Static application security testing · October 2026 Edition
Veracode Static Analysis vs Checkmarx One
Zero of fourteen models named Veracode Static Analysis first on the direct prompt; zero named Checkmarx One. Both were named by all fourteen models and Veracode Static Analysis carries 39 labels and Checkmarx One 44, so the shares are not directly comparable.
Named in one category this edition.
Named in four categories this edition.
First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate28%45%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 10; printed, not drawn.
Labels3944A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Veracode Static Analysis reading right to left. Rank and label count are printed, not drawn.Semgrep was named alongside these two in twelve of the fourteen direct answers. Semgrep vs Veracode Static Analysis · Semgrep vs Checkmarx One · SonarQube vs Veracode Static Analysis
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
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Veracode Static AnalysisFirst choices, of fourteen modelsCheckmarx One
Direct002 against Veracode Static Analysis · 3 against Checkmarx One
Paraphrase113 against Veracode Static Analysis · 6 against Checkmarx One
Comparative031 against Checkmarx One
Budget-constrained00
Scale-constrained001 against Veracode Static Analysis · 2 against Checkmarx One
Negative005 against Veracode Static Analysis · 8 against Checkmarx One
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.
Across every category in the October 2026 Edition, Veracode Static Analysis and Checkmarx One were named in the same answer 132 times, of the 138 answers naming Veracode Static Analysis and the 270 naming Checkmarx One. In those answers Checkmarx One took the first choice twenty-five times and Veracode Static Analysis three.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Veracode Static Analysis and Checkmarx One stood in it.
| 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 | | | |
Veracode Static Analysis Checkmarx One first choice named as an alternative argued againstblank: not namedEach cell is one answer, Veracode Static Analysis on the left and Checkmarx One on the right.The direct prompt
The plain question, one answer per model, grouped by where Veracode Static Analysis and Checkmarx One stood in it.
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.GPT-5.4 miniSemgrep alternatives: Checkmarx One, Snyk Code, Veracode Static Analysis
Perplexity SonarSemgrep alternatives: Checkmarx One, Snyk Code
GLM 4.7 FlashXSonarQube alternatives: Checkmarx One, Codacy, DeepSource, GitHub Advanced Security, OpenText Fortify, Semgrep, Snyk Code, Veracode Static Analysis
MiniMax M2.5Snyk alternatives: Semgrep, Veracode Static Analysis
Neither was named
10 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5no first choice
Gemini 3.5 FlashAikido Security alternatives: GitHub Advanced Security, GitLab SAST, Semgrep, Snyk Code
Grok 4.1 FastSnyk Code alternatives: Semgrep, SonarQube
Mistral SmallSnyk alternatives: Aikido Security, Semgrep
DeepSeek V4 FlashSnyk Code alternatives: CodeAnt.ai, Semgrep, SonarQube
Llama 4 MaverickSnyk Code
Qwen 3.7 FlashSonarQube alternatives: Semgrep, Snyk Code
Kimi K2Semgrep, Snyk Code alternatives: SonarQube
GPT-6 LunaSemgrep alternatives: GitHub Advanced Security, Snyk
Muse Glimmer 30BSonarQube alternatives: Semgrep
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
Level: the same share of first choices.
Veracode Static Analysis0%#12 of 13Checkmarx One0%#13 of 13Mid-marketThe figures above
Level: the same share of first choices.
Veracode Static Analysis2%#7 of 10Checkmarx One2%#8 of 10Enterprise
Checkmarx One leads by forty-one points.
Checkmarx One54%#1 of 10Veracode Static Analysis13%#2 of 10What the models said about Veracode Static Analysis
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight 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
“Full scans can delay fast-moving dev cycles, there's no on-premise option with code upload raising security concerns, and pricing can be steep” Claude Haiku 4.5 · negative prompt · soft negative
“not necessarily the one with the most heavy-duty enterprise compliance rules (like legacy tools Veracode or Fortify)” Gemini 3.5 Flash · direct prompt · soft negative
“the recommended static code security scanner for a mid-sized B2B company could be either Checkmarx or Veracode” Llama 4 Maverick · paraphrase prompt · first choice
“Excels in broad language/framework support (including legacy/binary), whole-app analysis, and enterprise integrations” Grok 4.1 Fast · comparative prompt · alternative
“Highly regulated environments, on-premises requirements, or teams needing very deep taint analysis.” GLM 4.7 FlashX · direct prompt · alternative
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
Also compared
Semgrep vs Veracode Static AnalysisSemgrep vs Checkmarx OneSonarQube vs Veracode Static AnalysisSonarQube vs Checkmarx OneSnyk Code vs Veracode Static AnalysisSnyk Code vs Checkmarx OneSnyk vs Veracode Static AnalysisSnyk vs Checkmarx OneCodeQL vs Veracode Static AnalysisCodeQL vs Checkmarx OneGitHub Advanced Security vs Veracode Static AnalysisGitHub Advanced Security vs Checkmarx OneAll twenty-eight head-to-head pages and the full SAST standing
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.