Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.

Portugal’s €5.5 million AMÁLIA large language model, publicly launched in October 2025, has achieved notable technical benchmarks but faces critical, unresolved questions about its openness, native-language data, and primary objectives, according to recent analysis by Duarte O.Carmo.

AMÁLIA is a consortium project involving approximately 60 researchers from Portugal’s leading institutions, including NOVA, IST, and IT. The model, based on a continuation of the EuroLLM multilingual foundation, was completed in September 2025 and is currently available to 450,000 academic users through the FCT platform. It handles Portuguese text and is set to include multimodal capabilities in future versions.

The model’s training involved 107 billion tokens, with roughly 5.8 billion tokens from Portuguese web archives, representing about 5.5% of the total. The model outperforms previous open models on European Portuguese benchmarks and surpasses Qwen 3-8B on most Portuguese tasks, though it still trails Qwen on some specific benchmarks like ALBA.

Despite these achievements, Duarte O.Carmo’s analysis raises three core questions: How open is ‘fully open’ in practice? How much native-language data is enough? And what should be the primary goal of the model—general performance or native-language specialization? These questions are central to evaluating the strategic success and transparency of Portugal’s investment, and they reflect broader issues across European sovereign AI projects.

Implications for European Sovereign AI Strategies

The questions raised by AMÁLIA’s development highlight systemic issues in Europe’s approach to sovereign language models. They underscore the need for transparency about openness, clarity on native data sufficiency, and alignment of objectives with national policy goals. How these questions are addressed will influence future investments, model design choices, and Europe’s competitiveness in AI.

European Sovereign LLM Efforts Face Common Challenges

Across Europe, nations like Italy, Germany, France, and Norway are investing in native-language large language models, often with public funds and academic collaborations. These efforts are characterized by a shared structural pattern: models are frequently built on multilingual foundations rather than from scratch, and their openness and native data strategies remain opaque. The case of AMÁLIA exemplifies these broader trends, illustrating both technical progress and persistent strategic uncertainties.

“The three questions—how open is ‘fully open,’ how much native data is enough, and what should we optimize for—are critical to understanding the true value and transparency of AMÁLIA.”

— Duarte O.Carmo

Unresolved Strategic and Technical Questions

It remains unclear how open AMÁLIA truly is, given the lack of transparency about its training data and access policies. The sufficiency of native Portuguese data for achieving comprehensive language understanding is also debated. Additionally, the ultimate goal—whether to prioritize native-language performance or broader multilingual capabilities—has not been explicitly defined by the development team, leaving these questions open for future assessment.

Next Steps in AMÁLIA’s Development and Evaluation

The final version of AMÁLIA is expected in June 2026, which will likely address some of these questions through further testing, data transparency, and policy clarifications. The ongoing evaluation by independent researchers and policymakers will be crucial in determining whether the model’s technical progress translates into strategic value aligned with Portugal’s national AI goals. Broader European projects will also watch these developments closely.

Key Questions

What are the main technical achievements of AMÁLIA so far?

AMÁLIA outperforms previous open models on European Portuguese benchmarks and beats Qwen 3-8B on most Portuguese tasks, demonstrating strong performance in native-language understanding.

Why are the questions about openness and native data important?

They determine how transparent, accessible, and effective the model will be, influencing trust, policy decisions, and future AI development strategies at a national and European level.

What are the risks of not addressing these questions?

Failing to clarify openness and native data sufficiency could lead to less transparent models, reduced public trust, and strategies that do not fully serve national language needs or AI sovereignty goals.

Will the final version of AMÁLIA resolve these questions?

It is uncertain. The final version due in June 2026 may clarify some issues, but the structural questions about openness, native data, and goals are broader and may require ongoing policy and technical debate.

Source: ThorstenMeyerAI.com