📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s €5.5M AMÁLIA LLM is now operational, outperforming many models on Portuguese benchmarks. However, critical questions about its openness, native data sufficiency, and goals remain unresolved, affecting broader European sovereign AI efforts.
Portugal’s €5.5 million investment in the AMÁLIA large language model has resulted in a functioning base version, now accessible to hundreds of academic users, but critical questions about its design and strategic purpose remain unanswered.
AMÁLIA is a consortium project involving approximately 60 researchers from Portugal’s leading institutions, including NOVA, IST, and IT. It is a continuation of the EuroLLM multilingual model, with the current version handling only text and knowledge up to the end of 2023. The model was completed in September 2025, with a final release planned for June 2026.
The technical approach involves building on an existing multilingual foundation rather than training from scratch, contrasting with Italy’s Minerva, which was trained from the ground up on Italian and English data. The training pipeline included 107 billion tokens, with around 5.8 billion from Portuguese sources, primarily the national web archive Arquivo.pt. The model outperforms previous open models on Portuguese benchmarks and beats Qwen 3-8B on most Portuguese tasks, though it still trails on some specific benchmarks like ALBA.
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.
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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.
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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.
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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.
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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 for Portugal’s National AI Strategy
The development of AMÁLIA marks a significant step for Portugal in establishing a sovereign AI infrastructure, demonstrating the country’s capacity to produce competitive language models. However, the unanswered questions about the model’s openness, native data sufficiency, and strategic focus could influence future policy, funding, and international collaboration. These issues are not just technical but also political, affecting how Portugal and broader Europe position their AI efforts amid global competition.
European Sovereign LLM Efforts and Structural Challenges
Across Europe, several countries and initiatives—such as Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and the OpenEuroLLM consortium—are pursuing sovereign-language large language models. These projects are operating under similar structural questions about how open their models truly are, how much native-language data is enough, and what their primary optimization goals should be. The public discourse has often focused on individual model launches, but experts argue the real issue lies in the broader structural patterns that define these efforts.
Portugal’s AMÁLIA is a key case because of its public funding and national scope, making its strategic choices particularly impactful for European AI sovereignty. The ongoing debate centers on whether models like AMÁLIA are truly open, whether their native data is sufficient, and what objectives they should prioritize—whether general performance, cultural representation, or strategic autonomy.
„AMÁLIA is an impressive piece of work. But the hard questions about its openness, native data, and goals remain.“
— Duarte O.Carmo
Unanswered Questions About Model Openness and Strategy
While the technical development of AMÁLIA is progressing, several key issues remain unresolved: the true extent of its openness to external scrutiny and collaboration, whether the native Portuguese data used is sufficient for long-term performance, and what the primary objectives—cultural preservation, general utility, or strategic autonomy—should be. These questions are still under discussion within the community and are not yet settled.
Upcoming Milestones and Policy Discussions
The final version of AMÁLIA is scheduled for release in June 2026, which will likely include further evaluations and strategic clarifications. Over the next 12-24 months, European policymakers and researchers are expected to debate the structural questions further, potentially influencing funding priorities, openness policies, and collaboration frameworks across the continent. Portugal’s next steps will also include assessing the model’s real-world utility and integration into national AI initiatives.
Key Questions
What are the main technical features of AMÁLIA?
AMÁLIA is built on a continuation of the EuroLLM multilingual foundation, trained on approximately 107 billion tokens, with a focus on Portuguese data from Arquivo.pt. It outperforms previous open models on Portuguese benchmarks and is set to be finalized in June 2026.
Why are the questions about openness and native data important?
These questions determine whether the model can be trusted for transparency, collaboration, and long-term relevance. Openness affects community involvement, while native data sufficiency impacts performance and cultural representation.
How does AMÁLIA compare to other European models?
Compared to models like Italy’s Minerva, AMÁLIA builds on an existing multilingual foundation rather than training from scratch, which influences its technical capabilities and strategic positioning.
What are the broader implications for European AI sovereignty?
The development and deployment of models like AMÁLIA reflect Europe’s efforts to build independent AI capabilities, but unresolved structural questions could limit their effectiveness and global competitiveness.
Source: ThorstenMeyerAI.com
