Four of fourteen models named Semgrep first on the direct prompt; zero named Checkmarx One. Both were named by all fourteen models and Semgrep carries 63 labels and Checkmarx One 44, so the shares are not directly comparable.
Named in three 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.
Across every category in the October 2026 Edition, Semgrep and Checkmarx One were named in the same answer 125 times, of the 208 answers naming Semgrep and the 270 naming Checkmarx One. In those answers Checkmarx One took the first choice nineteen times and Semgrep forty-two.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Semgrep OSS and Bandit are fast and low false-positive for obvious patterns, but miss vulnerabilities that span multiple functions... insufficient as sole nightly security scanners” Muse Glimmer 30B · negative prompt · soft negative
“Semgrep Community Edition as your only deep security scanner: It can be useful for fast checks and custom rules, but its analysis is limited to a file/function context” GPT-6 Luna · negative prompt · soft negative
“Checkmarx, Veracode, and Semgrep have all struggled with this because their rule-based engines flag anything that could be a vulnerability” Claude Haiku 4.5 · negative prompt · soft negative
“Semgrep as the primary scanner across all languages... For most budget-constrained companies, Semgrep OSS + SonarQube Community Edition... is the most commonly recommended starting point” Muse Glimmer 30B · budget prompt · first choice
“Tools like Semgrep are generally preferred for modern CI/CD because they are lightweight, customizable, and run significantly faster.” GLM 4.7 FlashX · negative prompt · first choice
“look for developer-friendly alternatives like Semgrep, where rules are written in YAML and read like standard code” Gemini 3.5 Flash · negative prompt · first choice
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.