# Model card

**Source:** https://promtable.com/glossary/model-card

> A model card is structured documentation accompanying a released model — what it does, what it was trained on, its evaluation results, intended uses, and known limitations.

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A model card is structured documentation accompanying a released model — what it does, what it was trained on, its evaluation results, intended uses, and known limitations.

Model cards (Mitchell et al., 2018) became a de facto standard by 2026 for both open-weight and closed-API model releases. A good card lists: training data composition, architecture, evaluation scores on standard benchmarks, intended use cases, out-of-scope uses, known biases and failure modes, license, and a contact for issues. Hugging Face requires them; major labs (OpenAI, Anthropic, Google, Meta) publish detailed system cards alongside each release. Reading the model card is the first step before adopting a new model in production — it tells you what the model is for, what it isn't, and how the lab measured it.

## Common mistakes

- Comparing benchmark numbers across providers without checking the eval methodology.
- Assuming "strong on benchmark X" means "strong on your task" — model cards rarely match your domain.

## Related terms

- [evals](https://promtable.com/glossary/evals)
- [fine-tuning](https://promtable.com/glossary/fine-tuning)
- [guardrails](https://promtable.com/glossary/guardrails)

## Sources

- [Mitchell et al. 2018 (arXiv)](https://arxiv.org/abs/1810.03993)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/model-card
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