QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

Back to Articles QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard Community Article Published April 21, 2026 Leen AlQadi LeenAlQadi Follow tiiuae Ahmed Alzubaidi amztheory Follow tiiuae Mohammed Alyafeai Alyafeai Follow tiiuae Maitha Alhammadi MaithaAlhammadi Follow tiiuae Shaikha Alsuwaidi Shaikha710 Follow tiiuae Omar saif alkaabi Omar-Alkaabi Follow tiiuae Basma Boussaha basma-b Follow tiiuae Hakim Hacid HakimHacid Follow tiiuae QIMMA validates benchmarks before evaluating models, ensuring reported scores reflect genuine Arabic language capability in LLMs. 🏆 Leaderboard · 🔧 GitHub · 📄 Paper If you've been tracking Arabic LLM evaluation, you've probably noticed a growing tension: the number of benchmarks and leaderboards is expanding rapidly, but are we actually measuring what we think we're measuring? 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. 🔍 The Problem: Arabic NLP Evaluation Is Fragmented and Unvalidated 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. Absent quality validation. Even native Arabic benchmarks are often released without rigorous quality checks. Annotation inconsistencies, incorrect gold answers, encoding errors, and cultural bias in ground-truth labels have all been documented in established resources. Reproducibility gaps. Evaluation scripts and per-sample outputs are rarely released publicly, making it hard to audit results or build on prior work. Coverage fragmentation. Existing leaderboards cover isolated tasks and narrow domains, making holistic model assessment difficult. To illustrate where QIMMA sits relative to existing platforms: Leaderboard Open Source Native Arabic Quality Validation Code Eval Public Outputs OALL v1 ✅ Mixed ❌ ❌ ✅ OALL v2 ✅ Mostly ❌ ❌ ✅ BALSAM Partial 50% ❌ ❌ ❌ AraGen ✅ 100% ❌ ❌ ❌ SILMA ABL ✅ 100% ✅ ❌ ✅ ILMAAM Partial 100% ✅ ❌ ❌ HELM Arabic ✅ Mixed ❌ ❌ ✅ ⛰ QIMMA ✅ 99% ✅ ✅ ✅ QIMMA is the only platform combining all five properties: open source, predominantly native Arabic content, systematic quality validation, code evaluation, and public per-sample inference outputs. ⛰ What's in QIMMA? QIMMA consolidates 109 subsets from 14 source benchmarks into a unified evaluation suite of over 52,000 samples, spanning 7 domains: Domain Benchmarks Task Types Cultural AraDiCE-Culture, ArabCulture, PalmX MCQ STEM ArabicMMLU, GAT, 3LM STEM MCQ Legal ArabLegalQA, MizanQA MCQ, QA Medical MedArabiQ, MedAraBench MCQ, QA Safety AraTrust MCQ Poetry & Literature FannOrFlop QA Coding 3LM HumanEval+, 3LM MBPP+ Code A few things stand out about this design: 99% native Arabic content. The only exception is code evaluation, which is inherently language-agnostic. First Arabic leaderboard with code evaluation. QIMMA integrates Arabic-adapted versions of HumanEval+ and MBPP+, making it possible to assess coding capability with Arabic-language problem statements. Diversity in Domains and Tasks. QIMMA evaluates real-world competency areas including education, governance, healthcare, creative expression, and software development. 🔬 The Quality Validation Pipeline This is the methodological heart of QIMMA. Before running a single model, we applied a multi-stage validation pipeline to every sample in every benchmark. Stage 1: Multi-Model Automated Assessment Each sample was independently evaluated by two state-of-the-art LLMs: Qwen3-235B-A22B-Instruct DeepSeek-V3-671B We chose two models with strong Arabic capability but different training data compositions, so that their combined judgment is more robust than either alone. Each model scores a sample against a 10-point rubric, with binary scores (0 or 1) per criterion: A sample is eliminated if either model scores it below 7/10. Samples where both models agree on