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Choose the model whose provider can give you current, version-specific evidence that you can retain—not merely a polished marketing page. Before signing, obtain the provider’s Article 53 technical-documentation index, downstream information pack, Union-copyright policy, public training-content summary, model or system card, evaluation and safety reports, and a dated change-notification process. Confirm that each document names the exact model, release or endpoint, modalities, and update date. Then map disclosed limitations and evaluation gaps to your own human-oversight, monitoring, security, and incident procedures. The EU AI Act’s general-purpose AI (GPAI) obligations have applied since 2 August 2025; enforcement powers apply from 2 August 2026, with older pre-2 August 2025 models generally needing compliance by 2 August 2027. This guide shows how to compare evidence and preserve it for the life of your deployment.
What evidence should an AI provider supply under the EU AI Act?
The core test is whether the provider supplies two complementary documentation sets, plus the copyright and training-data disclosures required for GPAI providers. One set is prepared for authorities; the other is intended for downstream AI-system providers such as an organisation integrating the model into a product or internal workflow. Both must remain current over the model’s lifecycle.
Authority-facing technical documentation
Article 53 requires a provider to draw up and maintain technical documentation about the model and make it available to the AI Office or other competent authorities on request. Commission FAQs describe information covering the model’s architecture, development process, training, testing, validation, computational resources, energy consumption, and evaluation results. Ask for a documentation index even when the underlying material is confidential; the index should identify what exists, its owner, version, date, and the access procedure.
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Downstream documentation must be practical enough for an integrator to understand what the model does and how to connect it safely. It should cover intended tasks, capabilities and limitations, supported modalities, technical integration requirements, input and output specifications, and relevant characteristics of the training data. Request the interface contract for the exact endpoint you will use, not a generic description of the provider’s product family.
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Copyright policy and training-content summary
Article 53 also requires a Union-copyright policy and a public summary of the content used to train the GPAI model. The public summary is not necessarily a complete item-by-item dataset list, and technical documentation supplied to authorities or downstream providers may contain confidential information. Treat the summary and policy as required evidence, but do not mistake their existence for proof that every possible copyright or data-provenance risk has been eliminated.
How do the EU AI Act dates affect a model-selection decision?
Use the dates as procurement checkpoints, not as a reason to defer documentation. GPAI provider obligations began applying on 2 August 2025. The Commission’s enforcement powers for those obligations enter application on 2 August 2026, and GPAI models placed on the market before 2 August 2025 are expected to comply by 2 August 2027.
| Date or threshold | What it means for a buyer |
|---|---|
| 2 August 2025 | GPAI provider obligations began applying, including technical documentation, a copyright policy, and a public training-content summary. |
| 2 August 2026 | European Commission enforcement powers for GPAI obligations enter application. Have your evidence register and escalation route ready before this date. |
| 2 August 2027 | GPAI models placed on the market before 2 August 2025 must meet the applicable obligations by this transition date. |
| More than 1023 FLOP | The Commission’s GPAI fact page describes models trained above this compute level and able to generate language as GPAI under its guidance. Confirm classification with your provider rather than inferring it from model size alone. |
| More than 1025 FLOP | The Commission says a model is presumed to pose systemic risk above this training-compute threshold; the threshold is under review. |
The General-Purpose AI Code of Practice can provide additional detail on Articles 53 and 55 until a harmonised standard is published. It is a useful interpretive reference, but your contract and evidence register should still identify the legal duties that apply to the provider and to your own role.
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What is the difference between authority documentation, downstream documentation and a model card?
They answer different questions. Authority documentation demonstrates how the provider developed and evaluated the model; downstream documentation lets you integrate and operate it; a model or system card communicates important behavior and limitations in a form that developers, policy teams and users can review.
| Artifact | Primary audience | Information to look for | How to use it in selection |
|---|---|---|---|
| Technical documentation | AI Office and other authorities | Architecture, development, training, testing, validation, compute, energy use, evaluations and lifecycle controls | Confirm that a controlled, current record exists and that access can be provided when legally required. |
| Downstream-provider information pack | Integrators and other downstream AI-system providers | Intended tasks, modalities, interfaces, input/output formats, integration requirements, data characteristics, capabilities and limitations | Determine whether your engineers and risk owners can implement the model without guessing. |
| Model or system card | Developers, governments, policy teams and end users | Intended use, benchmarks, evaluation methods, limitations, safety testing, red-team findings, release date and update history | Compare candidates on the same evidence and identify unresolved risks before a pilot. |
| Public training-content summary | Public and downstream stakeholders | Descriptive summary of training content, with the provider’s stated scope and update date | Assess provenance and copyright questions; do not treat it as a complete dataset inventory. |
| Union-copyright policy | Authorities, rights holders and customers | The provider’s process for complying with applicable Union copyright law | Check that the policy is current, owned by a named function and incorporated into vendor due diligence. |
Google’s responsible-AI guidance describes model, data and system cards as structured transparency artifacts and says they should communicate intended use and summarize evaluations. Google DeepMind’s catalog dates individual cards for Gemini, Gemma, generative and robotics models; use the exact model version and card date in your procurement record rather than assuming every version has identical disclosures. Anthropic states that each new model release has a model card or addendum covering capabilities and benchmark performance, known limitations and risks, safety evaluations and red teaming, training information and related practices. OpenAI’s EU AI Act customer guidance says it publishes public training-content summaries for its GPAI models and provides customers with EU AI Act information and a route to request relevant model documentation. In every case, verify the exact model, endpoint, access conditions and update date in the provider’s portal or contract.
