QIMMA validates benchmarks before evaluating models, ensuring reported scores reflect genuine Arabic language capability in LLMs.

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

Coding 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

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

2.3%

PalmX

3,001

0.8%

MedAraBench

4,960

0.7%

FannOrFlop

6,984

0.6%

ArabCulture

3,482

0.2%

MedArabiQ

499

0.2%

GAT

13,986

~0.0%

3LM STEM

2,609

~0.0%

AraDiCE-Culture

180

0.0%

ArabLegalQA

0.0%

AraTrust

522

0.0%

Taxonomy of Issues Found

💻 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

88%

3LM MBPP+

378

308

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

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

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

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

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

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

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}, 
}

QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard

🔍 The Problem: Arabic NLP Evaluation Is Fragmented and Unvalidated

⛰ What's in QIMMA?

🔬 The Quality Validation Pipeline

Stage 1: Multi-Model Automated Assessment

Stage 2: Human Annotation and Review

⚠️ What We Found: Systematic Quality Problems

By the Numbers

Taxonomy of Issues Found

💻 Code Benchmark: A Different Kind of Quality Work

⚙️ Evaluation Setup

Evaluation Framework

Metrics by Task Type

Prompt Templates

🏆 Leaderboard Results

The Size-Performance Relationship

🌟 What Makes QIMMA Different

🔗 Resources

🔖 Citation