A model card is a document published by a model’s maker describing its capabilities, training approach, intended uses, and limitations — often including its knowledge cutoff and known weaknesses. Model cards accompany major releases from providers like OpenAI, Anthropic, and Google.
Why it matters for brands
Model cards help you understand the engine you’re being represented by. Knowing a model’s knowledge cutoff explains why it might describe your brand with outdated information; knowing its intended uses and limitations helps you interpret how it behaves.
What to take from it
- Knowledge cutoff matters. If a model’s training data predates your rebrand, launch, or recent coverage, expect lag in training-based answers — retrieval-based engines will reflect changes faster.
- Limitations are disclosed. Providers often note where models are less reliable, which can explain hedging or errors.
- Updates are documented. New model cards signal behavior changes worth re-baselining against.
See navigating AI model updates and the related concept of a benchmark.