IT AI Index
Index › Vendors › Augment Code · September 2026 Edition
1 category · Ranked

Augment Code

19Judge labels
1First choices
0Negative labels
11 of 12Models named it
1Category
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.
Best standing
0% in AI coding for enterprise buyers
Rank 9 of 46 in the mid-market standing
1 of 12 models made it the first choice on the direct prompt; 0% of its 3 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small businessnot named
0%
No model named it for this buyer.
Mid-market9 of 46
2%3 labels
under the label threshold
Enterprise4 of 47
0%16 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.
CategoryFunctionShareRankNegative rateLabelsQuadrant
AI coding assistantsDeveloper platform2%9 of 460%3under 10 labels · led by GitHub Copilot at 67%

Movement

This is the first edition on this tier, so no move can be computed for Augment Code yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Augment Code across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash10001
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash00000
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200101
GLM 4.7 FlashX00000
MiniMax M2.501001

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 labels1
Paraphrase2 labelsNone
Comparative9 labelsNone
Budget-constrained0 labelsNone
Scale-constrained1 labelNone
Negative1 labelNone
First choiceAlternativeMentionNegative19 labels in all, every segment counted; 1 of the 1 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 Overall for Complex, Multi-Repository, or Microservice Architectures” Gemini 3.5 Flash · AI coding · direct prompt · first choice
“Best for: Enterprise monorepos, legacy refactoring” MiniMax M2.5 · AI coding · 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.

No model argued against it.

Named alongside

The products named in the same answers as Augment Code, over the 19 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.
ProductSame answerTook the first choice insteadHead to head
GitHub Copilot18 of 1912Not in the top three
Cursor16 of 190Not in the top three
Tabnine15 of 191Not in the top three
Amazon Q Developer15 of 190Not in the top three
Claude Code12 of 190Not in the top three
Sourcegraph Cody9 of 190Not in the top three
Gemini Code Assist4 of 190Not in the top three
JetBrains AI Assistant4 of 190Not in the top three
Windsurf4 of 190Not in the top three
Aider3 of 190Not in the top three
A head-to-head page exists where both products are in a category's top three. 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, four of the twelve in this edition, so these counts come from 16 of the 19 answers that named Augment Code and are not a share of its labels.

Domains cited

augmentcode.comYour site16
agentic.ai8
axify.io8
gartner.com5
learn.g2.com5
blog.exceeds.ai4
intuitionlabs.ai4
omidsaffari.com4
ai-agent-brief.com3
faros.ai3

Forty-four of the sixty domain citations in answers naming Augment Code came from somebody else's page.

Pages cited

augmentcode.com/tools/beyond-github-copilot-5-enterprise-ready-ai-coding-assistants12
augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases9
agentic.ai/best/enterprise-coding-agents8
axify.io/blog/the-best-ai-coding-assistants-a-full-comparison-of-20-tools8
augmentcode.com/tools/best-enterprise-ai-code-generators5
learn.g2.com/best-ai-coding-assistants5
gartner.com/reviews/market/ai-code-assistants4
omidsaffari.com/blog/best-ai-coding-agents-for-enterprise4

Pages are listed as the models cited 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.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at augmentcode.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.

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