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Classify the model itself, not just the application built around it. Under Article 3(63), a general-purpose AI (GPAI) model displays significant generality, can competently perform a wide range of distinct tasks, and can be integrated into different downstream systems or applications. The European Commission’s indicative technical screen flags models trained with more than 1023 floating-point operations (FLOP) that generate language, text-to-image, or text-to-video outputs. That screen is not a safe harbor: a model below it can still require a case-specific assessment.
After deciding that a model is GPAI, make a separate systemic-risk assessment. Article 51 presumes high-impact capability when cumulative training compute exceeds 1025 FLOP, while the Commission can designate a model because its capabilities or impact are equivalent. Finally, check whether the model has been placed on the EU market, including through integration into a marketed AI system. Keep a documented classification file because the Commission says its guidelines are non-binding and the legal result depends on the model’s facts.
What does Article 3(63) mean by a general-purpose AI model?
The legal test is functional rather than based on a product label such as “large language model.” Article 3(63) describes a model that displays significant generality and is capable of competently performing a wide range of distinct tasks. It must also be suitable for integration into varied downstream systems or applications.
Assess the model’s demonstrated capability, not merely the developer’s intended use. A model advertised for one task may still be general-purpose if it reliably transfers across unrelated tasks. Conversely, a very large model that remains narrowly competent may need a different analysis. Record benchmark results and representative task-transfer examples so another reviewer can understand why you reached your conclusion.
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What “significant generality” requires
- Multiple distinct tasks: test whether the same released model can handle substantially different functions rather than several versions of one narrow function.
- Competent performance: document quality thresholds, error rates or benchmark scores used to decide that performance is competent for each task. Do not equate the ability to produce an answer with reliable competence.
- Downstream integration: show whether other providers can connect the model to different applications, workflows, tools or user interfaces.
- Adaptability: note whether the model performs new tasks through prompting, configuration or tool use without additional model training.
Models used only for research, development or prototyping before they are placed on the market are excluded from the Article 3(63) definition described in the Commission’s explanation. The exclusion ends when the model is made available on the Union market, so retain dated records of each release stage.
Is there a FLOP threshold for GPAI?
There is an indicative threshold, not a definitive one. The Commission’s technical screen treats a model as within the indicative GPAI criterion when cumulative training computation is above 1023 FLOP and the model generates language, text-to-image or text-to-video output. Both elements matter for the screen, but neither replaces the legal assessment of generality and competence.
| Screening question | What to document | How to interpret it |
|---|---|---|
| Did cumulative training exceed 1023 FLOP? | Compute methodology, hardware assumptions, training runs, restarts and precision used. | Above the level is a strong indicator; below it is not an automatic exclusion. |
| What can the model generate? | Supported input and output modalities and the released generation functions. | Language, text-to-image or text-to-video generation activates the Commission’s indicative combination. |
| Does it show significant generality? | Benchmark results and examples covering distinct tasks. | This remains the core legal question in every case. |
| Can it be integrated downstream? | APIs, weights, access terms, tool interfaces and integration diagrams. | Broad integration capability supports GPAI classification even when the product has a narrow interface. |
Compute figures must be cumulative and reproducible. Explain whether an estimate includes pre-training, continued pre-training, fine-tuning runs and failed or repeated runs, and state the confidence level. A threshold number without its method can be misleading, especially when different teams count training operations differently.
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How do I classify a model step by step?
1. Identify the regulated object
Start with the model, not the chatbot, image editor or other interface through which users encounter it. Record architecture, parameter count, context limits, input and output modalities, intended tasks, acceptable-use policy, release date, distribution method and licence. List every downstream integration supplied or anticipated, including retrieval, code execution, browsing, robotics or other tools.
2. Apply the Article 3(63) definition
Ask whether the released model has significant generality and competent performance across many distinct tasks. Use a test set that spans the actual modalities and domains you claim to support. Preserve the prompts, versions, evaluator instructions and results; a conclusory statement that the model is “general” is weak evidence.
