A GPAI model (General Purpose AI model) is an AI model trained on large amounts of data that can competently perform a wide range of distinct tasks and be integrated into many different downstream systems or applications. Unlike AI built for a single job, a GPAI model is flexible by design. The EU AI Act introduced a formal legal definition for GPAI models in 2024, and as of August 2026, enforcement is active, meaning real compliance obligations now apply to businesses that develop, deploy, or build on these models. This article works through the questions business leaders are asking most, from what qualifies as a GPAI model to what your compliance obligations actually are.
How does a GPAI model differ from narrow AI?
A GPAI model differs from narrow AI in its scope of capability. Narrow AI is trained to perform one specific, fixed-purpose task, such as filtering job applicants or upscaling images. A GPAI model, by contrast, can write legal summaries, draft marketing emails, generate code, and reason through complex questions, all within the same model, without being retrained for each task.
The EU AI Act’s definition, set out in Article 3(63), captures this distinction precisely: a GPAI model “displays significant generality and is capable of competently performing a wide range of distinct tasks regardless of the way the model is placed on the market.” The phrase “regardless of the way the model is placed on the market” matters. It means the classification follows the model’s capabilities, not how a provider chooses to package or market it.
The Act also distinguishes between two related but separate concepts. A GPAI model is the underlying AI model itself. A GPAI system is what you get when that model is integrated into a downstream application, such as a chatbot, a content tool, or an enterprise assistant. This distinction shapes how compliance responsibilities are assigned across the supply chain.
GPAI currently encompasses two main categories: generative AI models that produce text, images, video, or code, and foundation models that are broadly applicable without necessarily generating new content. The DEKRA analysis of GPAI models notes that the capacity to generate language, whether text or speech, is treated as a strong indicator of general-purpose capability, because language sits at the centre of reasoning and knowledge representation.
One practical clarification worth noting: a model trained using enormous compute for a single narrow task, such as increasing image resolution, is not considered a GPAI model even if its training compute exceeds certain thresholds. The test is always capability breadth, not compute alone.
What makes a model qualify as a GPAI under the EU AI Act?
Under the EU AI Act, a model qualifies as a GPAI model when it meets three cumulative conditions: its training compute exceeds 10²³ floating point operations (FLOPs), it displays significant generality across a wide range of distinct tasks, and it can generate language (text or audio), text-to-image, or text-to-video outputs. The European Commission published these criteria in its July 2025 GPAI Guidelines.
The 10²³ FLOPs threshold is indicative, not absolute. A model below that threshold may still qualify if it displays substantial generality across tasks. A model above it may escape classification if it genuinely lacks general capabilities, for example, if it was trained exclusively for transcription, weather forecasting, or gaming. The threshold is a presumption, not a verdict.
In practice, the models that clearly fall within scope are the large language models most businesses already use: GPT-4, Claude Sonnet, Gemini Pro, and Llama 4 all meet the definition. The models that clearly fall outside it are purpose-built, single-task systems.
Two additional scoping rules matter for businesses that modify or build on existing models. First, models used only for internal research, development, or prototyping before market placement are excluded from the GPAI definition. Second, a downstream company that fine-tunes an existing GPAI model only becomes a GPAI provider in its own right if the modification is substantial, meaning it uses more than one-third of the compute of the original model. Ordinary fine-tuning, prompt engineering, and few-shot learning do not cross that line.
The Act also applies extraterritorially. Any provider placing a GPAI model on the EU market, regardless of where they are based, must comply. Providers outside the EU must appoint an authorised representative established within the EU before their model reaches the market.
What are the compliance obligations for GPAI model providers?
GPAI model providers face four baseline obligations under Article 53 of the EU AI Act: prepare and maintain technical documentation of the model’s training and testing process; provide downstream AI system providers with information about the model’s capabilities and limitations; establish a copyright compliance policy under EU Directive 2019/790; and publish a public summary of the training data using the mandatory AI Office template.
Each obligation has teeth. The technical documentation must be kept current and made available to the AI Office and national authorities on request. The downstream information obligation ensures that companies building products on top of a GPAI model understand what they are working with, which in turn enables those downstream providers to meet their own compliance requirements. The training data summary template was published on July 24, 2025, and its use is non-optional for in-scope providers.
What extra obligations apply to systemic-risk GPAI models?
Providers of GPAI models with systemic risk face four additional obligations under Article 55: conduct model evaluations, including adversarial testing, to identify and mitigate systemic risks; assess and mitigate systemic risks at EU level, including their sources; track, document, and report serious incidents to the AI Office without undue delay; and maintain adequate cybersecurity for the model and its physical infrastructure.
How can providers demonstrate compliance?
The GPAI Code of Practice, published July 10, 2025, provides a voluntary but practical route to compliance. Providers who sign it benefit from a presumption of conformity with Articles 53 and 55. Open-source GPAI models are exempt from the documentation and downstream information obligations, but not from the copyright policy or training data summary requirements. If an open-source model exceeds the systemic-risk compute threshold, all exemptions are void.
Providers who do not adhere to an approved code of practice or a European harmonised standard must demonstrate alternative adequate means of compliance directly to the Commission.
Does your business count as a GPAI deployer under EU law?
Most businesses are not GPAI model providers. They are deployers: companies that integrate a GPAI model or system into a product and put that product in front of users. A SaaS company calling the OpenAI API to power a customer service chatbot is almost always a deployer, not a provider. The provider is OpenAI. The deployer carries its own, separate set of obligations.