elimination are dropped immediately. However, where only one model flags a sample, it proceeds to human review in Stage 2. Stage 2: Human Annotation and Review Flagged samples are reviewed by native Arabic speakers with cultural and dialectal familiarity. Human annotators make final calls on: Cultural context and regional variation Dialectal nuance Subjective interpretation Subtle quality issues automated assessment may miss For culturally sensitive content, multiple perspectives are considered, since "correctness" can genuinely vary across Arab regions. ⚠️ What We Found: Systematic Quality Problems The pipeline revealed recurring quality issues across benchmarks; not isolated errors, but systematic patterns reflecting gaps in how benchmarks were originally constructed. By the Numbers Benchmark Total Samples Discarded Discard Rate ArabicMMLU 14,163 436 3.1% MizanQA 1,769 41 2.3% PalmX 3,001 25 0.8% MedAraBench 4,960 33 0.7% FannOrFlop 6,984 43 0.6% ArabCulture 3,482 7 0.2% MedArabiQ 499 1 0.2% GAT 13,986 1 ~0.0% 3LM STEM 2,609 1 ~0.0% AraDiCE-Culture 180 0 0.0% ArabLegalQA 79 0 0.0% AraTrust 522 0 0.0% Taxonomy of Issues Found ⚖️ Answer Quality False or mismatched gold indices, factually wrong answers, missing or raw text answers. 📄 Text & Formatting Quality Corrupt or illegible text, spelling and grammar errors, and duplicate samples. 💬 Cultural Sensitivity Stereotype reinforcement and monolithic generalizations about diverse communities. 🤝 Gold Answer Compliance Misalignment of gold answers with evaluation protocols. 💻 Code Benchmark: A Different Kind of Quality Work Code benchmarks required a different intervention. Rather than discarding samples, we refined the Arabic problem statements in 3LM's Arabic adaptations of HumanEval+ and MBPP+, leaving task identifiers, reference solutions, and test suites completely unchanged. The modification rates were striking: Benchmark Total Prompts Modified Unchanged Modification Rate 3LM HumanEval+ 164 145 19 88% 3LM MBPP+ 378 308 70 81% Modifications fell into five categories: Linguistic refinement : normalizing toward natural Modern Standard Arabic and consistent imperative style Clarity improvements : fixing ambiguous instructions and unclear constraints Consistency normalization : standardizing mathematical terminology, punctuation, and example formatting Structural corrections : fixing broken triple-quoted strings, indentation errors, corrupted text fragments Semantic refinements : clarifying whether ranges are inclusive/exclusive, preserving task intent ⚙️ Evaluation Setup Evaluation Framework QIMMA uses LightEval, EvalPlus and FannOrFlop as its evaluation framework, chosen for consistency, multilingual community adoption, and reproducibility. Metrics by Task Type Task Type Metric Benchmarks MCQ Normalized Log-Likelihood Accuracy AraDiCE-Culture, ArabicMMLU, ArabCulture, PalmX, 3LM STEM, MedArabiQ, GAT, MedAraBench, AraTrust Multi-select MCQ Probability Mass on Gold Choices MizanQA Generative QA F1 BERTScore (AraBERT v02) MedArabiQ, ArabLegalQA, FannOrFlop Code Pass@1 3LM HumanEval+, 3LM MBPP+ Prompt Templates QIMMA standardizes prompting by question format, with six template types: MCQ: generic multiple choice · MCQ-C: multiple choice with context passage · MCQ-I: multiple choice with specific instructions (GAT analogy/completion) · QA: generic open-ended QA · QA-C: QA with context · QA-F: fill-in-the-blank QA All prompts are in Arabic. For MizanQA and ArabCulture, benchmark-specific system prompts from the original papers are preserved. 