Snyk vs Checkmarx One
Two of fourteen models named Snyk first on the direct prompt; zero named Checkmarx One. Snyk was named by seven of the fourteen models and Checkmarx One by fourteen and Snyk carries 11 labels and Checkmarx One 44, so the shares are not directly comparable.
Named in eleven categories this edition.
Named in four 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 static application security testing page.
By framing
SnykFirst choices, of fourteen modelsCheckmarx One
Across every category in the October 2026 Edition, Snyk and Checkmarx One were named in the same answer sixty-nine times, of the 177 answers naming Snyk and the 270 naming Checkmarx One. In those answers Checkmarx One took the first choice thirteen times and Snyk nine.
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 4.1 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 | | | |
Snyk Checkmarx One first choice named as an alternative argued againstblank: not namedEach cell is one answer, Snyk on the left and Checkmarx One on the right.The direct prompt
Snyk first, Checkmarx One not the choice
2 of 14 modelsCheckmarx One was named in the answer but not as the choice, or not at all.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.Neither was named
8 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
Checkmarx One54%#1 of 10Snyk0%#10 of 10What the models said about Snyk
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“"We are forced to use Snyk–it's basically infuriating support and false positives... it's also insanely expensive"” Claude Haiku 4.5 · negative prompt · soft negative
“Snyk appears to be the most frequently recommended option for mid-market B2B companies running modern stacks” MiniMax M2.5 · direct prompt · first choice
What the models said about Checkmarx One
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“What I'd Avoid for Mid-Market: Checkmarx and Veracode are powerful but typically start at $60K–$100K+/year” Kimi K2 · paraphrase prompt · hard negative
“be especially cautious with legacy configurations of Checkmarx, Fortify/OpenText, and SonarQube for security use cases unless they are heavily tuned” Muse Glimmer 30B · negative prompt · soft negative
“Legacy enterprise | Checkmarx, Veracode, Fortify | Deep compliance... unless you have specific compliance requirements that mandate legacy tools” Kimi K2 · scale prompt · soft negative
“Checkmarx One (Gartner/Forrester Leader): Enterprise-grade with deep dataflow/taint analysis... Checkmarx for banks” Grok 4.1 Fast · comparative prompt · first choice
“an enterprise-grade scanner (like Checkmarx or Fortify) for nightly full-scan reports and compliance auditing.” Qwen 3.7 Flash · comparative prompt · first choice
“Gartner Magic Quadrant Leader for 7 consecutive years... Best For: Large enterprises, portfolio-level governance” Kimi K2 · comparative 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.