Small business▼−4Since September 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 4% in September 2026
Inside the floor by 7 pointsRank 6 → 36 of 131
Kaggle
11Judge labels
0First choices
0Negative labels
9 / 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.
Standing
7 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Kaggle was named 7 times in ML platforms, where Azure Machine Learning led with 21%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business37 of 139
0%4 labels
under the label threshold
Mid-market22 of 107
0%7 labels
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
In ml platforms · 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 Kaggle 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 |
|---|---|---|---|---|---|---|---|
| ML platforms | Data platform | 0% | 22 of 107 | 0% | 7 | under 10 labels · led by Azure Machine Learning at 21% | ▼−4Since September 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Kaggle held within the floor in its one category: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-market▼−4Since September 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 4% in September 2026
Inside the floor by 7 pointsRank 8 → 22 of 97
EnterprisegoneNot named this edition; 0% in September 2026.
0%from 0% in September 2026
Inside the floor by 11 pointsNot ranked
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 4% | 0% | ▼−4Since September 2026: 4% → 0%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 8 → 22 of 107 |
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 Kaggle 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 Kaggle 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 | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 1 | 1 | 0 | 2 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 1 | 0 | 0 | 1 |
| Llama 4 Maverick | 0 | 0 | 1 | 0 | 1 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 1 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 1 | 0 | 0 | 1 |
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
Direct0 labelsNone
Paraphrase0 labelsNone
Comparative0 labelsNone
Budget-constrained10 labelsNone
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative11 labels in all, every segment counted; 0 of the 0 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.
“Kaggle Notebooks is also commonly listed alongside Colab in cost-efficiency discussions for small projects.” Muse Glimmer 30B · ML platforms · budget prompt · alternative
“Use Google Colab or Kaggle Notebooks to explore and prototype models (cost: $0)” DeepSeek V4 Flash · ML platforms · budget prompt · alternative
“Free GPUs/TPUs (up to 30 hours/week), datasets hub, competitions.” Grok 4.20 · ML platforms · budget prompt · alternative
“## 2. Best for Data Science & Learning: Kaggle” GLM 4.7 FlashX · ML platforms · budget 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.
No model argued against it.
Named alongside
The products named in the same answers as Kaggle, over the 11 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Kaggle was named but was not.ShowHide
Named alongside
The products named in the same answers as Kaggle, over the 11 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Kaggle 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 Kaggle. 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 8 of the 11 answers that named Kaggle and are not a share of its labels.
Domains cited
checkthat.ai6
eweek.com6
g2.com6
byteplus.com4
sourceforge.net4
us.fitgap.com4
spotsaas.com3
aws.amazon.com2
deepnote.com2
deepusecase.com2
No domain is on file for Kaggle, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Kaggle stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Kaggle stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 74,000 a month (“kaggle”)
- AI search demand for its name, est.
- 778 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
0%
rank 22 of 107 in ML platforms
Segment leader
21%
Azure Machine Learning
- First choices
- 0 across its categories
- Named in
- 11 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 Kaggle
What the judge wrote, as written, with how often. The vendor table decides that these count as Kaggle; a claim can dispute any of them.
Kaggle Notebooks 4
Follow Kaggle
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 Kaggle'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 Kaggle, 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 Kaggle by email, built from the raw record of the edition. It shows:
- where Kaggle 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 Kaggle, 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.