📊 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 operational but raises key questions about openness, native data sufficiency, and optimization goals. These questions impact national AI strategies and European sovereignty efforts.
Portugal’s €5.5 million AMÁLIA large language model is now operational and publicly accessible, but fundamental questions about its openness, native-language data, and primary objectives remain unanswered, raising concerns about the broader European sovereign AI landscape.
AMÁLIA was developed through a consortium involving approximately 60 researchers from Portugal’s leading research institutions, including NOVA, IST, and IT. The project was announced in December 2024, with the base version completed by September 30, 2025, and launched publicly on October 1, 2025. It is designed as a continuation of the EuroLLM multilingual model, not trained from scratch, with a focus on Portuguese language tasks. The model is currently available to 450,000 academic users across Portugal’s higher education system, with knowledge limited to the end of 2023. The final version is scheduled for release in June 2026.
Technical details reveal that AMÁLIA’s training involved 107 billion tokens, with only about 5.8 billion from Portuguese sources, representing roughly 5.5% of the total. Supervised fine-tuning increased the Portuguese share to approximately 17-18%. Benchmarks show that AMÁLIA surpasses previous open models on European Portuguese tasks and outperforms Qwen 3-8B on most benchmarks, though it still trails Qwen on certain specific tests like ALBA, the team’s primary benchmark.
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.
Official Jetson AGX Orin 64GB Developer Kit 275 Tops, with 1TB SSD AI Embodied Intelligence Development Provides AI Large Models Deploying Openclaw
AGX Orin 64GB Development Kit makes it easy to get started with AGX Orin. Its compact size, rich…
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
Fine-Tuning Large Language Models: From Custom Datasets to High-Performance AI Models Using Modern Toolchains
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
Portuguese for Beginners: Practical Learning with SynapseLingo (Learn Portuguese)
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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.
Beyond the Public Cloud: Architecting Private, Secure, and Sovereign AI for the European Enterprise
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
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 AI Sovereignty and Policy
This development highlights the structural challenges faced by European countries in building open, native-language AI models. The questions surrounding AMÁLIA’s openness, native data sufficiency, and strategic goals reflect broader issues across Europe’s sovereign-LLM efforts. Addressing these questions is crucial for ensuring transparency, competitiveness, and independence in AI technology, especially as governments invest heavily in national models amid geopolitical and technological competition.European Sovereign LLM Initiatives Face Common Challenges
Across Europe, multiple countries and initiatives—such as Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and the OpenEuroLLM consortium—are developing national language models. Despite differing technical approaches, they share common questions about how open their models truly are, how much native-language data is enough, and what their primary optimization goals should be. Portugal’s AMÁLIA exemplifies this pattern, with a significant public investment and a focus on Portuguese language tasks, but also reveals the broader structural issues that remain unresolved across the continent.“The three questions—openness, native data sufficiency, and strategic goals—are central to understanding the true nature of Europe’s sovereign AI efforts.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Openness and Goals
It remains unclear how open AMÁLIA will be in its final form, especially regarding access, licensing, and transparency. Additionally, the strategic priorities—whether the model aims for broad accessibility, commercial deployment, or government use—are still under discussion. The extent to which native-language data volume and quality will be increased before final release is also uncertain, as is how these factors will influence the model’s future capabilities and positioning in Europe’s AI landscape.
Next Steps for AMÁLIA and European Sovereign Models
The final version of AMÁLIA is scheduled for release in June 2026, which will likely address some of the current gaps in native data and openness. Over the next 12-24 months, European initiatives are expected to refine their models, clarify their strategic goals, and potentially increase native-language data sources. Public and academic evaluations will continue to scrutinize these models, and policymakers are likely to debate the balance between openness, data sovereignty, and competitive advantage in AI development.
Key Questions
What are the main challenges facing AMÁLIA’s development?
The key challenges include increasing native Portuguese data, defining openness and access policies, and aligning the model’s strategic goals with national and European priorities.
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 it still trails Qwen on specific benchmarks like ALBA. Its approach of continuing from a multilingual foundation differs from models trained from scratch, affecting its strategic implications.
Why are questions about openness and native data so important?
They determine how accessible, transparent, and controllable the models are, impacting national sovereignty, ethical standards, and competitive positioning in AI technology.
What will happen after the final AMÁLIA release in June 2026?
Expect ongoing evaluations, potential increases in native-language data, policy debates on openness, and broader European efforts to define standards and strategies for sovereign AI models.
Source: ThorstenMeyerAI.com
