# QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

DevFeed: [QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard](<https://devfeed.tech/articles/qimma-a-quality-first-arabic-llm-leaderboard-7511.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/qimma-arabic-leaderboard>)

Author: Leen AlQadi; Ahmed Alzubaidi; Mohammed Alyafeai; Maitha Alhammadi; Shaikha Alsuwaidi; Omar saif alkaabi; Basma Boussaha; Hakim Hacid

Published: 2026-04-21T10:09:58Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [blog](<https://devfeed.tech/tags/blog.md>), [code](<https://devfeed.tech/tags/code.md>), [data](<https://devfeed.tech/tags/data.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [llm](<https://devfeed.tech/tags/llm.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>)

## AI overview

QIMMA is a quality-first Arabic LLM leaderboard that validates benchmark data before evaluating models. It addresses translation issues, annotation errors, encoding problems, cultural bias, reproducibility gaps, and fragmented task coverage. The platform combines native Arabic content, systematic validation, code evaluation, and public per-sample inference outputs across 109 subsets from 14 benchmarks and more than 52,000 samples.

## Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face