Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 14 → 48 of 89
Google Chat
12Judge labels
0First choices
2Negative labels
10 / 14Models named it
3Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.
Standing
3 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Google Chat was named 3 times in Frontline employee apps and 2 other categories, where Connecteam led with 49%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business48 of 90
0%1 label
under the label threshold
Mid-market36 of 106
0%1 label
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
In frontline employee apps · 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 Google Chat 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 |
|---|---|---|---|---|---|---|---|
| Frontline employee apps | Onboarding and employee experience | 0% | 36 of 106 | 0% | 1 | under 10 labels · led by Connecteam at 49% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Internal communications and intranet | Onboarding and employee experience | 0% | 45 of 86 | 0% | 1 | under 10 labels · led by Microsoft Teams at 14% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Whistleblowing and investigations | Core HR and payroll | 0% | 90 of 107 | 100% | 1 | under 10 labels · led by FaceUp at 25% |
Movement
Since September 2026, Google Chat held within the floor in all three categories: no change cleared 13 points.
Best category · Frontline employee apps · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 72 → 32 of 100
EnterprisegoneNot named this edition; 0% in September 2026.
0%from 0% in September 2026
Inside the floor by 13 pointsNot ranked
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Frontline employee apps | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 72 → 36 of 106 |
| Internal communications | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 33 → 45 of 86 |
| Whistleblowing | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 90 of 107, unchanged |
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 13 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 Google Chat 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 Google Chat 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 | 1 | 0 | 0 | 1 |
| Perplexity Sonar | 0 | 0 | 0 | 1 | 1 |
| Grok 4.1 Fast | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| 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 | 0 | 0 | 0 | 0 |
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
Direct1 labelNone
Paraphrase9 labelsNone
Comparative0 labelsNone
Budget-constrained1 labelNone
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative12 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.
“Best for: Lean B2B companies heavily reliant on Google Drive, Docs, and Gmail.” Gemini 3.5 Flash · Internal communications · paraphrase prompt · alternative
“Google Chat or Slack Free if you primarily need chat.” GLM 4.7 FlashX · Frontline employee apps · 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.
“such as WhatsApp, Instagram DM, Facebook Messenger, Skype, Zoom, Google Hangouts/Chat, and Gmail” Perplexity Sonar · Whistleblowing · negative prompt · hard negative
Named alongside
The products named in the same answers as Google Chat, over the 12 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google Chat was named but was not.ShowHide
Named alongside
The products named in the same answers as Google Chat, over the 12 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google Chat 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 Google Chat. 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 9 of the 12 answers that named Google Chat and are not a share of its labels.
Domains cited
slack.com5
learn.g2.com4
nextiva.com4
peoplemanagingpeople.com4
atlassian.com3
guideflow.com3
joinblink.com3
larksuite.com3
mailmodo.com3
agilityportal.io2
No domain is on file for Google Chat, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Names read as Google Chat
What the judge wrote, as written, with how often. The vendor table decides that these count as Google Chat; a claim can dispute any of them.
Google Chat (via Google Workspace) 1Google Hangouts/Chat 1
Follow Google Chat
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 Google Chat'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 Google Chat, 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 Google Chat by email, built from the raw record of the edition. It shows:
- where Google Chat 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 Google Chat, 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.