Small businessnewNew since September 2026: not ranked then, 0% now.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 21 of 37, unchanged
1 category · RankedAugment Code
23Judge labels
0First choices
3Negative labels
10 / 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
0% in AI coding for enterprise buyers
Rank 14 of 48 in the mid-market standing
0 of 14 models made it the first choice on the direct prompt; 29% of its 7 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business18 of 39
0%2 labels
under the label threshold
Mid-market14 of 48
0%7 labels
under the label threshold
Enterprise6 of 52
0%14 labels
accepted challenger
In ai coding · 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 Augment Code 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 |
|---|---|---|---|---|---|---|---|
| AI coding assistants | Developer platform | 0% | 14 of 48 | 29% | 7 | under 10 labels · led by GitHub Copilot at 75% | ▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Augment Code held within the floor in its one category: no change cleared 11 points.
Best category · AI coding · by segment
Mid-market▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 2% in September 2026
Inside the floor by 9 pointsRank 9 → 14 of 43
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 4 → 6 of 49
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| AI coding | 2% | 0% | ▼−2Since September 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 9 → 14 of 48 |
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 Augment Code 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 Augment Code 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 | 1 | 0 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 1 | 0 | 1 | 2 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.20 | 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 | 1 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 1 | 1 |
| 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
Direct6 labelsNone
Paraphrase0 labelsNone
Comparative7 labelsNone
Budget-constrained4 labelsNone
Scale-constrained2 labelsNone
Negative4 labelsNone
First choiceAlternativeMentionNegative23 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 complex, large codebases with enterprise governance: Augment Code” Perplexity Sonar · AI coding · direct prompt · alternative
“Augment Code handles enterprise monorepos and legacy refactoring” Muse Glimmer 30B · AI coding · direct prompt · alternative
“making it the strongest option for large monorepos” Claude Haiku 4.5 · AI coding · comparative prompt · alternative
“The Best for Complex Codebases & Monorepos” Gemini 3.5 Flash · AI coding · direct 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.
“Augment and Windsurf (Caution regarding GhostApproval)... did not issue swift fixes, leaving their users temporarily exposed.” Gemini 3.5 Flash · AI coding · negative prompt · soft negative
“Augment and Windsurf had not provided a fix update at the time of publication” GPT-6 Luna · AI coding · negative prompt · soft negative
Named alongside
The products named in the same answers as Augment Code, over the 23 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Augment Code was named but was not.ShowHide
Named alongside
The products named in the same answers as Augment Code, over the 23 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Augment Code 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 Augment Code. 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 23 of the 23 answers that named Augment Code and are not a share of its labels.
Domains cited
augmentcode.comYour site20
jellyfish.co8
kilo.ai7
sourcegraph.com7
tech-insider.org7
tembo.io7
layer3labs.io6
axify.io5
checkmarx.com5
daily.dev5
Fifty-seven of the seventy-seven domain citations in answers naming Augment Code came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
augmentcode.com ranks first on Google for the category's searches. In the answers, Augment Code takes 0% of first choices and GitHub Copilot takes 75%.ShowHide
Search and answers
augmentcode.com ranks first on Google for the category's searches. In the answers, Augment Code takes 0% of first choices and GitHub Copilot takes 75%.
ShowHideIn search
Google, US estimatesPosition for “ai coding assistant”
1
Position for “ai coding assistants”
–
not in the top ten
- Searches for its name, Google
- 6,600 a month (“augment code”)
- AI search demand for its name, est.
- 199 a month
- Organic visits to its site
- about 146,518 a month
- Searches its site ranks for
- 4,323 · 632 in the top three
- Sites linking to it
- 3,969
- Paid Google search
- No ads found in the estimate; that does not mean it runs none
In answers
This editionShare of first choices
0%
rank 14 of 48 in AI coding
Segment leader
75%
GitHub Copilot
- First choices
- 0 across its categories
- Named in
- 23 answers
- Its own site cited
- in 23 of the answers that named it
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.
What it publishes
ShowHide
What it publishes
ShowHideAddresses on augmentcode.com
1,796
subdomains included
Content
972
counted in the table below
Documentation
317
Product · Integration
15 · 0
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Report or ebook | 347 | 6 | 2026-09-18 | AI coding, SAST | |
| Comparison | 231 | 0 | 2026-02-13 | AI coding | |
| Template or tool | 132 | 4 | 2026-09-17 | AI coding, Observability | |
| Blog | 129 | 8 | 2026-09-11 | AI coding | |
| Glossary or explainer | 96 | 13 | 2026-07-27 | AI coding | |
| Case study | 29 | undated | |||
| Podcast or video | 8 | 2 | 2026-07-09 |
Most recent
- 2026-09-18Managed agents vs interactive sessions report or ebook
- 2026-09-18Budgeting ai agents vs headcount report or ebook
- 2026-09-18Engineering manager ai software factory report or ebook
- 2026-09-18Human in the loop vs human on the loop report or ebook
- 2026-09-18Coderabbit pricing costs limits report or ebook
- 2026-09-17Dark factory autonomous software delivery report or ebook
How it is countedHide how it is counted
Every page augmentcode.com exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Read September 29, 2026.
Names read as Augment Code
What the judge wrote, as written, with how often. The vendor table decides that these count as Augment Code; a claim can dispute any of them.
Augment 3
Follow Augment Code
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.
The company
Augment Code is its own company.ShowHide
The company
Augment Code is its own company.
ShowHideIn its own words
Stated by the vendor, not checked- Positioning
- The most powerful AI software development platform with the industry-leading context engine.
- For
- engineering organizations
- Price stated
- Standard $20/month; Business $100/month; Enterprise Custom augmentcode.com/pricing
- Starting price
- $20/month augmentcode.com/pricing
- Integrations claimed
- 4 augmentcode.com
- Certifications
- SOC 2 Type IICMEK & ISO 42001 complianceSOC 2 & Security Reports
- Customers named
- Pearl TechnologiesWEXGoFundMeIntercomRubrikPure StorageMoneyGramTekion
- Not stated on the pages read
- Free plan or trial, hosting
What Augment Code's own pages state, read September 28, 2026: augmentcode.com, augmentcode.com/pricing, trust.augmentcode.com, augmentcode.com/product/cli, augmentcode.com/customers. A claimed page can correct any of them.
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 Augment Code'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 Augment Code, 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 Augment Code by email, built from the raw record of the edition. It shows:
- where Augment Code 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 Augment Code, 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.