The right Arabic AI model for your business is the one that handles your customers' real Arabic, dialects included, at a cost you can carry and in a location your regulator accepts. In 2026 you have a real choice: Saudi Arabia's ALLaM, the UAE's Jais and Falcon, Qatar's Fanar and Egypt's Karnak all offer Arabic-first options, several with open weights. This guide explains each Arabic LLM, how to access it, how it compares with global models, which data laws apply, and how to choose without locking yourself in.
Why does Arabic need special attention from AI models?
Because Arabic is a huge language with a thin online footprint, and the Arabic your customers type rarely matches the textbook. UNESCO notes that Arabic is used daily by more than 400 million people. Yet W3Techs finds Arabic on only 0.6% of websites whose content language it can identify, against 49.5% for English (October 2026 data). Models learn from text, so Arabic gets a far smaller share of their training.
The gap shows up in four places:
- Dialects. Customers in Cairo, Riyadh and Casablanca can ask the same question with almost no words in common. Formal Modern Standard Arabic (MSA) does not cover them.
- Mixed scripts. People switch between Arabic, English and Arabizi (Arabic in Latin letters and numbers) inside one WhatsApp message.
- Right-to-left text. Prices, order numbers and English product names inside Arabic sentences break formatting and extraction in careless systems.
- Cultural context. Forms of address and religious occasions decide whether a reply sounds respectful or robotic.
Our pillar post on AI trends for 2027 names Arabic and sovereign AI as one of seven shifts to plan for. Here we go model by model.
What is ALLaM, and how can your business use it?
ALLaM is Saudi Arabia's Arabic-first model family, built at SDAIA's National Center for Artificial Intelligence and now offered commercially by HUMAIN. It reaches businesses in two forms.
The open one is ALLaM-7B-Instruct-preview on Hugging Face, listed under Apache 2.0. SDAIA trained it from scratch on 4 trillion English tokens, then 1.2 trillion mixed Arabic and English tokens, with a 4,096-token context window. You can run it on your own servers.
The larger one is ALLaM 34B. A 2025 evaluation paper calls it the most capable public ALLaM model and notes that HUMAIN built HUMAIN Chat, a closed service, on it. You use it through HUMAIN's products, not as a download.
In August 2026, Microsoft and HUMAIN announced plans to make ALLAM models available through Microsoft Foundry and to let organizations build specialized Arabic agents on ALLAM inside Microsoft 365 Copilot. No launch dates were given, so treat it as a roadmap. If your company runs on Microsoft 365 in Saudi Arabia, watch this route.
What is Jais, and what changed with Jais 2?
Jais is the UAE's flagship open Arabic model, and Jais 2 is the version worth testing today. Inception, a G42 company, released the first Jais in August 2023 as a 13-billion-parameter model built with MBZUAI and Cerebras.
Jais 2 arrived on Hugging Face in December 2025 in 8B and 70B chat versions. The Jais-2-70B-Chat model card describes a model trained from scratch with a custom Arabic-centric vocabulary, covering MSA, regional dialects and Arabic-English code-switching. It is published under Apache 2.0, but you must accept access conditions and share contact details to download it. Its 8,192-token context window is short for long contracts.
Inception also runs Jais 2 as a public web chat, so your team can try it before you commit engineering time.
What are Falcon Arabic and Falcon-H1 Arabic?
They are Arabic models from Abu Dhabi's Technology Innovation Institute (TII), and the newer one is built for long documents. TII launched Falcon Arabic in May 2025, a 7-billion-parameter model based on Falcon 3 with a 32,000-token context window.
In January 2026, TII launched Falcon-H1 Arabic in 3B, 7B and 34B sizes, on a hybrid Mamba-Transformer architecture with context windows of up to 256,000 tokens. Its product page lists Gulf, Levantine, North African and Egyptian dialects alongside MSA. TII reports that the 34B model scores 75.36% on the Open Arabic LLM Leaderboard, ahead of Qwen2.5 72B and Llama-3.3 70B. That is the vendor's own claim, so test it.
Access is the catch. TII offers both models through its Falcon Chat playground, and at the time of writing we found no Falcon Arabic or Falcon-H1 Arabic weights on TII's Hugging Face page. If self-hosting matters, confirm licence and deployment terms with TII first.
Which other regional Arabic models belong on your shortlist?
Two more: Qatar's Fanar and Egypt's Karnak.
Fanar 2 comes from the Qatar Computing Research Institute (QCRI) at Hamad Bin Khalifa University. The Fanar-2-27B-Instruct model card lists 27 billion parameters, a 32,768-token context window, an Apache 2.0 licence and support for MSA plus Gulf, Levantine and Egyptian dialects. QCRI built it by continuing the training of Google's Gemma 3 27B and offers API access on request.
Karnak is Egypt's entry. In August 2026, the Ministry of Communications and Information Technology announced the pilot launch of Karnak Chat, built on the Karnak model from its Applied Innovation Center. The ministry says Karnak handles MSA and Egyptian colloquial Arabic and can run inside local data centers when needed. The center publishes Karnak 40B on Hugging Face under Apache 2.0, built on the open Qwen3-30B-A3B model.