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3. Run the indicative technical screen
Calculate cumulative training compute and compare it with 1023 FLOP. Then verify whether the model generates language, text-to-image or text-to-video output. Treat a positive result as a screening signal that triggers a full review, not as the sole legal test. Treat a negative result as an unresolved question when the model otherwise performs broadly.
4. Determine market placement and provider role
Identify who developed or commissioned development and who first makes the model available in the Union, whether for payment or free. A provider outside the EU can be covered when it places the model on the Union market. Map distribution through downloads, an API, a hosted service, a partner or an AI system that incorporates the model.
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5. Assess systemic risk separately
Do not stop after deciding that the model is GPAI. Check cumulative training compute against 1025 FLOP, compare capabilities with the most advanced models, and look for a Commission designation based on equivalent capability or impact. Also evaluate the factors listed in Annex XIII: parameters, data and compute scale; input and output modalities; benchmark performance; adaptability; autonomy; tool access; reach among business users; and numbers of end users.
6. Record the decision and its uncertainty
Write a dated conclusion that states which facts are measured, which are estimates and which remain unknown. Assign owners to close material gaps, and set a review trigger for a new release, a major fine-tune, a new modality, a change in distribution or a Commission designation.
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When does the EU AI Act apply to a provider outside the EU?
Geography of incorporation is not the deciding factor. The relevant question is whether the provider places the model on the Union market. Making a model available to EU customers through a paid or free API, downloadable weights, a hosted product or an integrated AI system can create that connection.
For each route, retain launch dates, regions enabled, customer or user counts, contracts, access logs and the identity of the entity that first made the model available. If several companies are involved, document which entity developed or commissioned the model and which entity performed the market-placement act. This evidence also helps determine who must maintain the GPAI documentation.
What is the difference between GPAI and GPAI with systemic risk?
GPAI is the broad category. GPAI with systemic risk is a higher tier determined by capability, compute or a Commission designation. Article 51 presumes high-impact capability above 1025 training FLOP, but the presumption is not the only route: the Commission may designate a model whose capabilities or impact are equivalent even when the compute figure is lower or uncertain.
| Question | GPAI classification | Systemic-risk assessment |
|---|---|---|
| Primary test | Significant generality, competent performance across a wide range of distinct tasks and downstream integrability. | High-impact capability, including the Article 51 compute presumption or equivalent capability or impact. |
| Indicative compute level | More than 1023 FLOP plus qualifying generation modalities is the Commission’s indicative screen. | More than 1025 cumulative training FLOP creates an Article 51 presumption. |
| Other evidence | Parameters, data, modalities, benchmarks, adaptability and intended uses. | Annex XIII factors, user reach, autonomy, tool access, scalability and real-world impact. |
| Who can decide? | Provider’s documented case-specific classification, subject to legal interpretation. | The provider’s assessment and any Commission designation; the Court of Justice of the European Union provides authoritative interpretation. |
Systemic-risk status can change as capability, reach or deployment changes. A model that was not designated at release may later meet the criteria after a capability upgrade or broad integration, so include post-release monitoring in the compliance process.
What obligations apply to every GPAI provider?
The Commission’s obligations information states that the following requirements applied from 2 August 2025. They attach to GPAI providers regardless of whether the model is ultimately classified as systemic risk.
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- Technical documentation: prepare documentation for downstream providers and authorities, describing the model and the information needed to use and assess it.
- Copyright policy: maintain a policy addressing compliance with applicable Union copyright law.
- Public training-content summary: publish a summary of the content used to train the model in the form required by the applicable rules.
Keep versioned copies of each document and connect them to the exact model release. A generic policy that cannot be tied to a training run, model version or distribution route is difficult to defend.
What extra obligations apply to systemic-risk GPAI providers?
Systemic-risk providers must meet the GPAI requirements and add controls aimed at large-scale harm. The available data identifies these duties as:
- Notify the Commission when required for a systemic-risk model.
- Perform standardized model evaluation, including conducting and documenting adversarial testing of the model.
- Assess systemic risks and implement measures to mitigate them.
- Report serious incidents to the Commission without undue delay.
- Provide adequate cybersecurity for the model and the physical infrastructure on which it runs.
