📊 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 outperforming some benchmarks but faces unresolved questions about openness, native data, and objectives. These issues reflect broader challenges in Europe’s sovereign AI efforts.
Portugal’s €5.5 million AMÁLIA large language model is now operational, marking a significant milestone in the country’s AI ambitions, but key questions about its openness, native-language data, and strategic objectives remain unanswered.
AMÁLIA, developed through a consortium involving approximately 60 researchers from Portugal’s top institutions, was officially launched in October 2025. It is based on a continuation of the EuroLLM multilingual foundation, with the base version handling text only and planned multimodal capabilities. The model outperforms previous open models on European Portuguese benchmarks and surpasses Qwen 3-8B on most Portuguese tasks, though it still trails on certain benchmarks like ALBA.
The project was announced in December 2024, with the final version expected by June 2026. The training involved 107 billion tokens, with a small but significant portion from Portugal’s national web archive, Arquivo.pt. The model is currently accessible to 450,000 academic users via the FCT’s IAedu platform, with knowledge capped at the end of 2023.
Despite these achievements, public critique, notably from Duarte O.Carmo, highlights that fundamental questions about the model’s openness, adequacy of native data, and strategic goals remain largely unaddressed, raising concerns about the broader European sovereign-LLM landscape.
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 of AMÁLIA for Europe’s Sovereign AI Efforts
The development of AMÁLIA exemplifies Europe’s push for sovereign-language models, but the unresolved questions about openness, native data sufficiency, and strategic focus highlight systemic challenges. These issues are critical for policymakers and researchers as they shape Europe’s AI independence and competitiveness in the global landscape.
European Sovereign-Language Model Initiatives and Challenges
Across Europe, multiple countries and initiatives—such as Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and the OpenEuroLLM consortium—are pursuing sovereign-language models. These efforts are driven by strategic goals of independence, data sovereignty, and regional AI leadership. However, they face recurring questions about how open these models truly are, whether native-language data is sufficient, and what the primary objectives should be—be it performance, openness, or strategic autonomy.
Portugal’s AMÁLIA stands out as the first public, government-funded effort with a clear accountability framework, making these questions particularly salient in its case. The broader European landscape is still grappling with these foundational issues, which influence policy, funding, and research directions.
“The questions about openness, native data, and strategic goals are fundamental to understanding what these models can truly achieve and what they should aim for.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Openness and Strategy
It remains unclear how open AMÁLIA is in practice, especially regarding access and licensing. The sufficiency of native Portuguese data for long-term performance and adaptation is also debated, as is the strategic focus—whether to prioritize openness, performance, or sovereignty. The final version’s capabilities and the model’s alignment with Portugal’s strategic goals are still evolving, and detailed assessments are pending.
Upcoming Milestones and Critical Evaluations for AMÁLIA
The next 12 to 24 months will be crucial for clarifying AMÁLIA’s openness, native data adequacy, and strategic focus. The final version is scheduled for release in June 2026, after which comprehensive evaluations and comparisons with other European models will likely emerge. Policymakers and researchers will scrutinize whether AMÁLIA can meet its goals and how it influences Europe’s sovereign AI landscape.
Key Questions
What are the main concerns about AMÁLIA’s openness?
Critics question whether AMÁLIA is truly open in terms of access, licensing, and data sharing, which are vital for transparency and collaborative development.
How much native Portuguese data was used in training AMÁLIA?
Approximately 5.8 billion tokens from Portugal’s web archive were used during extended pre-training, representing about 5.5% of the total tokens, but whether this is sufficient for long-term performance remains debated.
What are the strategic goals of Portugal’s AMÁLIA project?
The project aims to develop a high-performing, publicly accountable Portuguese LLM that enhances national AI sovereignty, but specific priorities—such as openness versus performance—are still being defined.
When will the final version of AMÁLIA be available?
The final version is scheduled for release in June 2026, after which comprehensive evaluations are expected to shed light on its capabilities and strategic alignment.
Why are the three questions about openness, data, and goals important?
These questions are fundamental to understanding the true potential, limitations, and strategic value of national LLM efforts, influencing policy, funding, and international competitiveness.
Source: ThorstenMeyerAI.com