As a deployer, your primary obligations under the EU AI Act fall under Article 50, the transparency rules that activated on August 2, 2026. These include disclosing to users that they are interacting with an AI system, labelling synthetic media, and marking AI-generated content in machine-readable formats. The fact that your GPAI model vendor is compliant with Articles 53 and 55 does not cover your Article 50 obligations. Those belong to you.
There is a third category worth knowing: downstream providers. If your business fine-tunes an open-source GPAI model and redistributes the result commercially, you cross from deployer into provider territory. At that point, both sets of obligations apply. The threshold, as noted above, is whether the modification is substantial enough to constitute a significant change to the model.
An organisation using a GPAI tool purely for internal tasks, without placing a product or model on the market, is generally not a GPAI model provider. The provider route becomes relevant when you develop, commission, substantially modify, or make available a model to others.
Which GPAI models are currently considered systemic-risk models?
A GPAI model is presumed to carry systemic risk when its cumulative training compute exceeds 10²⁵ FLOPs, the threshold set in Article 51(2) of the EU AI Act. Based on publicly available compute estimates, models that fall into this category include GPT-4-class models from OpenAI, Gemini Ultra from Google, Claude 3 Opus and later versions from Anthropic, and Meta’s Llama 3 405B. It is worth noting that the EU AI Office has not published a formal public registry of officially designated systemic-risk models; the classifications above are based on compute estimates, not Commission designations.
Compute is not the only route to systemic-risk classification. Article 51(1) states a model also qualifies if it has “high-impact capabilities” matching or exceeding the most advanced general-purpose AI models, or if the Commission designates it based on other technical criteria. The Commission can update these thresholds as technology evolves.
Providers must notify the AI Office within two weeks of reasonably foreseeing or reaching the 10²⁵ FLOPs threshold. They must also maintain records of their compute estimation methodology, whether hardware-based (GPU tracking) or architecture-based, within a 30% accuracy margin.
For most businesses, the practical relevance of Article 55 is structural rather than direct. Training a model above the systemic-risk threshold is beyond the reach of all but a handful of organisations. What matters for everyone else is that the foundation models they build products on are provided by exactly the companies Article 55 binds. Major signatories of the voluntary GPAI Code of Practice, including OpenAI, Anthropic, Google, and Mistral, have committed to meeting those obligations.
How do GPAI regulations affect SEO and AI-generated content?
GPAI regulations affect SEO and AI-generated content primarily through Article 50 of the EU AI Act, which sets out transparency obligations for providers and deployers of generative AI systems. These obligations activated on August 2, 2026, and apply to any business whose AI-generated content or AI systems reach EU users, regardless of where that business is headquartered.
Article 50 imposes four specific duties. Providers of AI systems that interact directly with people must design them so users know they are dealing with AI. Providers of generative AI systems must mark outputs in a machine-readable format. Deployers using emotion recognition or biometric categorisation tools must inform individuals. Deployers of deepfakes must disclose that the content is AI-generated or AI-manipulated.
The labelling obligation for AI-generated text is more targeted than it first appears. It covers text published to inform the public on matters of public interest. Ordinary marketing content that goes through human review, with a person holding editorial responsibility, is generally exempt. The Article 50 analysis from Axipro confirms that supportive AI use, such as translation or writing assistance, also does not require labelling.
For SEO practitioners, the regulation is reshaping how AI-generated content is treated across traditional search and generative engines. The EU AI Act is pushing platforms toward greater transparency about how AI influences search results, and this affects GEO (Generative Engine Optimization) strategy as much as conventional rankings. Businesses that use AI to scale content output, as many now do, need a clear process for human review and editorial accountability. That process is both a compliance requirement and a content quality standard.
If your business relies on AI-assisted content creation at scale, the EU AI Act transparency obligations timeline published by Stibbe in July 2026 is worth reviewing. The Commission’s final guidelines on Article 50 were adopted on July 20, 2026, and the AI Board assessed the Code of Practice on Marking and Labelling of AI-Generated Content as adequate on July 8-9, 2026, making adherence the recognised route to demonstrating compliance.
When do GPAI compliance deadlines actually apply?
The GPAI compliance timeline has four key dates. GPAI provider obligations under Articles 53 and 55 became legally effective on August 2, 2025. The AI Office’s formal enforcement powers, including fines, model evaluations, and documentation requests, activated on August 2, 2026. Models already on the market before August 2, 2025, have until August 2, 2027, to achieve full compliance. Article 50 transparency obligations for deployers of AI-generated content also activated on August 2, 2026.
New GPAI models placed on the market after August 2, 2025 must comply from the outset. There is no grace period for models launched after that date. For AI systems already on the market before August 2, 2026, providers have until December 2, 2026, to meet the machine-readable marking requirement under Article 50(2).
The maximum fine for GPAI model providers under Article 101 is up to 3% of global annual turnover or €15 million, whichever is higher. This is distinct from the higher fine ceiling that applies to prohibited AI practices. As of the research date for this article, no formal enforcement actions or fines have been publicly reported under the GPAI provisions, but the AI Office now has the authority to act.
One important clarification: the Digital Omnibus agreed in May 2026 delayed certain high-risk AI system deadlines, but explicitly left the GPAI obligations and Article 50 transparency deadlines untouched. If you have been tracking AI Act timelines and noticed some dates shift, the GPAI deadlines were not among them.
For businesses using AI to produce content at scale, the practical step is to audit your current workflows now. Identify which tools you use, whether you are a deployer or a downstream provider, and whether your content review process establishes the editorial responsibility that exempts ordinary marketing content from labelling requirements. If you want to understand how AI-generated content fits into a compliant, high-performing SEO strategy, scaling content output with proper human oversight is the approach that satisfies both regulators and search engines.
This content was generated with the help of AI and it may contain mistakes
