📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA, a €5.5 million European Portuguese LLM, is operational and surpasses many benchmarks. However, key questions about its openness, native data, and goals remain unanswered, revealing broader issues in European sovereign-LLM efforts.
Portugal’s €5.5 million AI project, AMÁLIA, is now operational, with a base model launched in September 2025 and its final version due in June 2026. While it outperforms many benchmarks, experts are raising critical questions about its openness, native-language data sufficiency, and strategic goals, which have broader implications for European sovereignty in AI development.
AMÁLIA is a consortium project involving approximately 60 researchers from Portugal’s top research institutions, including NOVA, IST, and IT. It was announced in December 2024 and is based on a continuation of the pre-training phase of the EuroLLM model, rather than training from scratch, contrasting with Italy’s Minerva approach.
The model handles Portuguese text and is designed to eventually incorporate multimodal capabilities. Its current version, released in October 2025, is accessible to 450,000 academic users through the FCT’s IAedu platform and contains knowledge up to the end of 2023.
Technical analysis shows AMÁLIA outperforms previous open models on Portuguese benchmarks and surpasses Qwen 3-8B on most tests, though it still trails on some specific tasks like ALBA. The training involved 107 billion tokens, with approximately 5.8 billion from Portugal’s web archive, Arquivo.pt, representing 5.5% of extended pre-training. The supervised fine-tuning phase used about 17-18% Portuguese data.
Researcher Duarte O.Carmo has publicly critiqued the project, emphasizing three critical questions: How open is ‘fully open’ really? How much native-language data is enough? What should be the primary goals of such models? These questions reveal broader structural issues in European sovereign-LLM efforts, which are often analyzed individually rather than as part of a collective pattern.
AMÁLIA
The three hard
questions.
Portugal spent €5.5M to build a European Portuguese LLM. The base version is operational, the benchmarks beat Qwen 3-8B on most pt-PT tasks. So why are the most important questions still unanswered?
Last month, Duarte O.Carmo published the sharpest public analysis of AMÁLIA — Portugal’s state-funded European Portuguese large language model. He prefaces his critique with the necessary diplomatic apparatus before doing what almost nobody else in the European-sovereign-LLM discourse has been willing to do publicly: asking hard questions about whether the work, as released, actually does what it set out to do. This piece is a structural extension of his analysis. The AMÁLIA case study exposes three hard questions every national LLM effort needs to answer publicly — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
Three questions every national LLM effort needs to answer publicly.
Duarte O.Carmo’s framing maps cleanly onto the structural argument. Each question lands specifically in AMÁLIA — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
The three questions form a structural feedback loop. Q3 (optimization target) determines Q2 (data volume needed) which conditions Q1 (openness sufficient for community contribution). The European sovereign-LLM movement collectively benefits from these questions becoming standard methodology disclosure, not exceptional critique.
Portuguese Flash Cards – Learn Portuguese Language Vocabulary Words and Phrases – Basic Language for Beginners – Gift for Travelers, Kids, and Adults by Travelflips
PORTUGUESE FLASH CARDS – Basic Portuguese words and phrases for beginners and travelers
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
107 billion tokens. 5.8 billion clearly pt-PT.
The structurally tractable question with a structurally surprising answer. For a model whose entire stated purpose is European Portuguese prioritization, the native-language share of extended pre-training is 5.5%. The implications cascade into every other question.
AI Engineering: Building Applications with Foundation Models
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
The Olmo standard. AMÁLIA’s current state.
Allen Institute for AI’s Olmo project defines what “fully open” operationally requires. Olmo doesn’t lead frontier benchmarks. That’s not the point. The point is to be the structural reference for openness. AMÁLIA’s “fully open source” claim should track to the operational standard.
Press Type Model Separator, ABS Model Assembly & Disassembly Tool, Ergonomic Hobby Assembling Model Tool
【Efficient Model Separation】Effortlessly separate model components with this press type model separator improving efficiency and reducing damage risk
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
Four strategic positions. AMÁLIA between two and three.
