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Dynamic application security testing · November 2026 Edition

OWASP ZAP vs Probely

One of fourteen models named OWASP ZAP first on the direct prompt; one named Probely. OWASP ZAP was named by fourteen of the fourteen models and Probely by six and OWASP ZAP carries 57 labels and Probely 10, so the shares are not directly comparable.

OWASP ZAP

accepted challenger

Named in six categories this edition.

Probely

accepted challenger

Named in two categories this edition.

First-choice share26%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate25%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 13; printed, not drawn.
Labels5710A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, OWASP ZAP reading right to left. Rank and label count are printed, not drawn.Invicti was named alongside these two in twelve of the fourteen direct answers. Invicti vs OWASP ZAP · Invicti vs Probely · OWASP ZAP vs Acunetix

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 dynamic application security testing page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
OWASP ZAPFirst choices, of fourteen modelsProbely
Direct114 against OWASP ZAP
Paraphrase001 against OWASP ZAP
Comparative10
Budget-constrained130
Scale-constrained00
Negative109 against OWASP ZAP
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.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where OWASP ZAP and Probely 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
OWASP ZAP Probely first choice named as an alternative argued againstblank: not namedEach cell is one answer, OWASP ZAP on the left and Probely on the right.

The direct prompt

The plain question, one answer per model, grouped by where OWASP ZAP and Probely stood in it.

Both were the first choice

1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
GLM 4.7 FlashXOWASP ZAP, Probely alternatives: Burp Suite, Invicti, StackHawk

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.20Invicti alternatives: Escape, Intruder, Probely, StackHawk
Mistral SmallAcunetix, Invicti alternatives: OWASP ZAP, StackHawk
DeepSeek V4 FlashAcunetix alternatives: Invicti, OWASP ZAP, StackHawk
Llama 4 MaverickInvicti alternatives: Burp Suite, OWASP ZAP, StackHawk

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Invicti alternatives: Acunetix, Bright Security, StackHawk
GPT-5.4 miniStackHawk alternatives: Burp Suite, Invicti
Gemini 3.5 FlashStackHawk alternatives: Acunetix, Bright Security, Escape, Invicti
Perplexity SonarInvicti alternatives: Bright Security, Escape, StackHawk
Qwen 3.7 FlashInvicti alternatives: Acunetix, Apidog, Postman, Veracode DAST
Kimi K2Acunetix, Intruder alternatives: Bright Security, Detectify
MiniMax M2.5Acunetix, Bright Security alternatives: Detectify, Intruder
GPT-6 LunaStackHawk alternatives: Burp Suite DAST, Invicti
Muse Glimmer 30BInvicti alternatives: Acunetix, StackHawk

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
OWASP ZAP leads by forty-seven points.
OWASP ZAP51%#1 of 13
Probely4%#5 of 13
The full small business standing →
Mid-marketThe figures above
OWASP ZAP leads by twenty-four points.
OWASP ZAP26%#2 of 13
Probely2%#7 of 13
The full mid-market standing →
Enterprise
Level: the same share of first choices.
OWASP ZAP0%#14 of 14
Probely0%#– of 14
The full enterprise standing →

What the models said about OWASP ZAP

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

“it is advisable to avoid or proceed with extreme caution when using OWASP ZAP or Invicti in automated pipelines” Mistral Small · negative prompt · hard negative
“Avoid for Automated Pipelines: OWASP ZAP (if you lack a dedicated team to manage it).” Qwen 3.7 Flash · negative prompt · hard negative
“Unmanaged open-source tools (like raw OWASP ZAP) unless you have dedicated headcount” Gemini 3.5 Flash · direct prompt · hard negative
“Best default for a limited budget: ZAP by Checkmarx (formerly OWASP ZAP). It's free and open source” GPT-6 Luna · budget prompt · first choice
“Use OWASP ZAP (actively maintained, modern browser crawling support, strong CI/CD integration)” Gemini 3.5 Flash · negative prompt · first choice

What the models said about Probely

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 Overall Value: Probely ... My top recommendation: Start with a trial of Probely or OWASP ZAP” GLM 4.7 FlashX · direct prompt · first choice
“Top-Rated Options (from 2023–2025 reviews): Probely, StackHawk, Rapid7 InsightAppSec” Grok 4.20 · negative prompt · alternative
“StackHawk or Probely — Developer-friendly, strong CI/CD focus.” Grok 4.20 · scale 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.