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Static application security testing · October 2026 Edition

CodeQL vs Checkmarx One

Zero of fourteen models named CodeQL first on the direct prompt; zero named Checkmarx One. CodeQL was named by eleven of the fourteen models and Checkmarx One by fourteen and CodeQL carries 27 labels and Checkmarx One 44, so the shares are not directly comparable.

CodeQL

accepted challenger

Named in one category this edition.

Checkmarx One

criticized challenger

Named in four categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate11%45%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 10; printed, not drawn.
Labels2744A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, CodeQL 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 CodeQL · Semgrep vs Checkmarx One · SonarQube vs CodeQL

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.
CodeQLFirst choices, of fourteen modelsCheckmarx One
Direct003 against Checkmarx One
Paraphrase116 against Checkmarx One
Comparative031 against Checkmarx One
Budget-constrained101 against CodeQL
Scale-constrained002 against Checkmarx One
Negative102 against CodeQL · 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, CodeQL and Checkmarx One were named in the same answer forty-four times, of the 74 answers naming CodeQL and the 270 naming Checkmarx One. In those answers Checkmarx One took the first choice seven times and CodeQL three.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where CodeQL and Checkmarx One stood in it.
ModelDirectCOParaphraseCOComparativeCOBudget-constrainedCOScale-constrainedCONegativeCO
Claude Haiku 4.5
GPT-5.4 miniCOCO
Gemini 3.5 FlashCOCO
Perplexity SonarCO
Grok 4.1 FastCOCOCO
Mistral SmallCO
DeepSeek V4 FlashCOCOCOCO
Llama 4 Maverick
Qwen 3.7 FlashCOCO
Kimi K2COCO
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 LunaCOCOCOCO
Muse Glimmer 30BCOCO
CO CodeQL Checkmarx OneCO first choiceCO named as an alternativeCO argued againstblank: not namedEach cell is one answer, CodeQL on the left and Checkmarx One on the right.

The direct prompt

The plain question, one answer per model, grouped by where CodeQL and Checkmarx One stood in it.

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.
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

Neither was named

11 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
MiniMax M2.5Snyk alternatives: Semgrep, Veracode Static Analysis
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.
CodeQL0%#8 of 13
Checkmarx One0%#13 of 13
The full small business standing →
Mid-marketThe figures above
CodeQL leads by two points.
CodeQL4%#5 of 10
Checkmarx One2%#8 of 10
The full mid-market standing →
Enterprise
The order flips: Checkmarx One leads at enterprise.
Checkmarx One54%#1 of 10
CodeQL2%#7 of 10
The full enterprise standing →

What the models said about CodeQL

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“CodeQL requires a subscription to GitHub Advanced Security (GHAS), which is highly enterprise-focused and expensive” Gemini 3.5 Flash · budget prompt · soft negative
“CodeQL (A "Proceed with Caution" for Closed-Source Teams) ... GHAS licensing, which can be prohibitively expensive” Gemini 3.5 Flash · negative prompt · soft negative
“CodeQL if your stack or plan doesn't fit: Check language support before adopting it” GPT-6 Luna · negative prompt · soft negative
“GitHub CodeQL – Free for public repositories ... It provides deep code analysis and is widely used for finding vulnerabilities in multiple languages.” Mistral Small · budget prompt · first choice
“my default recommendation would be GitHub CodeQL if you already use GitHub” GPT-5.4 mini · paraphrase prompt · first choice
“Lower noise: CodeQL (semantic, low FPs)” Grok 4.1 Fast · negative 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
Also compared
Semgrep vs CodeQLSemgrep vs Checkmarx OneSonarQube vs CodeQLSonarQube vs Checkmarx OneSnyk Code vs CodeQLSnyk Code vs Checkmarx OneSnyk vs CodeQLSnyk vs Checkmarx OneCodeQL vs GitHub Advanced SecurityCodeQL vs Veracode Static AnalysisGitHub Advanced Security vs Checkmarx OneVeracode Static Analysis 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.