Trivy vs OWASP Dependency-Check
Zero of fourteen models named Trivy first on the direct prompt; zero named OWASP Dependency-Check. Both were named by all fourteen models and Trivy carries 35 labels and OWASP Dependency-Check 37, so the shares are not directly comparable.
Named in nine categories this edition.
Named in two 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 software composition analysis page.
By framing
TrivyFirst choices, of fourteen modelsOWASP Dependency-Check
Across every category in the November 2026 Edition, Trivy and OWASP Dependency-Check were named in the same answer sixty-seven times, of the 284 answers naming Trivy and the 106 naming OWASP Dependency-Check. In those answers OWASP Dependency-Check took the first choice eleven times and Trivy twenty-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 | | | | |
Trivy OWASP Dependency-Check first choice named as an alternative argued againstblank: not namedEach cell is one answer, Trivy on the left and OWASP Dependency-Check on the right.The direct prompt
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.Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.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
Trivy26%#2 of 14OWASP Dependency-Check19%#3 of 14Trivy34%#2 of 19OWASP Dependency-Check10%#3 of 19Trivy6%#6 of 14OWASP Dependency-Check2%#10 of 14What the models said about Trivy
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Top Recommendation for Open-Source Tools: Trivy (by Aqua Security) ... the gold standard open-source scanner for mid-sized engineering teams” Gemini 3.5 Flash · paraphrase prompt · first choice
“Top overall choice (free & powerful): Trivy (open-source; CLI, CI/CD, containers, IaC; zero cost, no vendor lock‑in)” GLM 4.7 FlashX · budget prompt · first choice
What the models said about OWASP Dependency-Check
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“generally not recommended for modern pipelines due to high false-positive rates and slow build times” Gemini 3.5 Flash · paraphrase prompt · hard negative
“OWASP Dependency-Check as your sole scanner: It's an actively maintained project, not one I'd dismiss. But its approach relies in part on matching components to vulnerability records” GPT-6 Luna · negative prompt · soft negative
“it is commonly used with caution because it is CPE and metadata driven, has historically high false-positive rates, lacks built-in reachability / exploitability prioritization” Muse Glimmer 30B · negative prompt · soft negative
“For budget-conscious teams or open-source projects, OWASP Dependency-Check is recommended” Claude Haiku 4.5 · budget prompt · first choice
“Primary SCA: OWASP Dependency-Check (free, comprehensive scanning)” Kimi K2 · budget prompt · first choice
“I'd suggest OWASP Dependency-Check as your primary tool” Claude Haiku 4.5 · 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.