AI Visibility of Car Brands in Türkiye 2026

Türkiye's AI Car Map: Which Brands Do the Models Agree On?

On October 22–26, 2026, we compared four AI platforms across 10 Turkish-language car purchase queries. We recorded 1,036 brand/institution appearances and 92 standardized unique names. The brand sets of ChatGPT, Gemini and Perplexity overlapped 47.9% on average, while Google AI Overview's overlap with these three systems stayed at about 8%. Average overlap across the four surfaces rose to 45.6% in the premium sedan query and fell to 17.9% in the sporty character query.

Prepared by: Magna Dijital SEO & GEO Team · Editor: Mert Feke · Measured: October 22–26, 2026 · Published: October 30, 2026 · Last updated: November 3, 2026

Research Question: Does the Same Car Need Bring Up the Same Brands?

When a user who wants to buy a car in Türkiye brings the same need to ChatGPT, Gemini, Perplexity or Google AI Overview, do they see the same car brands?

Do AI systems converge to the same degree for a user looking for a premium sedan and a user who prioritizes sporty character?

How far do different purchasing needs, such as an electric car, a hybrid, a family SUV or value for money, change the universe of brands?

And a more basic question:

Is seeing a name connected to the automotive ecosystem in an AI answer really the same as getting a car brand recommendation?

In this research, which we at Magna Dijital Pazarlama Ajansı carried out with Magna AI Analyzer, we examined not only which brands appear but also cross-platform overlap, change by purchasing intent, consistency over time and brand identity fit.

Scope and Method of the Research

The research is based on a five-day measurement set taken on October 22, 23, 24, 25 and 26, 2026.

How were the names standardized?

The raw data can contain different spellings of the same car brand. Obvious brand variants such as Mercedes / Mercedes-Benz, Skoda / Škoda, Cupra / CUPRA, Mini / MINI, MG / MG Motor and Volvo / Volvo Car Turkey were unified.

On the other hand, names representing different brand or performance sub-brand levels, such as Mercedes-AMG, Audi Sport or Hyundai N, were not automatically merged into the parent brand.

Likewise, companies and services that are not car brands but were extracted from Google AI Overview results in connection with the automotive ecosystem were not quietly deleted from the data set.

With this controlled approach, the 1,036 appearances were analyzed across 92 standardized unique names.

How was cross-platform overlap measured?

The Jaccard overlap rate (Jaccard similarity) was used to measure the similarity of the name sets two platforms produced for the same purchase query on the same day.

If two platforms produce exactly the same name set, the rate is 100%; if they produce no shared names, it is 0%.

This metric does not show the quality or commercial success of brands, but how far AI platforms meet in the same universe of brands for the same purchasing need.

There Is a Shared AI Core in Car Brands

This research reveals a different visibility structure from our earlier retail and travel studies.

Eight of the 92 standardized names appeared at least once on all four AI surfaces in the combined five-day data set:

The number of brands/names seen across all three of ChatGPT, Gemini and Perplexity is 21.

So it would not be right to say that AI systems build entirely independent universes of brands in the car category.

There is a clear shared core, especially around established car brands.

Purchasing Intent Changes Platform Agreement

Despite this shared core, agreement in brand recommendations is not the same in every purchasing need.

This difference should not be taken to a causal conclusion such as "AI understands premium sedans better."

The more limited conclusion the data shows is this:

As purchasing intent changes, the similarity of the brand candidate sets AI platforms produce also changes significantly.

Premium Sedan: The Purchasing Need Where AI Agrees Most

Taking all four surfaces together, the premium sedan query produced the highest platform overlap in the research: 45.6%.

In this query, established premium brand names such as Mercedes-Benz, BMW, Audi, Volvo and Lexus entered the shared candidate pool again and again across platforms.

Looking only at ChatGPT, Gemini and Perplexity, average overlap in the premium sedan query rises as high as 69.4%.

This is one of the important differences of the car research:

For some purchasing intents, generative AI systems can come close to a quite strong shared universe of brands.

The Brand Universe Diverges More for Sporty Character and Electric Cars

The driving pleasure and sporty character query produced the lowest agreement across the four surfaces: 17.9%.

In the electric car query, average overlap was 20.6%.

Looking only at the three generative AI systems, overlap was 26.2% in the sporty character query and 37.0% in the electric car query.

Neither of these queries forms a shared brand core as narrow and common as the premium sedan query.

Same Question, Different Surface: Why Should Google AI Overview Be Assessed Separately?

One of the most important methodological findings of the car research is that Google AI Overview produces a markedly different universe of names from the other three systems.

The brand set overlap among ChatGPT, Gemini and Perplexity themselves is 47.9% on average.

Google AI Overview's overlap with these three generative AI systems stays at about 8%.

It would not be right to interpret this difference as "Google AI Overview recommends worse cars."

Google AI Overview is not the same kind of answer model; it builds a different discovery surface from search and web sources.

So what the research reveals here is not a quality ranking, but different brand/name extraction depending on the type of surface.

A Car Brand, or a Related Name from the Automotive Ecosystem?

