We built QIMMA قمّة (Arabic for "summit"), to answer that question systematically. Instead of aggregating existing Arabic benchmarks as-is and running models on them, we applied a rigorous quality validation pipeline before any evaluation took place. What we found was sobering: even widely-used, well-regarded Arabic benchmarks contain systematic quality issues that can quietly corrupt evaluation results.

This post walks through what QIMMA is, how we built it, what problems we found, and what the model rankings look like once you clean things up.

Arabic is spoken by over 400 million people across diverse dialects and cultural contexts, yet the Arabic NLP evaluation landscape remains fragmented. A few key pain points have motivated this work:

Translation issues. Many Arabic benchmarks are translations from English. This introduces distributional shifts. Questions that feel natural in English become awkward or culturally misaligned in Arabic, making benchmark data less representative of how Arabic is naturally used.