Approximately €100M+ in publicly disclosed European sovereign-LLM funding across the major initiatives. The structural question every project faces: what is the actual competitive position you’re staking? Four options — none mutually exclusive — but each requiring different commitments.
European sovereign AI research resources
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
Three standards. For AMÁLIA and the movement.
The structural critique generalizes beyond AMÁLIA. Italy, France, Germany, Switzerland, the OpenEuroLLM consortium, and every subsequent national project benefit from public discourse holding national LLM efforts to operational standards on openness, data accounting, and strategic positioning.
The European sovereign-AI agenda is a serious strategic project that deserves serious public discourse. O.Carmo’s analysis is what serious public discourse looks like. Appropriately diplomatic. Structurally rigorous. Willing to ask the hard questions in public when the public investment justifies it. More of this is needed — across every European sovereign-LLM project, not just AMÁLIA.
Implications of AMÁLIA for European Sovereign-LLMs
The development of AMÁLIA underscores a broader challenge for European AI sovereignty: balancing openness, native-language data, and strategic goals. The project’s progress and the questions it raises highlight systemic issues that could influence future policy and research directions across Europe. Addressing these questions is vital for ensuring that national models serve their intended purposes without unintended limitations or strategic gaps.
European Sovereign-LLM Initiatives and Structural Questions
Across Europe, multiple countries and consortia—such as Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and others—are developing national large language models. These efforts are often driven by a desire for AI sovereignty, but they face common structural questions about openness, native data sufficiency, and strategic objectives. The public discourse tends to focus on individual model capabilities rather than these underlying systemic issues, which are crucial for long-term success and collaboration.
Portugal’s AMÁLIA is a key case because it involves significant public investment and national accountability, making the questions about its openness and data more pressing at a policy level. The ongoing debate reflects broader uncertainties about how these models will be governed and integrated into national AI strategies.
“The three questions—openness, native data, and objectives—are the structural pillars that every European sovereign-LLM effort must address openly and honestly.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Openness and Strategy
It remains unclear how open AMÁLIA truly is, especially regarding access to training data, model weights, and fine-tuning procedures. The extent to which native Portuguese data suffices for long-term performance and strategic goals is also still under discussion. The final version’s capabilities and strategic positioning are still evolving, and some gaps in transparency and data are expected to be addressed before June 2026.
Upcoming Milestones and Policy Discussions
The final version of AMÁLIA is scheduled for release in June 2026, which will likely clarify some of the current uncertainties about its capabilities and openness. Meanwhile, European policymakers and researchers are expected to intensify discussions around the three core questions—openness, native data, and model objectives—shaping the future of sovereign AI initiatives across the continent. Public and private stakeholders will monitor these developments closely to assess strategic alignment and transparency.
Key Questions
What are the main challenges facing AMÁLIA’s development?
The main challenges include ensuring openness and transparency of the model, determining whether the native Portuguese data used is sufficient for long-term performance, and defining clear strategic objectives aligned with Portugal’s AI sovereignty goals.
How does AMÁLIA compare to other European models?
AMÁLIA outperforms many open models on Portuguese benchmarks and surpasses Qwen 3-8B on most tasks, but it still trails on some specific benchmarks like ALBA. Its approach of building on a multilingual foundation contrasts with Italy’s from-scratch training, highlighting different strategic choices.
Why are the questions about openness and native data so important?
These questions determine how transparent, adaptable, and strategically aligned the models are. Openness affects collaboration and trust, while native data sufficiency impacts the model’s performance and relevance for national needs.
What are the broader implications of these issues for Europe?
Addressing these questions is critical for Europe’s goal of AI sovereignty. Systematic transparency and clear strategic objectives will influence policy, research, and international competitiveness in AI development.
Source: ThorstenMeyerAI.com