In Google AI Overview results, companies and services with different commercial roles also appeared alongside car brands.

That is why in the research we keep the distinction:

Car brand ≠ a related name in the automotive ecosystem

This matters especially when assessing Google AI Overview, because the surface works differently from the other three generative AI systems.

Which Brands Appeared Most Often Over Five Days?

The chart below is not a ranking of "Türkiye's best car brands" or "the most successful brands in AI."

It only shows how many times selected brands were recorded on different days, in different queries and on different platforms within the 1,036 total observations.

Brands such as BMW, Mercedes-Benz, Toyota, Hyundai and Renault form broad profiles with both high total appearances and spread across more than one purchasing context.

However, the number of total appearances does not explain a brand's profile on its own.

Broad Visibility and Niche Visibility Are Not the Same Thing

This table reveals two different behaviors in visibility.

One brand can appear widely across different AI systems and different purchasing needs.

Another brand can come up again and again only in a specific category query it is strongly associated with.

Broad visibility ≠ niche category visibility.

In Which Purchasing Contexts Does AI See Togg?

Togg does not appear only in the electric car query.

In the research it appeared in these purchasing needs:

• city driving,
• value for money,
• family SUV,
• long-distance driving / comfort / safety,
• premium sedan,
• electric car.

Tesla, on the other hand, although seen on three AI platforms and over all five days in the same five-day data set, appeared only in the electric car query.

How Well Do Brand Recommendations Hold Over Five Days?

Cross-platform overlap and the same platform's behavior over time are separate measurements.

To keep the same methodology as the other Magna studies in the series, consistency over time was calculated from the same platform's consecutive-day brand sets for the same query.

Average consecutive-day consistency across all queries is about 57.4%.

It is notable that the electric car query had the lowest consistency over time in this five-day sample.

However, it cannot be concluded from this data that change in the electric car market caused it.

All the research shows is this:

In this query, the AI systems' brand sets changed more over the five-day measurement window than in the other car purchasing needs.

How Should the Findings Be Read from a B2C GEO Perspective?

This research does not test whether a specific GEO practice causes a specific brand's visibility.

So the data does not support the conclusion:

"If this optimization is done, AI will recommend the brand."

On the other hand, the car research shows that explaining brand visibility with a single total score can hide some important differences.

These dimensions are not a new commercial score or a universal GEO framework.

They are measurement dimensions that need to be kept separate from each other to interpret the research data correctly.

The core distinction:

Appearing in an AI answer ≠ appearing on different models ≠ appearing in different purchasing needs ≠ being represented with the right brand identity.

Magna Dijital's AI visibility analysis and GEO methodology content covers how these concepts are applied outside the research.

What This Research Shows

There is a shared AI brand core in the car category

BMW, Mercedes-Benz, Toyota, Hyundai, Renault, Lexus, Kia and Opel appeared at least once on all four surfaces.

Generative AI systems resemble each other far more than they resemble Google AI Overview

Average brand set overlap among ChatGPT, Gemini and Perplexity is 47.9%. Google AI Overview's overlap with these systems is about 8%.

Purchasing intent strongly changes brand agreement

Average overlap across the four surfaces is 45.6% for premium sedans but falls to 17.9% in the sporty character query.

Broad brand visibility and niche category visibility are not the same thing

Togg spread across six purchasing needs, while Tesla, despite appearing only in the electric car query, was present regularly on three platforms and over five days.

Stability over time also varies by purchasing intent

Consecutive-day consistency was 66.7% in the value-for-money query and 43.5% in the electric car query.

A brand recommendation and a related name in the automotive ecosystem are not the same thing

Especially in Google AI Overview results, different commercial roles such as brands, manufacturers, distributors, tire makers, marketplaces and mobility services can appear in the same extraction pool.

What This Research Does Not Show

This study:

• does not rank Türkiye's best car brands.
• does not measure vehicle quality or reliability.
• does not show unit sales or market share.
• does not make a real car comparison in terms of value for money.
• does not interpret a brand appearing often as consumer preference.
• does not rank AI systems by overall accuracy or quality.
• does not show that Google AI Overview is better or worse than the other AI systems.
• does not treat a high position in a single query as overall AI visibility success.
• does not measure whether AI visibility creates web traffic, leads or sales.
• does not prove that a specific GEO effort increases visibility.

The aim of the research is to examine comparatively which brands and names different AI systems take into the candidate pool when answering car purchasing intents in Türkiye, how far these brand sets change across platforms, purchasing needs and days, and how accurately the results represent the car brand level.

Limitations of the Research

The measurement window is five days. Longer-term studies may show different behavior over time.

The research is limited to Türkiye and Turkish-language queries. Brand sets may change in different market, language and user contexts.

92 unique names do not mean 92 car brands. Companies, services and institutions that are not car brands were also extracted, especially in Google AI Overview results.

Google AI Overview is not the same kind of answer surface as the other three systems. So the low overlap of about 8% should not be interpreted directly as a difference in quality or accuracy.