The field as of October 2026:
| Model | Origin | Open or closed | How to access | Strengths |
|---|---|---|---|---|
| ALLaM 7B | Saudi Arabia (SDAIA) | Open weights, Apache 2.0 | Hugging Face download | Trained from scratch, easy to self-host |
| ALLaM 34B | Saudi Arabia (HUMAIN) | Closed | HUMAIN Chat; Microsoft Foundry planned | Microsoft 365 route ahead |
| Jais 2 (8B, 70B) | UAE (Inception, MBZUAI, Cerebras) | Open weights, Apache 2.0, gated | Hugging Face, Jais web chat | Arabic-centric vocabulary, code-switching |
| Falcon-H1 Arabic (3B to 34B) | UAE (TII) | No public weights found | Falcon Chat playground | Up to 256K context |
| Fanar 2 (27B) | Qatar (QCRI, HBKU) | Open weights, Apache 2.0 | Hugging Face, API on request | Gulf, Levantine and Egyptian dialects |
| Karnak (40B) | Egypt (MCIT Applied Innovation Center) | Open weights, Apache 2.0 | Hugging Face, Karnak Chat pilot | Egyptian colloquial Arabic, local hosting |
Do regional Arabic models beat global models like GPT, Gemini or Claude?
On Arabic benchmarks some do, but the benchmark is not your business, and no public table settles it for closed models. The main scoreboard is the Open Arabic LLM Leaderboard on Hugging Face, which ranks open Arabic LLMs. TII's Falcon-H1 Arabic claim comes from it.
Read such results with care. Leaderboards test knowledge and reading tasks, not a frustrated customer typing Egyptian Arabic at midnight, and vendors publish their best numbers. Closed models from OpenAI, Google and Anthropic sit outside an open-model leaderboard, so you must test them yourself.
The line between regional and global is also blurring. Fanar 2 is built on Gemma 3 and Karnak on Qwen3, so part of their Arabic strength rests on a global foundation. ALLaM 7B and Jais 2, by contrast, were trained from scratch. For your decision, origin matters less than two questions: how well does it handle your customers' Arabic, and where does it run?
Which data protection laws decide where your Arabic AI can run?
Three laws matter most in our region, and each cares about where personal data goes and who processes it.
- Saudi Arabia. The Personal Data Protection Law was enacted on 16 September 2021 and governs the processing of personal data of individuals residing in the Kingdom. SDAIA's National Data Governance Platform runs the national register, with a registration route for entities outside the Kingdom and an AI service provider accreditation service.
- United Arab Emirates. Federal Decree by Law No. 45 of 2021 Concerning the Protection of Personal Data has been in force since 2 January 2022 and is listed as active.
- Egypt. The Personal Data Protection Law No. 151 of 2020 now has its executive regulations, and the Personal Data Protection Center says the focus has turned to practical implementation.
What it means for you: before any customer chat reaches a model, ask where prompts and logs are stored, whether the vendor trains on your data, and what legal basis covers any transfer abroad. Open-weight models give the cleanest answer, because you can host them in-country. This is not legal advice, so bring your counsel in early, especially for health, finance and government work.
How do you choose the right Arabic AI model for your business?
Test candidates on your own Arabic, price them per conversation, and pick hosting your regulator accepts. The process we recommend:
- Collect 150 to 200 real messages. Pull them from WhatsApp, email and call notes, with personal details removed.
- Cover every dialect you serve. Include Egyptian, Gulf, Levantine or Maghrebi samples, Arabizi and mixed Arabic-English messages in the proportions you actually receive.
- Shortlist three models. One global frontier model, one open regional model you could host, and one hosted regional service.
- Score blind with native speakers. Rate accuracy, tone and dialect fit without revealing which model wrote which answer.
- Price the whole conversation. Tokenizers split Arabic differently (Jais 2 and Karnak ship Arabic-optimized vocabularies for this reason), so compare cost per 1,000 resolved conversations, not price per million tokens.
- Match hosting to the law. Choose vendor cloud, in-country cloud or your own servers before the pilot.
- Keep a switch. Put a thin layer between your systems and the model so you can swap models when a better one ships.
That last step is where most projects go wrong. Connect the model through a standard interface such as MCP, and your CRM, catalog and WhatsApp flows stay put when the model changes. If customer service comes first, our guide to Arabic AI customer service on WhatsApp and the web covers the integration side.
Which Arabic AI model should you start with?
Start with the one that wins your own test, and build so you can leave it. Saudi enterprises on Microsoft 365 may find ALLaM through Foundry the easy path once it ships. If data must stay on your servers, Jais 2, Fanar 2 and Karnak offer open weights. For long Arabic documents, Falcon-H1 Arabic deserves a trial once access terms are clear. A global frontier model belongs in every comparison.
We stay model-independent on purpose: we build the integration layer, the Arabic test set and the guardrails, then let your results pick the model.
Want to know which Arabic AI model fits your customers? Book a 15-minute call and we will help you design a test on your own Arabic data. Or see how we build custom enterprise software and AI integrations.