🏆 Leaderboard Results Results as of April 2026; covering top 10 evaluated models. Visit the live leaderboard for current rankings. Rank Model AVERAGE AraDiCE-Culture ArabicMMLU ArabCulture PALMX 3LM STEM AraTrust MizanQA MedArabiQ ArabLegalQA GAT MedAraBench HumanEval+ MBPP+ FannOrFlop 🥇 1 Qwen/Qwen3.5-397B-A17B-FP8 68.06 82.78 77.54 61.75 83.91 88.67 90.04 73.36 47.30 54.94 55.89 47.97 67.68 76.72 44.33 🥈 2 Applied-Innovation-Center/Karnak 66.20 73.33 80.94 53.49 81.40 93.10 89.08 55.92 55.78 71.58 61.06 54.19 33.54 64.55 58.91 🥉 3 inceptionai/Jais-2-70B-Chat 65.81 78.89 81.29 83.24 83.73 87.96 90.23 71.78 52.79 69.60 51.67 50.89 19.51 43.65 56.13 #4 Qwen/Qwen2.5-72B-Instruct 65.75 77.22 73.78 63.83 77.77 87.55 88.51 63.49 50.06 70.74 55.90 44.19 37.20 72.75 57.51 #5 Applied-Innovation-Center/AIC-1 65.37 73.33 72.02 77.52 76.11 88.13 90.61 56.36 53.75 68.96 62.11 50.78 28.05 69.58 47.83 #6 Qwen/Qwen3.5-122B-A10B 64.84 74.44 73.17 37.78 81.46 86.18 86.97 64.01 47.04 55.11 50.90 52.49 65.24 72.43 60.54 #7 Sakalti/Ultiima-72B 64.49 78.33 72.28 68.79 76.75 83.70 89.08 60.44 44.58 69.12 46.91 42.25 39.02 74.07 57.56 #8 meta-llama/Llama-3.3-70B-Instruct 63.96 77.22 71.57 78.05 77.95 88.28 85.63 67.44 56.25 64.00 51.13 54.86 27.44 71.16 24.43 #9 Qwen/Qwen2.5-32B-Instruct 63.26 70.56 68.76 75.80 72.07 81.03 85.82 53.78 48.08 69.27 56.94 36.51 34.15 72.75 93.10 #10 FreedomIntelligence/AceGPT-v2-32B-Chat 61.14 76.67 70.62 79.79 74.46 84.88 86.97 63.89 49.96 71.46 56.04 47.32 23.78 54.50 15.56 Scale does not guarantee best performance. The top 10 spans models from 32B to 397B parameters, with several mid-size models outperforming larger ones on specific domains. Arabic-specialized models lead on cultural and linguistic tasks. Jais-2-70B-Chat ranks highest on ArabicMMLU and ArabCulture, while Karnak leads on 3LM STEM and ArabLegalQA. Coding remains the hardest domain for Arabic-specialized models. The top HumanEval+ and MBPP+ scores belong to multilingual models, with Qwen3.5-397B leading both. The Size-Performance Relationship Across the full leaderboard (46 models), a clear but imperfect size-performance correlation emerges. However, there are interesting exceptions: Arabic-specialized models often outperform size-matched multilingual models Instruction-tuned models consistently outperform their base counterparts except for Qwen3 Some smaller Arabic-specialized models (Fanar-1-9B, ALLaM-7B) outperform much larger multilingual models on specific domains 🌟 What Makes QIMMA Different To summarize the distinctive properties of QIMMA: Property Details Quality-first philosophy Validation runs before evaluation, not as an afterthought Multi-model validation Two LLMs with different training + human review for flagged cases 99% native Arabic Avoids translation artifacts almost entirely Multi-domain, multi-task 7 domains, 3 task types (MCQ, QA, code), 109 subsets Code evaluation First Arabic leaderboard to include code generation Full transparency Per-sample inference outputs publicly released, not just aggregate scores LightEval-based Unified, reproducible evaluation codebase Dialectal awareness Explicit handling of MSA vs. dialectal variation in prompts and rubrics 🔗 Resources 🏆 Leaderboard: QIMMA Leaderboard 💻 Code: GitHub 📄 Paper: Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation 🔖 Citation @misc{alqadi2026arabicbenchmarksreliableqimmas, title={Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation}, author={Leen AlQadi and Ahmed Alzubaidi and Mohammed Alyafeai and Hamza Alobeidli and Maitha Alhammadi and Shaikha Alsuwaidi and Omar Alkaabi and Basma El Amel Boussaha and Hakim Hacid}, year={2026}, eprint={2604.03395}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2604.03395}, } More from this author Falcon Perception tiiuae 62 April 1, 2026 Alyah ⭐️: Toward Robust Evaluation of Emirati Dialect Capabilities in Arabic LLMs tiiuae 24 January 27, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. Comment · Sign up or log in to comment Upvote 3