📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal launched AMÁLIA, a €5.5 million European Portuguese language model, which performs well on benchmarks but prompts three hard questions about its openness, native data, and objectives. These questions highlight broader issues in European sovereign-LLM efforts.
Portugal’s €5.5 million investment in the AMÁLIA large language model has resulted in a functioning, benchmarked model for European Portuguese, but it raises three fundamental questions about openness, native-language data, and strategic objectives that remain unresolved.
AMÁLIA was developed through a consortium involving approximately 60 researchers across Portugal’s top research institutions, including NOVA, IST, and IT, and was announced in December 2024. The model, completed in September 2025, is currently accessible to 450,000 academic users via the FCT’s IAedu platform. It is based on a continuation of the EuroLLM model, not trained from scratch, with a focus on Portuguese language data, including 5.8 billion tokens from the Portuguese web archive Arquivo.pt. The model outperforms previous open models on Portuguese benchmarks and surpasses Qwen 3-8B on most tests, though it still trails on some specific benchmarks like ALBA.
Despite these achievements, public analysis by Duarte O.Carmo and others highlights three key questions: How open is ‘fully open’ in practice? How much native-language data is enough? And what should the model’s primary optimization goals be? These questions are not yet answered and are central to evaluating the project’s strategic value and effectiveness.
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.
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.
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.
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-Language Models
The questions raised by AMÁLIA reflect broader challenges faced by European countries developing their own LLMs, such as transparency, data sufficiency, and strategic focus. Addressing these issues is crucial for establishing credible, autonomous AI capabilities that serve national interests and reduce reliance on large US or Chinese models. The debate influences future investments and policy decisions across Europe.
European Sovereign-Language Model Initiatives and Challenges
European countries have launched multiple sovereign LLM projects, including Italy’s Minerva, Germany’s Aleph Alpha, and France’s Mistral, often with similar structural questions about openness, native data, and goals. These efforts are part of a broader movement to develop independent AI capabilities amid concerns over reliance on non-European models. The public discourse has largely focused on technical benchmarks, while strategic and structural questions remain underexplored.
“The three questions about openness, native data, and objectives are essential for understanding what these models can and should do.”
— Duarte O.Carmo
Unresolved Strategic and Technical Questions
It remains unclear how open ‘fully open’ truly is in practice, given proprietary constraints and data access issues. The adequacy of native-language data—whether 5.8 billion tokens from Arquivo.pt suffices for robust performance—is still debated. Additionally, the ultimate objectives for AMÁLIA—whether prioritizing benchmark performance, strategic autonomy, or real-world applicability—are not definitively set, and the final version’s development may address some of these gaps.
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. Meanwhile, ongoing discussions among policymakers, researchers, and industry stakeholders will shape strategic decisions for European sovereign AI initiatives. Transparency around data use, openness, and goal-setting will be key topics in the months ahead.
Key Questions
What are the main challenges facing AMÁLIA’s development?
The main challenges include defining the true level of openness, assessing whether the native Portuguese data is sufficient, and setting clear strategic goals for the model’s future development and deployment.
How does AMÁLIA compare to other European models?
AMÁLIA outperforms previous open models on Portuguese benchmarks and beats Qwen 3-8B on most tests, but still trails on some specific benchmarks like ALBA. Its approach of building on a multilingual foundation differs from models trained from scratch.
Why are these questions important for European AI sovereignty?
Addressing these questions ensures European models are transparent, strategically aligned, and capable of serving national interests without over-reliance on non-European AI giants.
What is the significance of the final version scheduled for June 2026?
The final release will provide clarity on the model’s capabilities, openness, and strategic focus, influencing future policy and investment decisions across Europe.
What broader lessons does AMÁLIA offer for other national AI initiatives?
AMÁLIA exemplifies the importance of clear strategic questions and transparency in developing sovereign LLMs, emphasizing that technical benchmarks alone are insufficient for meaningful evaluation.
Source: ThorstenMeyerAI.com