Brand identity classification was not done manually, with sources, for all 92 names. That is why no "wrong brand rate" or similar percentage is produced in this publication.

Consistency over time was measured across consecutive days. This metric does not mean future brand recommendations will stay the same.

A brand appearing in an AI answer was not treated as real product fit or user preference.

The 10 Queries Used

Q1 — City driving

Türkiye'de şehir içi kullanım için hangi otomobil markalarını önerirsin?
(Which car brands would you recommend for city driving in Türkiye?)

Q2 — Value for money

Türkiye'de fiyat-performans açısından hangi otomobil markaları öne çıkıyor?
(Which car brands stand out for value for money in Türkiye?)

Q3 — Family SUV

Türkiye'de aile kullanımı için en iyi SUV markaları hangileri?
(What are the best SUV brands for family use in Türkiye?)

Q4 — Long-distance driving, comfort and safety

Türkiye'de uzun yol konforu ve güvenlik açısından hangi otomobil markalarını önerirsin?
(Which car brands would you recommend for long-distance comfort and safety in Türkiye?)

Q5 — Premium sedan

Türkiye'de premium sedan otomobil almak isteyenlere hangi markaları önerirsin?
(Which brands would you recommend to people who want to buy a premium sedan in Türkiye?)

Q6 — Luxury SUV

Türkiye'de lüks SUV satın almak için hangi otomobil markaları değerlendirilmeli?
(Which car brands should be considered for buying a luxury SUV in Türkiye?)

Q7 — Driving pleasure and sporty character

Türkiye'de sürüş keyfi ve sportif karakter açısından hangi otomobil markaları öne çıkıyor?
(Which car brands stand out for driving pleasure and sporty character in Türkiye?)

Q8 — Electric car

Türkiye'de elektrikli otomobil almak için hangi markaları önerirsin?
(Which brands would you recommend for buying an electric car in Türkiye?)

Q9 — Hybrid car

Türkiye'de hibrit otomobil konusunda hangi markalar öne çıkıyor?
(Which brands stand out for hybrid cars in Türkiye?)

Q10 — Luxury, prestige and technology

Türkiye'de prestij, konfor ve teknoloji açısından en iyi lüks otomobil markaları hangileri?
(What are the best luxury car brands in Türkiye in terms of prestige, comfort and technology?)

Conclusion: There Is a Shared Brand Core in the Car Category, but No Single AI Ranking

The five-day research shows that car brands can have a stronger shared brand core in AI systems than e-commerce or travel platforms.

The brand sets of ChatGPT, Gemini and Perplexity overlap 47.9% on average.

However, this agreement is not the same across all purchasing needs.

Average overlap across the four surfaces rises to 45.6% in the premium sedan query and falls to 17.9% in the sporty character query.

Google AI Overview, meanwhile, builds a markedly different universe of names from the other three systems. Against overlap in the 45–52% band among generative AI systems, AIO's overlap with these systems is about 8%.

Brand-level examples also show why a one-dimensional visibility score may not be enough.

BMW with 86, Mercedes-Benz with 78, Toyota with 72 and Hyundai with 71 total appearances form broad profiles, while Togg spreads across six different purchasing needs with 33 appearances.

Tesla, despite appearing only 14 times, concentrates these appearances across three platforms, five days and entirely within the electric car query.

That is why, in car AI visibility, the meaningful question is not only:

"Does our brand appear in the AI answer?"

The more meaningful question is:

"In which purchasing needs, on which AI surfaces, how regularly and with how accurate a brand identity is our brand represented?"

About This Research

We prepared this research at Magna Dijital Pazarlama Ajansı, based on a comparative analysis of a five-day multi-platform AI visibility measurement we took on October 22–26, 2026 with Magna AI Analyzer, a tool we developed ourselves.

Magna Dijital is an integrated, data-driven digital marketing agency founded in 2014; our SEO & GEO team runs our GEO and AI visibility work.

Between October 22 and 26, 2026, 10 Turkish-language queries representing the car purchase decision in Türkiye were examined on ChatGPT, Gemini, Perplexity and Google AI Overview.

The aim of the research is not to rank car brands by quality, sales, product success or consumer preference, but to examine comparatively the cross-platform overlap, change by purchasing intent, consistency over time and brand identity fit of the brand and name sets different AI systems produce.

How Can You Use This Research for Your Brand?

If you are a car brand or distributor, you need to track separately in which purchasing needs your brand stands out and how differently you are represented in Google AI Overview and in generative AI systems.

If you want to see the same measurement for your own brand and competitors, choose our AI Visibility Analysis; if you want us to improve how your brand appears in AI answers based on the findings, choose our GEO Services. We explain how we design measurements and interpret findings on our GEO Methodology page.

Related research

• AI Visibility of E-Commerce and Retail Brands in Türkiye 2026 — Platform overlap rose to 52.9% in electronics but stayed at 8.5% in premium fashion.
• AI Visibility of Travel Platforms in Türkiye 2026 — 80.5% of 195 names appeared on only one AI platform; overlap in the Bodrum query was 4.5%.

You can find all of our research on our Magna Research page.