How should I compare candidate models for transparency?
Score each candidate against the same seven axes and require evidence for every score. A provider that publishes many pages but cannot tie them to the endpoint you will call is less transparent for procurement purposes than one with fewer, precisely versioned documents.
1. Legal coverage
Ask whether the provider explicitly addresses Article 53 technical documentation, a Union-copyright policy and the public training-content summary. Record the document title, owner, publication date and the model versions covered.
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2. Version control
Check that cards, evaluations and training summaries identify a release, snapshot, API endpoint or other immutable identifier. Require a history of updates and a notice period for changes to the model, modalities, safety behavior or documentation.
3. Downstream usability
Look for supported input and output formats, context or file limits when stated, rate and authentication requirements, latency or availability commitments if they are material to your use, and integration prerequisites. The provider should explain capabilities and limitations in operational terms rather than only describing the model’s intended purpose.
4. Evaluation depth
Prefer reports that disclose benchmark methods, test populations, evaluation dates, safety evaluations, red-team scope, known limitations and unresolved risks. A benchmark number without the test method, version and confidence context is weak evidence for a production decision.
5. Systemic-risk readiness
If the model may fall within the systemic-risk provisions, request evidence of notification, risk assessment and mitigation, evaluations, incident reporting and cybersecurity controls. Do not infer systemic-risk readiness from a general security certification or a marketing statement.
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6. Operational access
Confirm that your team can download or otherwise retain the relevant records, that confidential material can be accessed by the right legal and security personnel, and that the provider has a process for sending updates. A public webpage that can change without an archive is not a sufficient evidence-retention plan.
7. Contract and geography
Identify the provider’s EU entity or authorised representative where applicable, the contracting party, the governing service terms and the route for regulatory requests. Ensure the contract preserves your access to compliance evidence after an endpoint is retired or your account changes.
What procurement process should I follow?
Run the same evidence workflow for every finalist and retain the results in a dated register. This keeps model selection tied to your legal role and actual deployment rather than to a generic “compliant” label.
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- Classify the candidate and your role. Determine whether the candidate is a GPAI model and whether your organisation is acting as a provider, deployer, importer, distributor or downstream integrator. Document who places the model or system on the EU market and who controls the final use.
- Request the two documentation packages before signing. Ask for the authority-facing technical-documentation index and the downstream-provider information pack. Require the exact model name, version or endpoint identifier, release date and contact responsible for updates.
- Verify the public artifacts. Collect the training-content summary, Union-copyright policy, model or system card, evaluation reports, acceptable-use policy and release or update dates. Save copies or immutable references according to your retention rules.
- Map disclosures to your risk assessment. For every limitation, failed evaluation, unavailable benchmark or red-team finding, specify the compensating control: human review, restricted scope, input filtering, output validation, monitoring, fallback handling or a decision not to deploy.
- Check systemic-risk duties where relevant. Ask for evidence of notification, risk-management measures, evaluations, incident-reporting arrangements and cybersecurity controls when the model’s classification or capability makes those duties applicable.
- Set contractual change controls. Require notice when the model version, modalities, safety behavior, training-data summary, terms or documentation changes. Define whether you can reject a material change, pin an earlier version, or run a new validation before continued use.
- Create and maintain an evidence register. Record the document name, exact model or endpoint, provider, date obtained, source location, confidentiality level, internal owner, review date and linked control. Recheck it against the 2 August 2026 enforcement date and the 2 August 2027 transition date for older models.
What should an evidence register contain?
A register turns transparency claims into auditable records. Keep one row per artifact and one change log per model version.
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- Legal artifacts: technical-documentation index, downstream information pack, copyright policy and training-content summary.
- Evaluation artifacts: model or system card, benchmark methods and results, safety evaluations, red-team reports, known limitations and unresolved risks.
- Operations: interface specification, input/output constraints, authentication and integration requirements, service-change process and incident contact.
- Governance: internal risk owner, human-oversight design, monitoring metrics, escalation thresholds, review date and approval status.
- Provenance: date retrieved, document revision, URL or portal location when available, retained copy or hash, and the person who verified the scope.
Separate public evidence from confidential evidence. The Act does not make every provider disclosure public, so your register should show which materials are available to authorities or downstream providers under controlled access and which are publicly reviewable.
When can an open-source model use documentation exemptions?
