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AI SOC agents · October 2026 Edition

Prophet Security vs Torq

One of fourteen models named Prophet Security first on the direct prompt; zero named Torq. Prophet Security was named by twelve of the fourteen models and Torq by thirteen and Prophet Security carries 24 labels and Torq 20, so the shares are not directly comparable.

Prophet Security

accepted challenger

Named in three categories this edition.

Torq

accepted challenger

Named in three categories this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%5%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#5A position in a field of 8; printed, not drawn.
Labels2420A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Prophet Security reading right to left. Rank and label count are printed, not drawn.Radiant Security was named alongside these two in twelve of the fourteen direct answers. Dropzone AI vs Prophet Security · Dropzone AI vs Torq · Radiant Security vs Prophet Security

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 AI SOC agents page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Prophet SecurityFirst choices, of fourteen modelsTorq
Direct101 against Torq
Paraphrase01
Comparative20
Budget-constrained00
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 October 2026 Edition, Prophet Security and Torq were named in the same answer thirty-one times, of the 72 answers naming Prophet Security and the 143 naming Torq. In those answers Torq took the first choice three times and Prophet Security five.

Every model, every framing

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

The direct prompt

The plain question, one answer per model, grouped by where Prophet Security and Torq stood in it.

Prophet Security first, Torq not the choice

1 of 14 modelsTorq was named in the answer but not as the choice, or not at all.
Grok 4.1 FastDropzone AI, Prophet Security, Radiant Security alternatives: Arctic Wolf, Huntress Managed EDR, Microsoft Security Copilot

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.
Gemini 3.5 FlashDropzone AI alternatives: Intezer AI SOC, Prophet Security, Radiant Security
GLM 4.7 FlashXRadiant Security, UnderDefense MAXI AI SOC alternatives: Dropzone AI, Microsoft Security Copilot, Torq
GPT-6 LunaDropzone AI alternatives: Microsoft Security Copilot, Prophet Security, Torq

Neither was named

10 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Radiant Security alternatives: Conifers CognitiveSOC, Dropzone AI, Stellar Cyber Open XDR
GPT-5.4 miniTorq AI SOC Platform alternatives: CrowdStrike Falcon, Google Security Operations, Panther
Perplexity SonarRadiant Security alternatives: Dropzone AI, Stellar Cyber Open XDR
Mistral SmallRadiant Security, SentinelOne Purple AI
DeepSeek V4 FlashRadiant Security, UnderDefense alternatives: Dropzone AI, Microsoft Security Copilot + Sentinel
Llama 4 MaverickRadiant Security alternatives: SentinelOne Purple AI
Qwen 3.7 FlashSentinelOne Purple AI alternatives: Radiant Security
Kimi K2Radiant Security alternatives: Dropzone AI, Stellar Cyber Open XDR, UnderDefense Agentic AI SOC
MiniMax M2.5UnderDefense MAXI AI SOC alternatives: Radiant Security
Muse Glimmer 30BRadiant Security, UnderDefense

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
Prophet Security leads by two points.
Prophet Security2%#7 of 11
Torq0%#11 of 11
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Prophet Security2%#4 of 8
Torq2%#5 of 8
The full mid-market standing →
Enterprise
The order flips: Torq leads at enterprise.
Torq9%#3 of 10
Prophet Security5%#5 of 10
The full enterprise standing →

What the models said about Prophet Security

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 Dropzone AI, Prophet Security, or Radiant Security if building in-house capability” Grok 4.1 Fast · direct prompt · first choice
“Have a mixed tool stack and want full autonomy? Look at Prophet Security.” DeepSeek V4 Flash · comparative prompt · first choice
“For a mixed-vendor stack, compare Dropzone, Prophet, and Qevlar.” GPT-6 Luna · comparative prompt · first choice

What the models said about Torq

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

“What to Avoid: "The Enterprise Giants" ... too heavy and expensive for a lean mid-market team.” Gemini 3.5 Flash · direct prompt · hard negative
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.