IT AI Index

Index › Vendors › DeepSeek · September 2026 Edition

2 categories · Named, not ranked

DeepSeek

9Judge labels

0First choices

6Negative labels

7 of 12Models named it

2Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

5 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. DeepSeek was named 5 times in ML platforms and 1 other category, where Azure Machine Learning led with 17%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business104 of 124

under the label threshold

Mid-market99 of 99

under the label threshold

Enterprisenot named

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 DeepSeek 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
ML platforms	Data platform	0%	99 of 99	100%	4	under 10 labels · led by Azure Machine Learning at 17%
AI coding assistants	Developer platform	0%	25 of 46	0%	1	under 10 labels · led by GitHub Copilot at 67%

Movement

This is the first edition on this tier, so no move can be computed for DeepSeek 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 DeepSeek across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Mention	Negative	Labels
Claude Haiku 4.5	0	0	0
GPT-5.4 mini	0	0	0
Gemini 3.5 Flash	1	0	1
Perplexity Sonar	0	0	0
Grok 4.1 Fast	0	1	1
Mistral Small	0	1	1
DeepSeek V4 Flash	0	0	0
Llama 4 Maverick	0	0	0
Qwen 3.7 Flash	0	1	1
Kimi K2	0	0	0
GLM 4.7 FlashX	0	0	0
MiniMax M2.5	0	1	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-constrained3 labelsNone

Scale-constrained0 labelsNone

Negative6 labelsNone

First choiceAlternativeMentionNegative9 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.

No positive label carried a quote.

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.

“leading experts to advise against logging in or using the service for enterprise tasks until stability improves” Qwen 3.7 Flash · ML platforms · negative prompt · hard negative

“DeepSeek is considered high-risk, especially for work-related tasks involving sensitive data.” Mistral Small · ML platforms · negative prompt · hard negative

“Avoid for sensitive data; self-host if possible, but assess compliance.” Grok 4.1 Fast · ML platforms · negative prompt · hard negative

“DeepSeek - Also flagged as highly privacy-invasive” MiniMax M2.5 · ML platforms · negative prompt · hard negative

Named alongside

The products named in the same answers as DeepSeek, over the 9 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and DeepSeek was named but was not.

ProductSame answerTook the first choice insteadHead to head

Amazon SageMaker4 of 90Not in the top three

Azure Machine Learning4 of 90Not in the top three

Google Vertex AI4 of 90Not in the top three

Continue2 of 91Not in the top three

OpenRouter2 of 91Not in the top three

ChatGPT2 of 90Not in the top three

Cursor2 of 90Not in the top three

DagsHub2 of 90Not in the top three

Databricks2 of 90Not in the top three

GitHub Copilot2 of 90Not 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 DeepSeek. 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 7 of the 9 answers that named DeepSeek and are not a share of its labels.

Domains cited

dev.to4

dagshub.com3

thirstysprout.com3

blog.incogni.com2

checkthat.ai2

cisa.gov2

cloudzero.com2

ibm.com2

layer3labs.io2

reddit.com2

Twenty-four of the twenty-four domain citations in answers naming DeepSeek came from somebody else's page.

Pages cited

dagshub.com/blog/top-deep-learning-platforms3

dev.to/polarsquad/aiml-platforms-pros-and-cons-28ka3

thirstysprout.com/post/best-machine-learning-platforms3

blog.incogni.com/ai-llm-privacy-ranking-20252

cloudzero.com/blog/llm-gateways2

respan.ai/articles/best-llm-gateways2

sciencedirect.com/science/article/pii/S26663899240018802

techtarget.com/ai/news/366618546/Assessing-if-DeepSeek-is-safe-to-use-in-the-enterprise2

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 DeepSeek'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 DeepSeek, 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 deepseek.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.