/v1/car — car_model

Not just a Toyota. A Land Cruiser 300.

The model classifier distinguishes 2,003 exact models across 145 brands — learning the fine visual differences in grilles, lights and body lines that separate a Camry from a Corolla or an X5 from an X6.

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2,003

exact models

145

brands covered

85.2%

model accuracy

Every model we recognize

Search by model name or filter by brand. This is the live class list of the model network — if it’s here, the API can name it.

Showing 30 of 2003 models

BrandModel
Toyota4Runner
Toyota86
ToyotaAVANZA
ToyotaAllex
ToyotaAllion
ToyotaAlphard
ToyotaAltis
ToyotaAqua
ToyotaAurion
ToyotaAuris
ToyotaAvalon
ToyotaAvensis
ToyotaBelta
ToyotaBlade
ToyotaBrevis
ToyotaC-HR
ToyotaCOASTER
ToyotaCaldina
ToyotaCamry
ToyotaCamry Gracia
ToyotaCamry IX
ToyotaCarina
ToyotaCelica
ToyotaChaser
ToyotaCorolla
ToyotaCorolla Axio
ToyotaCorolla Ceres
ToyotaCorolla Cross
ToyotaCorolla Fielder
ToyotaCorolla Rumion

Two networks, one honest verdict

The final answer is not a single network’s guess. A 181-class brand specialist and the 2,003-class model network each make an independent prediction, and calibrated confidence gates fuse them into one make_model verdict.

When the two networks disagree and neither is confident, the API withholds the answer — brand_uncertain is set to true and the label comes back null. A decision field tells you which fusion rule fired, so uncertain results are always distinguishable from confident ones.

  • — Agreement between both networks produces the highest-confidence verdicts.
  • — A confident brand specialist can veto an implausible model prediction.
  • — Unresolvable conflicts return null — never a silent wrong answer.

networks agree

"make_model": {
  "brand": "Toyota",
  "model": "Land Cruiser 300",
  "confidence": 0.91,
  "decision": "agreement"
}

networks disagree — no guess

"make_model": {
  "brand": null,
  "model": null,
  "brand_uncertain": true,
  "decision": "conflict_low_confidence"
}

85.2% across 2,003 classes

The hardest task in vehicle AI

Telling 2,003 model classes apart is far harder than reading a badge: the network must learn generation-level styling cues across millions of photos. It scores 85.2% overall accuracy, measured on hundreds of thousands of real listing photos the network never saw during training.

Frequently asked questions

What format do model predictions come in?

Each prediction is a brand and model pair — for example Toyota with Land Cruiser 300, or BMW with X5 — plus a confidence score and the top-5 candidate list. The fused make_model object also reports which network decided and why.

What happens if my exact model is not covered?

The brand classifier still covers 181 makes, so you get a reliable brand verdict even when the model network has no matching class. You can also request coverage — the class list grows with every retraining cycle.

Can it tell apart similar models like X5 and X6?

Yes — that is exactly what the network is trained for: fine-grained differences in grilles, lights, rooflines and proportions. For close calls, use the confidence score and top-5 candidates to decide how to handle the result.

Does it distinguish model generations?

Some classes are generation-specific (for example Land Cruiser 200 Series vs Land Cruiser 300), and the training data spans multiple generations per model. Where generations share a class, the API returns the common model name.

How do I get only brand and model without the other analysis?

Call /v1/car with body_type=false, color=false and position=false. Only the brand and model networks run, and the response returns faster.

Start recognizing cars today

Free tier included — 150 requests per month, no credit card required. Your first car profile is one POST request away.

Get Free API KeyBrowse all 181 brands