Using AI in Arabic: What Actually Works in 2026

If you have tried to use a mainstream AI tool in Arabic, you already know the feeling: the formal answer reads beautifully, then you ask for something in your own dialect and it drifts — too stiff, slightly off, occasionally just wrong. That is not your imagination, and it is not a flaw you are doing anything to cause. It is a direct consequence of how these systems were built and what "Arabic" actually means.

Arabic is not one language to a model. It is Modern Standard Arabic (MSA) — the formal register of news, government, contracts, and education — sitting on top of more than a dozen living dialects that people actually speak, from Gulf Khaleeji to Egyptian to Levantine to Maghrebi. MSA is what gets written down and digitized, so models see enormous amounts of it. Dialects live mostly in speech and informal chat, so models see far less, and what they do see is messier. Understanding that split is the single most useful thing you can know before you type a word.

Where general models hold up — and where they slip

The big international models — ChatGPT, Gemini, Claude — are genuinely strong in MSA. For formal writing, translation, summarizing a document, drafting a polite email, or explaining a concept in standard Arabic, they are reliable enough for real work. The grammar is sound, the register is appropriate, and the output reads like proper written Arabic.

The slippage starts when you move toward dialect, nuance, and culture. Ask for a Khaleeji marketing caption, a casual WhatsApp reply in Emirati, or a joke that lands locally, and general models tend to default back to MSA or produce something that is technically Arabic but culturally flat. They can also stumble on names, places, religious phrasing, and the small conventions that signal "a real person from here wrote this." None of this makes them useless — it means you have to know which jobs to hand them and how to ask.

The regional models closing the gap

A serious wave of Arabic-first models has matured, and in 2026 they are no longer research curiosities — they are strategic infrastructure for government, finance, healthcare, and public services across the region.

Jais — a regionally tuned Arabic-first family. The latest generation is large (around 70 billion parameters) and was trained on what is described as the largest Arabic-first dataset assembled to date, spanning roughly 17 regional dialects alongside MSA. It has topped Emirati-dialect evaluation benchmarks among large models — a meaningful signal for Gulf use.

Fanar — a Qatar-developed model with a strong focus on Arabic dialect understanding and on translating between English, MSA, and dialects. It is multimodal (text and images) and was refined with feedback from hundreds of testers across Arab countries.

ALLaM — a Saudi-developed family that has ranked among the strongest smaller models for dialect capability, well suited to lighter, cost-sensitive deployments.

Falcon (Arabic) — the Falcon family extended with enhanced Arabic capability, offered in several sizes with very long context windows, useful when you need to process large Arabic documents at once.

The practical takeaway is not "abandon the international tools." It is that for dialect-heavy, culturally sensitive, or sovereignty-conscious work, a regional Arabic-first model is increasingly the better instrument — and the gap with general models in everyday Arabic keeps narrowing.

How to actually get good Arabic results

Regardless of which model you use, a few habits reliably raise the quality:

Say which Arabic you want. Do not just write "in Arabic." Specify MSA for formal output, or name the dialect ("Emirati/Khaleeji dialect," "Egyptian," "Levantine") for conversational output. This one instruction removes most of the drift.

Match the model to the job. Formal documents, translation, and summaries — any strong general model in MSA. Dialect copy, local tone, regional nuance — reach for an Arabic-first model.

Give a local example. One or two lines of the exact tone you want, written the way a local would, anchors the output far better than adjectives.

Translate through MSA when in doubt. If a dialect result feels off, ask for MSA first, confirm the meaning, then ask the model to render it in the target dialect. This two-step path is how several regional systems are designed to work internally.

Always review names, numbers, and religious or cultural phrasing. This is where even good models make quiet mistakes, and it is exactly where a local reader notices instantly.

Why this matters for your business

For a business serving the Gulf and the wider Arab world, "good enough in English, translated later" is no longer good enough. Customers can tell when Arabic content was written by someone who understands them versus run through a generic model on default settings. The opportunity in 2026 is that the tools to do this well — both international models for MSA and regional models for dialect — are finally within reach, and most competitors have not yet figured out how to use them properly.

That is the gap our catalogue is built around: prompts, skills, and assets written and vetted bilingually, in clean tashkeel-free Arabic, so the Arabic you ship reads like it was meant for the reader — because it was.