AI Model Card Generator (2026)

A model card is a short structured document that describes a machine learning model — what it does, how it was trained, how it performs, and its known limitations. The format was introduced by Mitchell et al. in the 2019 FAT* paper "Model Cards for Model Reporting" and has since become the de-facto industry standard, adopted by Google, Hugging Face, Microsoft, IBM, and OpenAI. Model cards are now referenced explicitly in EU AI Act Annex IV, NIST AI RMF, and Colorado SB 24-205.

No U.S. or EU statute requires the specific "model card" format, but multiple laws require the underlying content. EU AI Act Annex IV requires technical documentation covering nearly identical sections. California AB 2013 requires a training data summary. Colorado SB 24-205 requires a deployer impact assessment. NIST AI RMF measure-manage functions expect model documentation. A well-formed model card satisfies all four in one artifact.

Eight sections cover 95% of what regulators and enterprise customers ask for: (1) model details (name, version, owner, type, license, contact), (2) intended use (primary use case, users, out-of-scope), (3) training data (summary, sources, time period, personal data), (4) evaluation (datasets, metrics, subgroup performance), (5) ethical considerations (risks, mitigations, fairness audit), (6) deployment and operations (context, monitoring, human oversight, incident response), (7) regulatory posture (EU AI Act, NIST, ISO/IEC 42001, HIPAA, GDPR), (8) references and contact.

Annex IV requires a general description of the AI system (model card section 1-2), a detailed description of elements including design and training (section 3), assumptions about the persons or groups on which the AI system is intended to be used (section 2), performance metrics and appropriateness (section 4), risk management and steps taken to mitigate (sections 5-6), monitoring (section 6), and the technical file being kept up to date. A model card is the human-readable summary; Annex IV also requires deeper artifacts, but the model card indexes them.

A model card describes a single model artifact. A system card describes a product built from one or more models — including retrieval-augmented pipelines, safety filters, human-review layers, and business logic. OpenAI publishes system cards for GPT-4, GPT-4o, and Sora. For most AI compliance programs you need both: a model card per foundational model and a system card per product feature.

Yes — the deployer is on the hook to document how a third-party foundation model is used. Even if you use OpenAI, Anthropic, or Mistral models unmodified, you still need to document the specific version, evaluation on your data, prompts and system instructions, guardrails, monitoring, and human oversight. Vendors often publish upstream model cards you can reference as an appendix.

Every material change: new training data, retraining, new safety filter, new deployment context, new evaluation set, or discovered risk. Time-stamp each release and maintain a version history. Colorado SB 24-205 explicitly requires post-deployment monitoring and an annual impact assessment refresh, so plan on at least annual review.

No. This generator is client-side only. Nothing is transmitted to our servers. Download the Markdown locally and commit it to your GRC repo or Notion.

Yes — copy the iframe embed snippet below onto any page. The tool is free to use and free to embed. Please keep the link back to aicompliancevendors.com so users can access updates.

Publish both a human-readable Markdown or HTML page on your website (indexed by search engines and easy to share with customers) and a machine-readable JSON version (schema.org/CreativeWork or Hugging Face model-card YAML) for downstream tooling. Some regulators may also request a PDF for a specific technical file.

Enterprise buyers now routinely ask for a model card as part of vendor security review. Having a well-formed, up-to-date card on your website — indexed and cited — shortens the sales cycle. This is one of the reasons more vendors publish public model cards even when not required.

Fill "TBD" and leave the section visible. Regulators, buyers, and auditors treat missing sections as a red flag, but honest TBDs paired with a documented completion plan are viewed as good faith.