.5 FlashBrandShield alternatives: Bolster, Corsearch, Red Points, ZeroFox
Mistral SmallBolster AI alternatives: BrandShield, Cyble, Red Points, ZeroFox Brand Protection
DeepSeek V4 FlashBolster AI alternatives: BrandShield, Red Points
Llama 4 MaverickBrandShield alternatives: Bolster, Netcraft Domain Protection, ZeroFox Brand Protection
Kimi K2BrandShield, Red Points alternatives: Bolster AI, ZeroFox
GLM 4.7 FlashXBolster AI, BrandShield alternatives: ZeroFox
MiniMax M2.5Bolster AI alternatives: BrandShield
GPT-6 LunaBolster alternatives: Doppel, Fortra Brand Protection, Netcraft
Muse Glimmer 30BBrandShield alternatives: Bolster AI, Red Points

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
IP Moat leads by thirty points.
IP Moat32%#1 of 11
MarqVision2%#6 of 11
The full small business standing →
Mid-marketThe figures above
IP Moat leads by twenty-one points.
IP Moat23%#1 of 11
MarqVision2%#6 of 11
The full mid-market standing →
Enterprise
IP Moat is not named for this buyer.
IP Moat—not named
MarqVision0%#10 of 10
The full enterprise standing →

What the models said about IP Moat

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

“IP Moat looks like the strongest value pick I found because its pricing starts at $105/month” GPT-5.4 mini · budget prompt · first choice
“IP Moat stands out as the best brand protection platform for companies with a limited budget.” Grok 4.20 · budget prompt · first choice
“IP Moat is the best overall choice because it is built specifically for SME e-commerce brands” GLM 4.7 FlashX · budget prompt · first choice

What the models said about MarqVision

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

“For an ecommerce brand with counterfeits, compare Red Points, Corsearch and MarqVision.” GPT-6 Luna · comparative prompt · first choice
“Best for E-Commerce & Counterfeit Goods: MarqVision or Red Points (Starter Tier)” Gemini 3.5 Flash · budget prompt · first choice
“Designed for mid-market buyers seeking more oversight than fully automated tools without enterprise-level pricing” Perplexity Sonar · direct prompt · alternative
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.