Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 9 → 10 of 47
CodeQL
74Judge labels
3First choices
13Negative labels
14 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
4% in SAST for mid-market buyers
Rank 5 of 71 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 11% of its 27 labels there were negative.
What the models named instead of CodeQL →By buyer segmentRead the same way at every buyer size.
Small business11 of 51
0%26 labels
accepted challenger
Mid-market5 of 71
4%27 labels
accepted challenger
Enterprise7 of 45
2%21 labels
accepted challenger
In sast · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named CodeQL for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Static application security testing | Developer platform | 4% | 5 of 71 | 11% | 27 | accepted challenger | ▲+2Since September 2026: 2% → 5%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, CodeQL held within the floor in its one category: no change cleared 11 points.
Best category · SAST · by segment
Mid-market▲+2Since September 2026: 2% → 5%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
5%from 2% in September 2026
Inside the floor by 9 pointsRank 6 → 5 of 65
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 12 of 42, unchanged
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| SAST | 2% | 5% | ▲+2Since September 2026: 2% → 5%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 6 → 5 of 71 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated CodeQL across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated CodeQL across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 1 | 1 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 2 | 2 |
| Perplexity Sonar | 0 | 1 | 1 | 0 | 2 |
| Grok 4.20 | 1 | 2 | 1 | 0 | 4 |
| Mistral Small | 1 | 0 | 1 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 4 | 0 | 0 | 4 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 2 | 0 | 0 | 2 |
| Kimi K2 | 0 | 2 | 0 | 0 | 2 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 1 | 0 | 1 |
| GPT-6 Luna | 0 | 3 | 0 | 1 | 4 |
| Muse Glimmer 30B | 0 | 2 | 0 | 0 | 2 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct5 labelsNone
Paraphrase6 labels1
Comparative14 labels1not counted in share
Budget-constrained19 labels1
Scale-constrained8 labels1
Negative22 labels2not counted in share
First choiceAlternativeMentionNegative74 labels in all, every segment counted; 3 of the 6 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“GitHub CodeQL – Free for public repositories ... It provides deep code analysis and is widely used for finding vulnerabilities in multiple languages.” Mistral Small · SAST · budget prompt · first choice
“my default recommendation would be GitHub CodeQL if you already use GitHub” GPT-5.4 mini · SAST · paraphrase prompt · first choice
“Lower noise: CodeQL (semantic, low FPs)” Grok 4.20 · SAST · negative prompt · first choice
“Semantic dataflow analysis (stronger than patterns)... but slower (builds DB, nightly-suited), fewer languages” Grok 4.20 · SAST · comparative prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“CodeQL requires a subscription to GitHub Advanced Security (GHAS), which is highly enterprise-focused and expensive” Gemini 3.5 Flash · SAST · budget prompt · soft negative
“CodeQL (A "Proceed with Caution" for Closed-Source Teams) ... GHAS licensing, which can be prohibitively expensive” Gemini 3.5 Flash · SAST · negative prompt · soft negative
“CodeQL if your stack or plan doesn't fit: Check language support before adopting it” GPT-6 Luna · SAST · negative prompt · soft negative
Named alongside
The products named in the same answers as CodeQL, over the 74 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and CodeQL was named but was not.ShowHide
Named alongside
The products named in the same answers as CodeQL, over the 74 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and CodeQL was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named CodeQL. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 70 of the 74 answers that named CodeQL and are not a share of its labels.
Domains cited
dev.to47
checkmarx.com44
appsecsanta.com38
safeguard.sh29
augmentcode.com28
opsera.ai26
aikido.dev23
getautonoma.com20
corgea.com19
endorlabs.com18
No domain is on file for CodeQL, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where CodeQL stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where CodeQL stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 2,900 a month (“codeql”)
- AI search demand for its name, est.
- 81 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
4%
rank 5 of 71 in SAST
Segment leader
36%
Semgrep
- First choices
- 3 across its categories
- Named in
- 74 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as CodeQL
What the judge wrote, as written, with how often. The vendor table decides that these count as CodeQL; a claim can dispute any of them.
GitHub CodeQL 11CodeQL (by GitHub) 2CodeQL (GitHub) 1GitHub CodeQL / GitHub code scanning 1
Follow CodeQL
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when CodeQL's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as CodeQL, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for CodeQL by email, built from the raw record of the edition. It shows:
- where CodeQL is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name CodeQL, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.