Abu Dhabi has succeeded in building an artificial intelligence system that understands Arabic better than any other model according to global standards. It achieves this while being smaller and faster than competing systems developed by major tech companies.

The Falcon-H1 Arabic model, developed by the Technology Innovation Institute (TII), ranks first on the leaderboard of open Arabic large language models (Open Arabic LLM Leaderboard), which measures the effectiveness of AI systems in handling the Arabic language. This leading model, which contains 34 billion parameters, surpasses Meta's Llama-70B model and China's Qwen-72B model, despite being less than half their size.

For Arabic speakers, the impact is tangible and practical. Anyone who has tried popular AI tools in Arabic is aware of the gap: responses that may seem grammatically correct but lack meaning, tools that fail to understand dialects, or translations that ignore cultural context. The Falcon-H1 Arabic model has been specifically built to address these issues.

Arabic remains one of the most challenging languages for AI to model; the function of words changes based on subtle shifts, word order is flexible, and daily life involves a mix of dialects and modern standard Arabic. Global systems that are primarily trained on English often struggle with these challenges.
Research published in the journal "Communications of the ACM" indicates that the Arabic language lacks large, high-quality, annotated datasets—especially for dialects and informal speech—making most AI systems inadequately trained for real-world use. This shortfall is reflected daily in education, customer service, government services, and healthcare chatbots that operate in Arabic worse than they do in English.

The Falcon-H1 Arabic model has been trained on "Arabic-first" datasets that encompass formal language, regional dialects, and culturally rooted content. The model comes in three sizes: 3 billion, 7 billion, and 34 billion parameters, allowing organizations to choose based on their computational resources.

The smallest model (3B) outperforms Microsoft’s Phi-4 Mini model by 10 percentage points in Arabic language tests. The 7B version leads its category, while the largest model (34B) surpasses systems more than double its size, achieving a precision rate of 75.36% in comprehensive understanding tests of the Arabic language.

Beyond performance scores, the model addresses tasks relevant to everyday life: understanding colloquial phrases, reasoning in Arabic, maintaining long conversations, and interpreting context rather than translating word-for-word. It can process up to 192,000 words in a single conversation, sufficient for analyzing legal contracts, academic research, or complete medical records without losing the thread of context.

Faisal Al-Banai, advisor to the UAE President and Secretary-General of the Advanced Technology Research Council, stated that this achievement enables Arabic-speaking communities to benefit from “available, relevant, and impactful innovation.”

Over 450 million people speak Arabic in more than 20 countries. However, the language has historically been secondary in the development of global AI. Many major systems "support" Arabic only as an addition to models trained in English. In contrast, Falcon-H1 Arabic was designed from the outset with Arabic at the heart of the development process.

The positive implications of this achievement extend across multiple sectors in the UAE and beyond; schools can deploy virtual teachers that genuinely understand the students' language and dialect. Healthcare providers can utilize AI tools that respect cultural context. Additionally, companies can automate customer support without losing nuanced cultural differences, and government services can operate chat systems in natural Arabic instead of translated English formulations.