📊 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 now operational but faces three fundamental questions about its openness, native-language data, and objectives. These issues highlight broader challenges in Europe’s sovereign AI efforts.
Portugal’s €5.5 million AMÁLIA large language model (LLM) is now operational, with the base version released in September 2025, but critical structural questions about its openness, native-language data, and strategic goals remain largely unanswered, affecting the country’s AI policy and European efforts.
AMÁLIA, a consortium involving approximately 60 researchers from Portugal’s top academic institutions, was announced in December 2024 and is now accessible to 450,000 academic users through the FCT’s IAedu platform. It is built as a continuation of the EuroLLM multilingual model, rather than from scratch, with a focus on Portuguese language tasks. Benchmarks show AMÁLIA outperforms previous open models on Portuguese-specific tests and beats Qwen 3-8B on most benchmarks, though it still lags on some key tasks like ALBA.
Despite its technical achievements, public analysis by Duarte O.Carmo and others questions the model’s openness, the adequacy of native Portuguese data used, and the strategic objectives guiding its development. These questions are part of a broader pattern seen across European sovereign-LLM projects, which often lack transparent answers to these fundamental issues.
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.
European sovereign AI development kit
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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 AI Strategy and European Sovereign Models
The unresolved questions about AMÁLIA’s openness, native data, and purpose highlight broader challenges faced by European countries in developing sovereign AI models. These issues influence national AI policies, international competitiveness, and the future of European technological independence. The way Portugal addresses these questions could set a precedent for other nations pursuing similar projects.
European Sovereign LLM Efforts and Portugal’s Investment
Since 2024, several European countries and consortia—including Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and others—have launched or announced large language models with national or regional backing. These efforts aim to foster European independence from US and Chinese AI giants but face common structural questions about openness, native-language data, and strategic goals. Portugal’s AMÁLIA stands out as a publicly funded, high-profile case that exemplifies these issues, especially given its transparent investment and broad academic involvement.
“The three questions are not accusations but the structural framework that any honest evaluation of national LLM investment needs to internalize.”
— Duarte O.Carmo
Unresolved Questions About Openness, Data, and Goals
It is not yet clear how open AMÁLIA truly is, especially regarding access to its underlying code and training data. The adequacy of native Portuguese data remains debated, with only a small portion of training tokens sourced from Portuguese web archives. The strategic objectives—whether the model is meant for broad deployment, research, or policy—are still ambiguous, and final assessments will depend on further disclosures before June 2026.
Next Steps for AMÁLIA and European Sovereign AI Projects
Over the coming months, the AMÁLIA team is expected to release a final version by June 2026, which may address some of the current gaps. Public and academic scrutiny will likely intensify, with calls for transparency about training data, licensing, and strategic intent. The broader European AI community will observe whether these questions are resolved or remain open, influencing future investments and policy decisions.
Key Questions
What are the main concerns about AMÁLIA’s openness?
Experts question whether the model’s code, training data, and underlying architecture will be fully accessible, which is vital for transparency and independent evaluation.
How much native Portuguese data was used in training?
Approximately 5.8 billion tokens from Portuguese sources, mainly from the national web archive Arquivo.pt, represent about 5.5% of the total pre-training tokens, raising questions about data sufficiency.
What are the strategic goals behind AMÁLIA?
It remains unclear whether AMÁLIA is designed primarily for research, national policy, commercial deployment, or a combination, as official statements have not clarified its long-term purpose.
Will the final version address current gaps?
It is expected that the June 2026 release will clarify some issues, but whether it will fully resolve questions about openness and strategic direction is still uncertain.
Why does this matter beyond Portugal?
AMÁLIA exemplifies the challenges faced by European nations in building sovereign AI, influencing regional policies, and shaping Europe’s role in global AI development.
Source: ThorstenMeyerAI.com
