BMW Sells 105 Models. Tesla Sells Six.

Two brands can sit next to each other in a reliability table while running completely different businesses. BMW carries 105 models; Tesla sells six. That single number quietly breaks most rankings you read.

By ·Aug 8, 2026·2 min read
Row of different car models lined up at a dealership
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When a table says Brand A has more recalls than Brand B, the first question should be how many different vehicles each one sells. That number varies far more than people assume.

I counted every model we track, by brand, for the ones currently in our index.

FIG.01·Models tracked per brand
BrandModels in our index
BMW105
Ferrari71
Chevrolet62
Ford59
Audi50
Saab7
Tesla6
Genesis6
ROWS 8·SOURCE ForCar

A seventeen-fold spread, and that's before you consider that a "model" can mean a nameplate sold for forty years or one sold for three.

Why this wrecks every ranking built on totals

Take recalls. We found Ford leading every brand on raw recall count, and it does — but a company selling dozens of nameplates across cars, trucks and vans will collect more campaigns than one selling nine models, regardless of engineering quality.

The same distortion runs through complaint counts. The five most complained-about model years in America are all Fords, and volume explains most of it before any fault does.

It even affects averages. A brand with a wide catalogue includes commercial vans and heavy trucks, which score differently from passenger cars — so a "brand reliability average" is partly a measure of what the company happens to build.

What a wide catalogue means for a buyer

Parts and expertise scale with volume. A model from a brand that sells millions across a dozen nameplates shares components with its siblings. That's why a Sierra and a Silverado are mechanically the same truck — and why parts for either are cheap and every mechanic knows them.

A narrow catalogue means the model is the company. When a brand sells nine things, each one gets more engineering attention per unit — but there's less shared-parts cushion if something goes wrong, and fewer independent specialists.

Beware the long tail. Wide catalogues contain models that sold in tiny numbers for two or three years. Those are the ones where parts get scarce, and they're invisible in a brand-level average.

How to compare brands honestly

Use per-model figures rather than totals. That's why our brand pages show recalls alongside the number of models and the average score across rated models, instead of a single headline count — the brand hub puts them side by side.

And go one level deeper before buying. A brand average is composed of models that can differ enormously: on any given marque our best and worst rated models routinely sit a full star apart. The signature failure of each brand is more useful than its ranking, and the individual car matters more than either — which is what a VIN check is for.

Frequently asked questions

Why does the number of models a brand sells matter?

Because most brand rankings use totals. A company selling dozens of nameplates collects more recalls and complaints than one selling nine, regardless of engineering quality — the count measures catalogue size as much as reliability.

Is a brand with fewer models more reliable?

Not inherently. A narrow line-up concentrates engineering attention per model but offers less shared-parts availability and fewer independent specialists who know the cars.

How should I compare brands fairly?

Use per-model figures rather than totals — recalls per model, average score across rated models — and then look at the specific model and year, where variation within a brand is usually larger than variation between brands.

What is the long tail problem?

Wide catalogues include models that sold in small numbers for a few years. Parts for those get scarce and expertise is thin, but they disappear inside a brand-level average.

brandsrecallsreliability
Denis Kataev
Founder & Editor · Serial Solopreneur

Denis Kataev is a serial solopreneur and the founder of ForCar. With 15+ years in software engineering and 10 years in SEO, he builds data-driven products end to end — backed by a sharp eye for design. At ForCar he mines proprietary vehicle datasets, turning raw numbers into buying advice you can actually trust.

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