Haakkim/Haakkim-1.0v

Haakkim — Arabic LLM Arena Battles v1.0

Haakkim (حَكِّم, "judge") is an open arena-style human preference evaluation platform for Arabic large language models. This dataset contains all battle records collected during the first release snapshot, including voted comparisons and skipped battles across 11 Arabic dialect varieties.

🌐 Platform: https://haakkim.tech 🏆 Live Leaderboard: https://haakkim.tech/#leaderboard

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Dataset Summary

Statistic	Value
Total battle records	1,273
— Voted comparisons	1,130
— Skipped (no vote cast)	143
Ranked-eligible battles (BT scoring)	831
Models evaluated	67 (ranked MSA)
Dialects covered	11
Evaluation modes	Ranked Arena, Side-by-Side, 10 Questions
Scoring method	Bradley–Terry with IPW & bootstrap CIs
Release date	April 2026

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Vote Outcome Distribution (Voted Battles)

Outcome	Count	Share
Model A wins	360	31.9%
Model B wins	364	32.2%
Tie	342	30.3%
Both Bad	64	5.7%
Total voted	1,130

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Dialect Coverage

Dialect	Battles	Share
Modern Standard Arabic (MSA)	986	77.5%
Tunisian	115	9.0%
Saudi	83	6.5%
Egyptian	45	3.5%
Levantine	22	1.7%
Sudanese	12	0.9%
Omani	5	0.4%
Iraqi	2	0.2%
Moroccan	1	<0.1%
Libyan	1	<0.1%
Algerian	1	<0.1%

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Evaluation Mode Breakdown

Mode	Battles	Used for Ranking
Ranked Arena (arena_random)	952	✅ Yes (BT scoring)
Side-by-Side (side_by_side)	292	❌ Win-rate only
10 Questions (ten_questions)	29	❌ Win-rate only

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MSA Official Leaderboard (Top 10)

Scores are Bradley–Terry scores centered at 1000. A 1-point gap corresponds to win odds of e¹ ≈ 2.7:1. The full 67-model leaderboard is available at haakkim.tech/leaderboard.

Rank	Model	BT Score	95% CI	Battles
1	mistralai/ministral-3b-2512	1001.75	[1001.20, 1002.93]	40
2	mistralai/ministral-8b-2512	1001.61	[1000.72, 1002.97]	43
3	Qwen/Qwen3-235B-A22B-Thinking-2507	1001.21	[1000.47, 1002.00]	38
4	Qwen/Qwen3-30B-A3B-Instruct-2507	1001.14	[999.96, 1002.83]	31
5	deepseek/deepseek-v3.2-exp	1001.13	[1000.27, 1002.16]	38
6	deepseek/deepseek-v3.1	1000.99	[999.81, 1002.07]	29
7	Qwen/Qwen3-235B-A22B-Instruct-2507	1000.98	[1000.12, 1002.08]	39
8	deepseek/deepseek-r1-0528	1000.93	[1000.10, 1002.14]	38
9	openai/gpt-oss-120b	1000.93	[1000.04, 1002.58]	25
10	deepseek/deepseek-v3.2	1000.89	[999.86, 1002.25]	31

Note on score scale: Haakkim uses unscaled log-odds units (centered at 1000), not Elo-style scaling. This produces a ~4-point spread across 67 models. Chatbot Arena-style scores (scaled by 400/ln10 ≈ 173.7) would show the same ranking with hundreds-of-points spreads. Both conventions encode identical win probabilities.

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Data Schema

Each row is a JSON object with the following fields:

{
  "id":                    "<UUID>",
  "model_a":               "<model name>",
  "model_b":               "<model name>",
  "winner":                "model_a" | "model_b" | "tie" | "both_bad" | "skipped",
  "evaluation_session_id": "<UUID>",
  "evaluation_order":      <int>,
  "conversation_a":        [{"role": "...", "content": "..."}, ...],
  "conversation_b":        [{"role": "...", "content": "..."}, ...],
  "full_conversation":     [{"role": "...", "content": "..."}, ...],
  "conv_metadata": {
    "source":              "arena_random" | "side_by_side" | "ten_questions",
    "dialect":             "msa" | "saudi" | "egyptian" | "levantine" | ...,
    "is_ranked_arena":     true | false,
    "sampling_weight":     <float>,
    "category_tag":        {"domain": "...", "tags": [...], "difficulty": "..."},
    "input_tokens_a":      <int>,
    "output_tokens_a":     <int>,
    "input_tokens_b":      <int>,
    "output_tokens_b":     <int>
  },
  "timestamp":             "<ISO 8601>"
}

Field Notes

• model_a / model_b: Reflect the exact presentation order the evaluator saw (A/B swap already applied). Model identities are not anonymised.
• winner: "skipped" means the user did not cast a vote. These rows are included for transparency but are excluded from BT scoring.
• conversation_a / conversation_b: Turn-by-turn transcripts (system prompt, user message, assistant response) for each model side.
• full_conversation: Interleaved transcript combining both sides as the evaluator saw them.
• conv_metadata.sampling_weight: Inverse-probability weight used for BT estimation (clamped to [P1, P99]).
• conv_metadata.category_tag: Automated domain/difficulty annotation (does not affect scoring).

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Preprocessing & Scoring Pipeline

Before BT scoring, all battles pass through a fixed preprocessing pipeline:

Step	Drop Reason	Dropped	Remaining
Raw joined battles	—	—	1,133
Structural validation	Invalid winner, missing model/prompt/response	37	1,096
Source filter	Non-rankable source (Side-by-Side, 10Q)	265	831
Exact deduplication	Duplicate battle IDs	0	831
Repeat cap	>1 vote per (session, prompt, model pair)	0	831
Failed comparisons	Empty or failed model generation	0	831
Ranked-eligible			831

Effective Sample Size (ESS): 465 (clamped) — indicates limited variance inflation despite exposure imbalance. Graph: 67 nodes, 774 edges, density 0.35, fully connected (1 component).

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Privacy & PII

All text fields are scrubbed before release. Masked patterns include: email addresses, phone numbers, national IDs, passport numbers, credit card numbers, dates of birth, usernames, and IBANs. Rows containing high-risk PII are dropped entirely. The export never includes device identifiers, IP addresses, or browser fingerprints.

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Team

This dataset was created by researchers at the College of Computing, Umm Al-Qura University, Mecca, Saudi Arabia.

Name	Role	HuggingFace
Mourad Mars	Principal Investigator	@mouradmars
Hassan Barmandah	AI Researcher	@HassanB4
Abdulrhman Alassaf	Software Engineer	—

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Citation

If you use this dataset in your research, please cite:

@misc{mars2026haakkim,
  title        = {Haakkim: An Arena-Style Human Preference Evaluation Platform for Arabic {LLMs}},
  author       = {Mars, Mourad and Barmandah, Hassan and Alassaf, Abdulrhman},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Haakkim/Haakkim-1.0v}},
  note         = {College of Computing, Umm Al-Qura University, Mecca, Saudi Arabia}
}

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License

This dataset is released under CC BY 4.0. Model responses remain subject to the terms of the respective model providers.

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Contact

• Platform: https://haakkim.tech
• Institution: College of Computing, Umm Al-Qura University, Mecca, Saudi Arabia