Arabic.AI Partners with Stanford to Launch Arabic AI Benchmark Platform

Mohammed Fathy

Last updated 28-01-2026

3 min

Arabic.

AI and Stanford join forces on a “long-overdue” benchmark for Arabic language models.

The project extends Stanford’s HELM framework, long seen as a “gold standard”.

It gives startups a shared yardstick to judge Arabic models, rather than guessing quality.

An Arabic leaderboard and new conversational tests offer a clear performance baseline.

The move signals Arabic AI is “on equal footing”, even if challenges still remain.

Arabic.AI has teamed up with Stanford University’s Center for Research on Foundation Models on what feels like a long-overdue move: building the first truly holistic benchmark to assess Arabic large language models. In simple terms, it means Arabic AI systems will now be judged with the same level of care and consistency as models built for English or other major languages. For many founders and developers across the region, that’s not just academic housekeeping, it’s a big deal.

The work leans on Stanford’s HELM framework, which has become a kind of gold standard for comparing how language models perform and where they fall short. By extending HELM to cover Arabic, the partners are offering researchers and companies a shared reference point, something that has been missing for years. And believe it or not, the lack of solid benchmarks has been a real headache for startups trying to decide which models are actually up to scratch.

Arabic.AI, known for its Arabic-first LLM-X flagship model and the smaller LLM‑S, frames the collaboration as part of a broader push to strengthen the ecosystem, rather than just polish its own credentials. Nour Al Hassan, the company’s CEO, pointed out that although more than 400 million people speak Arabic, the language has often been sidelined when it comes to rigorous AI evaluation. According to him, working with Stanford’s CRFM helps put Arabic on equal footing, with transparency and visibility baked in. That said, I reckon this kind of partnership also sends a quiet signal to enterprises: Arabic AI is maturing fast.

The first phase is already wrapped up. It includes an Arabic leaderboard built on HELM and new ways to test conversational AI in Arabic, offering what the teams describe as a reliable baseline for understanding performance. On the flip side, benchmarks alone won’t suddenly fix issues like dialect diversity, but they do give everyone a common starting line, which is spot on.

From an Arageek point of view, this hits close to home. I’ve sat in more than one startup meetup where founders moaned that comparing Arabic models was a bit of a faff, with no neutral yardstick to rely on. So seeing a globally recognised research centre step into this space is something to be chuffed to bits about, even if it’s just one step in a much longer journey. The details of what HELM Arabic measures are now publicly available on Stanford’s site, and, well, it definately feels like a foundation others in the region can start building on.

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