# System cards and model cards: what to read before you adopt a model

- **Published:** 2025-11-21
- **Updated:** 2026-10-12
- **Category:** Models & Benchmarks
- **Author:** Sinthetix
- **URL:** https://sinthetix.com/blog/system-cards-and-model-cards-what-to-read

> Vendors publish system cards and model cards with every major model. Here is which sections matter to a business buyer, and a short checklist for reading them.

When a lab ships a new model it usually publishes a document describing it. These go by two names: a **system card** (broader, covers safety testing of the model as deployed) and a **model card** (shorter, a structured fact sheet). Google describes its model cards as "simple, structured overviews of how an advanced AI model was designed and evaluated" [4]. The documents are written by the vendor, so they are evidence, not an audit. They are still the cheapest due diligence available.

## What the real ones contain

The three examples below are openly available and show how much the format varies.

- **OpenAI's GPT-5 system card** (August 13, 2025) has sections on model data and training, safety evaluations (disallowed content, jailbreaks, prompt injection, hallucinations), red teaming and external assessments, and a Preparedness Framework review [1].
- **Anthropic's Claude Opus 4 and Sonnet 4 system card** (May 2025) covers training data, safeguards results, agentic safety including prompt injection against computer use, and an alignment assessment, and states the safety level each model was deployed under [2].
- **Meta's Llama 3.1 model card** is shorter: model information, intended use, hardware and software, training data, benchmark scores, responsibility and safety, and ethical considerations and limitations [3].

## The sections that matter to a buyer

1. **Intended use and out-of-scope use.** Meta's card states Llama 3.1 is "intended for commercial and research use in multiple languages" and lists uses outside scope [3]. If your use case is not in the intended list, ask why.
2. **Evaluations.** Look for the evaluation method, not just the score. OpenAI's card reports hallucination rates measured with an LLM-based grader that agreed with human reviewers 75% of the time [1]. That is useful context for how far to trust the headline figure.
3. **Limitations and known failure modes.** These are often the most decision-relevant paragraphs.
4. **Safety testing.** Check for prompt-injection results if you plan to give the model tools [1][2].
5. **Data handling.** Anthropic's card says its models were trained partly on data from users "who have opted in" [2]. OpenAI's says training data includes information users or trainers provide or generate [1]. The card describes training, not how your own data is handled, so read the vendor's data-retention documentation as well.

## A reading checklist

| Question | Where to look | Red flag |
| --- | --- | --- |
| Is my use case in scope? | Intended use | Silent on your domain |
| How was it measured? | Evaluation sections | Scores with no method or comparison |
| What goes wrong? | Limitations | Section missing or generic |
| Can it be attacked via inputs? | Prompt-injection and jailbreak tests | No agentic or tool-use testing |
| Whose data trained it? | Training data | No statement on user data |
| Does the card match the model I will call? | Model name and date | Card covers a different version |

Last, record the card's version and date next to the model you adopt. Models change, and a card written for one snapshot may not describe the next.

## Sources

1. [GPT-5 System Card](https://cdn.openai.com/gpt-5-system-card.pdf), OpenAI, 2025-08-13
2. [System Card: Claude Opus 4 & Claude Sonnet 4](https://www-cdn.anthropic.com/07b2a3f9902ee19fe39a36ca638e5ae987bc64dd.pdf), Anthropic, 2025-05-22
3. [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), Meta (llama-models repository), 2024-07-23
4. [Model cards](https://deepmind.google/models/model-cards), Google DeepMind, 2025-11-21

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