Acunetix vs StackHawk
Four of fourteen models named Acunetix first on the direct prompt; three named StackHawk. Both were named by all fourteen models and Acunetix carries 35 labels and StackHawk 41, so the shares are not directly comparable.
Named in three categories this edition.
Named in three categories 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 dynamic application security testing page.
By framing
AcunetixFirst choices, of fourteen modelsStackHawk
Across every category in the November 2026 Edition, Acunetix and StackHawk were named in the same answer forty-four times, of the 104 answers naming Acunetix and the 186 naming StackHawk. In those answers StackHawk took the first choice eight times and Acunetix six.
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 | | | | | |
Acunetix StackHawk first choice named as an alternative argued againstblank: not namedEach cell is one answer, Acunetix on the left and StackHawk on the right.The direct prompt
Acunetix first, StackHawk an alternative
4 of 14 modelsStackHawk was named in the answer but not as the choice, or not at all.StackHawk first, Acunetix an alternative
3 of 14 modelsAcunetix was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
StackHawk13%#3 of 13Acunetix8%#4 of 13Acunetix13%#3 of 13StackHawk11%#4 of 13Acunetix0%#13 of 14StackHawk0%#12 of 14What the models said about Acunetix
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Acunetix notes its heuristic approach and AcuSensor reduces false positives, but still recommends” Muse Glimmer 30B · negative prompt · soft negative
“more enterprise-friendly, but usually much more expensive” GPT-5.4 mini · budget prompt · soft negative
“For most mid-sized B2B companies, Acunetix offers the best balance of cost, ease of use, and sufficient capabilities” Claude Haiku 4.5 · paraphrase prompt · first choice
“A pragmatic mid-sized B2B starting point is Acunetix for a balanced commercial DAST with low false positives” Muse Glimmer 30B · paraphrase prompt · first choice
“For dev teams wanting quick, reliable scans with low false positives: Choose Invicti or Acunetix.” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about StackHawk
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“its own docs acknowledge that scan results can include false positives, so findings still need review and confirmation” GPT-5.4 mini · negative prompt · soft negative
“Modern mid-market / DevSecOps native | StackHawk, Escape, Bright (NeuraLegion)” Kimi K2 · scale prompt · first choice
“the best DAST tool is usually StackHawk if your team is developer-driven” GPT-5.4 mini · direct prompt · first choice
“StackHawk — Best for Developer-Centric & Modern API-First B2B SaaS” Gemini 3.5 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.