📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA, a €5.5M European Portuguese LLM, is now operational but faces three fundamental questions about its openness, native data, and goals. These issues have broader implications for Europe’s sovereign AI efforts.
Portugal’s €5.5 million AMÁLIA large language model is now operational, with the base version publicly available to academic users, but fundamental questions about its openness, native-language data, and strategic objectives remain unresolved.
Developed through a consortium of approximately 60 researchers across Portugal’s leading institutions, AMÁLIA is a continuation of the EuroLLM multilingual model, focusing on European Portuguese. The model was completed in September 2025 and is accessible via the FCT’s IAedu platform, primarily used by 450,000 academic users.
Technically, AMÁLIA is not trained from scratch but builds on a pre-existing multilingual foundation, with a training dataset that includes around 5.8 billion tokens from Portuguese sources—mainly from the national web archive Arquivo.pt. It outperforms previous open models on Portuguese benchmarks and beats Qwen 3-8B on most tests, though it still trails on some specific tasks like ALBA.
Despite these achievements, questions about the model’s openness, the sufficiency of native-language data, and its primary objectives have been raised publicly, notably by researcher Duarte O.Carmo, highlighting issues that extend beyond Portugal to broader European AI efforts.
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 European Sovereign AI Efforts
The questions surrounding AMÁLIA reflect larger challenges faced by European countries in developing autonomous, transparent, and strategically aligned AI models. How open these models truly are, how much native-language data is enough, and what they should optimize for are critical for shaping future policies and investments. Addressing these issues transparently influences not only Portugal’s AI trajectory but also sets a precedent for other national initiatives across Europe, impacting sovereignty, innovation, and ethical standards in AI development.
European Sovereign-Language Model Initiatives and Challenges
Across Europe, nations like Italy, Germany, France, and Norway are investing in their own large language models, often with public funding and strategic goals aligned with national interests. Many of these projects, including Portugal’s AMÁLIA, are operating under similar structural questions: the extent of openness, native-language data sufficiency, and primary optimization goals. Public discourse has often focused on individual model capabilities rather than the systemic patterns shaping these efforts, raising concerns about transparency and strategic coherence.
Portugal’s investment and the public release of AMÁLIA serve as a case study for these broader issues, with the model’s development reflecting both technical choices and strategic priorities that are still being debated within the European AI community.
“The core questions about openness, native data, and objectives are not just technical but strategic, shaping Europe’s AI sovereignty.”
— Duarte O.Carmo
Unresolved Questions About AMÁLIA’s Openness and Goals
It remains unclear how open AMÁLIA truly is, especially regarding access to training data and model weights. The final strategic objectives—whether the model aims for broad public deployment, commercial use, or research—are also still under discussion. Additionally, the sufficiency of native-language data and the model’s capacity to meet diverse linguistic and cultural needs are ongoing debates, with final answers expected only after the June 2026 release.
Next Steps for Portugal’s AMÁLIA Development and Evaluation
The final version of AMÁLIA is scheduled for release in June 2026, which will likely include updates addressing current gaps. Researchers and policymakers will scrutinize its openness, data transparency, and strategic focus. Broader European discussions are expected to intensify around establishing common standards for sovereign-language models, with Portugal’s experience serving as a key case study in shaping these policies.
Key Questions
What are the main concerns about AMÁLIA’s openness?
Questions center on how accessible the model’s weights and training data are to the public or researchers, which impacts transparency and reproducibility.
How much native Portuguese data was used in training AMÁLIA?
Approximately 5.8 billion tokens from Portuguese sources, mainly from Arquivo.pt, representing about 5.5% of the extended pre-training dataset.
What are the strategic objectives behind AMÁLIA?
The goals are still under discussion, but include supporting academic research, national AI sovereignty, and potentially commercial applications, with final clarity expected in the final release.
Why do these questions matter beyond Portugal?
They reflect broader European challenges in developing autonomous, transparent, and strategically aligned AI models, influencing policy and investment decisions across the continent.
Source: ThorstenMeyerAI.com