Adversarial testing should be reproducible: define threat scenarios, test access, findings, severity ratings, remediation owners and retest dates. Incident and security records should cover both model behavior and attacks against servers, storage, credentials and deployment infrastructure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do open-source, fine-tuned or internal models get an automatic exemption?
No blanket exemption follows from a label such as “open source,” “fine-tuned” or “internal.” The classification still turns on the model’s generality, competence, integration potential and market-placement facts. A fine-tuned derivative may require its own assessment when its capabilities, modalities or distribution change materially.
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What should be in a GPAI classification file?
A reviewer should be able to reproduce both the technical screen and the legal reasoning. Include:
- Architecture, parameter count, context limits and all input/output modalities.
- Training-compute methodology, cumulative FLOP estimate, hardware and precision assumptions, and confidence range.
- Training-data description, parameter and data scale, and the copyright policy.
- Benchmark results, task-transfer examples, evaluation protocols and known limitations.
- Intended tasks, acceptable-use rules, autonomy, tool access and adaptability without additional training.
- Release dates, licences, API or download terms, hosting arrangements and downstream integration diagrams.
- Identity of the developer, commissioning entity and first market-placement provider.
- EU business-user and end-user reach, customer records and distribution geography.
- Systemic-risk analysis against Article 51 and Annex XIII, including any Commission notification or designation.
- Public training-content summary, technical documentation, risk assessments, incident logs and cybersecurity controls.
How should I handle a borderline or incomplete assessment?
Use a written comparison rather than a single threshold. Score the model on breadth and competence of distinct tasks; modalities and output types; cumulative training compute; parameter and data scale; benchmark performance and adaptability; autonomy, scalability and tool access; Union user reach; and the route by which it was placed on the market.
| Borderline fact | Useful evidence | Practical response |
|---|---|---|
| Compute estimate near 1023 or 1025 FLOP | Independent reconstruction of runs, hardware logs and precision assumptions. | Record a range and test both the indicative GPAI screen and systemic-risk presumption. |
| Broad capability but narrow product interface | Weights or API behavior, internal evaluations and downstream integration options. | Assess the model itself; do not rely on the interface’s marketing description. |
| Unknown EU reach | Access controls, contracts, logs and reseller records. | Establish whether Union users can obtain the model and who enabled that access. |
| Major fine-tune or new modality | Before-and-after benchmarks, compute records and release documentation. | Open a new classification review instead of carrying forward the old conclusion. |
When evidence is missing, state the assumption and confidence level, obtain the missing record where possible, and use a conservative interim control for market releases. The Commission expressly says its guidelines are not legally binding, so a threshold-only memo is not a substitute for legal advice based on the consolidated regulation.
How often should the classification be revisited?
Recheck the consolidated regulation, Commission guidance, any designation and any delegated act before publishing a compliance conclusion. The Act allows thresholds to be adjusted through delegated acts as technology changes. Reopen the file after a major training run, capability upgrade, modality expansion, substantial increase in user reach, new downstream integration or regulatory designation.
Frequently Asked Questions
Can a model below 10^23 FLOP still be treated as GPAI?
Yes. The Commission’s compute-and-modality combination is an indicative screen, not a minimum safe harbor. A smaller model with significant generality, competent performance across many distinct tasks and broad downstream integration still needs a documented case-specific assessment.
Does putting a model behind an API change its legal status?
An API changes the distribution evidence, not the underlying capability analysis. It can also establish market placement when EU users can obtain the model, so preserve access terms, launch dates and the entity that enabled availability.
What if the training-compute number cannot be reconstructed precisely?
Use a documented range, identify omitted runs and assumptions, and explain the confidence level. Uncertainty near either indicative threshold should trigger a full capability, reach and impact review rather than an automatic low-risk conclusion.
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Who has the final authority when Commission guidance and a provider assessment differ?
The Commission’s guidelines are non-binding. The consolidated EU AI Act and, ultimately, authoritative interpretation by the Court of Justice of the European Union control the legal meaning; a Commission designation can also change a model’s systemic-risk status.
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