Open-source status is conditional, not automatic. The Commission says the Article 53(1)(a) and (b) documentation exemptions can apply when a qualifying free or open-source licence is used and the model’s parameters, architecture and usage information are publicly available. The exemption does not apply to a GPAI model that poses systemic risk.
Before relying on an exemption, verify the licence, the completeness and accessibility of the parameters, architecture and usage information, and the model’s systemic-risk status. Keep the verification in your register and continue to assess obligations that may apply to your downstream AI system, deployment context or other role.
How do I evaluate training-data transparency without overclaiming?
Start with the public training-content summary, then ask targeted questions about scope and limits. A useful summary identifies broad content categories, collection or filtering practices where disclosed, update date and the model versions covered. It may not reveal every source, record or licensed item, and confidentiality or security constraints can limit detail.
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What systemic-risk questions should I ask a provider?
For a model that may be presumed to pose systemic risk above the Commission’s 1025 FLOP training-compute threshold, ask for evidence that goes beyond ordinary model-card transparency. The threshold is under review, so ask the provider to explain its current classification and the basis for it.
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- Has the provider notified the relevant authority and recorded the model as potentially systemic risk where required?
- What risk-identification and mitigation process covers dangerous capabilities, misuse, loss of control and model updates?
- Which evaluations and adversarial tests were run, by whom, on what version and with what unresolved findings?
- How are serious incidents detected, triaged, reported and communicated to downstream customers?
- What cybersecurity controls protect weights, training infrastructure, interfaces and monitoring data?
- What changes trigger a new evaluation, customer notice or contractual review?
Ask for evidence that is specific to the model and release you will use. A corporate security overview cannot substitute for model-specific risk management, evaluation, incident and cybersecurity records.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What common transparency mistakes should buyers avoid?
Relying on a “compliant” badge
Compliance is a set of duties and records, not a single label. Ask which provider role, model version and obligations the statement covers, and obtain the underlying documents.
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Undated material cannot establish what was evaluated or disclosed at the time of your decision. Require a publication or update date and an immutable version or endpoint reference.
Confusing public information with downstream documentation
A public summary may omit interface specifications, data characteristics or confidential technical details needed for integration. Request the downstream-provider pack separately.
Ignoring model updates
Silent changes can invalidate your validation and risk assessment. Pin versions where feasible and require notice, re-evaluation and approval for material changes.
Using benchmark scores without methods
Scores are not comparable when datasets, prompts, evaluators or versions differ. Record the method, date, population and limitations alongside every number.
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Assuming open source removes all duties
The exemption is conditional and does not cover systemic-risk GPAI models. Verify the licence, public information and classification before relying on it.
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Failing to plan evidence retention
If a provider retires an endpoint or changes a portal, a live link alone may not preserve your proof. Retain dated copies or controlled references and assign an owner to review them.
How should I make the final model decision?
Select the candidate with the strongest evidence-to-risk fit, not necessarily the largest benchmark score. A practical decision record should state the intended tasks, the exact model version, the evidence available, the limitations accepted, the controls required, the owner who approved residual risk and the conditions that would trigger re-evaluation.
Reject or pause a candidate when the provider cannot identify the model version, will not supply downstream integration information, offers no update or incident-notification process, or leaves material training, copyright, safety or systemic-risk questions unanswered. A smaller or less capable model with stable documentation and controllable behavior can be the safer choice for a regulated deployment.
“Providers of general-purpose AI models must draw up and maintain technical documentation about the model, including information about the development process, to provide to the AI Office upon request.” — European Commission, Guidelines on obligations for General-Purpose AI providers.
“Documentation is a key method of achieving transparency for developers, governments, policy actors, and end users of your product.” — Google AI for Developers, Responsible Generative AI Toolkit.
Frequently Asked Questions
Can a model card alone prove that an AI model complies with the EU AI Act?
No. A model card is one transparency artifact and may not include the authority-facing technical documentation, downstream-provider information, copyright policy or training-content summary required for GPAI providers. Use it alongside dated contractual and regulatory evidence tied to the exact model version.
What if a provider refuses to share confidential technical documentation?
Ask for an index describing the withheld material, the legal or security reason for restricted access, and a controlled route for your authorised reviewers or competent authorities. Record the restriction and assess whether the remaining evidence is sufficient for your use case before approval.
Do EU AI Act documentation duties apply to an API customer?
The provider’s GPAI duties and your organisation’s duties depend on your role and on the final AI system you build or deploy. An API customer acting as a downstream integrator still needs enough provider information to assess integration, limitations, oversight and risk controls.
How often should an AI model transparency review be repeated?
Repeat it whenever the provider changes the model version, endpoint behavior, modalities, safety documentation, training-content summary or contractual terms, and at the internal review interval set by your risk assessment. A scheduled review before major regulatory milestones is also prudent.
What should I do when two providers disclose different benchmark results?
Compare the evaluation method, dataset, prompt or task definition, model version, date and scoring rules before comparing numbers. If methods remain incomparable, prioritize the provider that discloses limitations and reproducible methodology, then run a task-specific validation using your own data and controls.
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