Gemma 4 vs Llama 4 vs Qwen 3.6 on Arabic Evaluation

Three open-weight families dominate the 2026 sovereign shortlist for Arabic-capable assistants: Gemma 4 from Google DeepMind, Llama 4 from Meta, and Qwen 3.6 from Alibaba Cloud. All three ship multilingual instruct variants, all three claim Arabic competence, and all three can be deployed on hardware an Omani institution owns. The procurement question is narrower: when an Arabic-first workload meets a published evaluation harness, which family wins, where, and by how much. This piece distils what the public benchmarks say in May 2026, and what that means for buyers building under Gemma 4 256K context alongside other open families.

The three contenders in 2026

Each family is a different bet on the Arabic problem, and the differences matter before any benchmark is run.

• Gemma 4 from Google DeepMind ships in four sizes (E2B, E4B, 26B-A4B, 31B), is multimodal (text and image, audio on small variants), and was pre-trained on 140+ languages with out-of-the-box support for 35+. The 256K context window is the headline differentiator. Arabic is supported but is not the optimisation target.
• Llama 4 from Meta ships as Scout (17B active, 16 experts) and Maverick (17B active, 128 experts), both natively multimodal mixture-of-experts. Meta lists 12 supported languages including Arabic. The instruct variants land in the top tier of HELM Arabic for open weights, with Maverick the stronger of the two.
• Qwen 3.6 from Alibaba Cloud spans Plus, Flash, Max-Preview, 35B-A3B mixture-of-experts, and 27B dense. Qwen 3.6 cites support for 201 languages and dialects with explicit attention to right-to-left scripts and Arabic dialects. The 27B dense model is the pragmatic single-institution default; Plus is the throughput choice.

None of these is an Arabic-first model. Falcon Arabic and Falcon-H1 Arabic from TII still lead the dedicated Arabic leaderboards. The question this article answers is which of the three multilingual flagships an institution should pick when one of them must do the daily Arabic work alongside English, code, and tool use.

Arabic eval methodology

Sovereign procurement decks in 2026 settle on four published Arabic suites plus a domestic spot-check.

• ALUE, the Arabic Language Understanding Evaluation, modelled on GLUE: NLI, semantic similarity, sentiment, dialect identification, offensive-language detection. Strong baseline for everyday MSA correctness.
• ArabicMMLU, the Arabic counterpart to MMLU, with 14,575 native Arabic multiple-choice questions across 40 subjects sourced from school exams in eight Arab countries, validated to roughly 96 percent accuracy. The right test for institutional knowledge.
• AraBench, QCRI's long-running Arabic machine-translation suite, covering MSA and dialectal Arabic across multiple genres. Operationally relevant when inbound foreign correspondence has to be summarised and translated inside the perimeter.
• HELM Arabic, the Stanford CRFM holistic suite. The current leaderboard top open-weights entry is Qwen3 235B A22B Instruct 2507 FP8 at 0.786, with Llama 4 Maverick (17Bx128E) Instruct FP8 also in the top ten according to the Stanford CRFM HELM Arabic announcement. Qwen 3.6 variants now inherit and extend that lineage.
• Omani-formal MSA spot-check, an in-house harness of ministerial correspondence, Royal Court phrasing, governance-memo style, and Khaleeji code-switched chat. This is the test that decides procurement, not any public number.

Run all five against every candidate. Lock the harness so quarterly re-runs remain valid as new model variants land.

Where each model wins, where each loses

The directional picture below reflects published runs and Hosn's own internal eval against Omani-formal prompts. Treat single-point scores as indicative; the spread between runs is real.

The headline: Qwen 3.6 wins the typical Arabic procurement matrix. Llama 4 Maverick is a credible alternative when an institution prefers Meta's licence terms or already runs Llama infrastructure. Gemma 4 wins when long context or on-device deployment dominate the requirement, and is the right secondary in a multi-model rack.

Practical recommendation per use-case

• Ministry assistant for Arabic correspondence and English research: Qwen 3.6 27B as the default; Falcon Arabic as the secondary for Maghrebi or classical Arabic.
• Procurement-file synthesis or whole-codebase reads: Gemma 4 31B as the long-context primary, Qwen 3.6 as the secondary for everything else.
• Existing Llama-stack institutions: Llama 4 Maverick FP8 with vLLM as the primary, with a Qwen 3.6 fallback wired in for Arabic-heavy tasks.
• Edge or on-device sovereign assistants: Gemma 4 E4B and 26B-A4B for laptop and small-workstation deployments where Qwen 3.6 27B is too heavy.
• Multi-model rack: run Qwen 3.6 and Gemma 4 side by side, add Llama 4 Maverick or Falcon Arabic on request, route per task inside the institution's own control plane.

Rapid model churn and re-eval cadence

The 2026 cycle is uncomfortably fast. Qwen 3.6 dense 27B shipped in late April, Llama 4 Maverick is still receiving new instruct revisions, Gemma 4 sizes are evolving across E2B, E4B, 26B-A4B, and 31B. A score that was decisive in March may be merely competitive by July. The right discipline for a sovereign institution is to lock the evaluation harness, not the model: ALUE, ArabicMMLU, AraBench, HELM Arabic, and the in-house Omani spot-check, run quarterly and on every adapter promotion. Decide the model question on this run, not the run from last quarter.

Email [email protected] or message +968 9889 9100 for a one-hour briefing tailored to your concurrency, dialect mix, and procurement constraints. Pricing is by quotation. We will come to you.