AI models tested for Arabic
Does it actually work for Saudi, Najdi, Hijazi or Gulf Arabic? Benchmarks rarely say. The community tests these models on real speech and text — and reports back.
CAMeLBERT
CAMeL Lab, NYU Abu Dhabi
BERT models pre-trained per Arabic variant (MSA, dialectal, classical) for NER, POS, sentiment and dialect identification. The mix checkpoint remains the most-used entry point despite dating from 2021.
Fanar-2-27B
QCRI / HBKU
Arabic-centric flagship of the Fanar 2.0 release (Mar 2026), continually pretrained from google/gemma-3-27b-pt on ~166B Arabic, English and code tokens with 32K context. It adds native Arabic reasoning traces, selective thinking mode and tool calling. This repo is text-in/text-out; image generation, image understanding and poetry are separate Fanar-2 models.
Fanar-1-9B
QCRI / HBKU
Qatar's sovereign Arabic LLM. This 8.7B instruct model — the 'Prime' branch — continually pretrains google/gemma-2-9b on 1T Arabic and English tokens; a separate 7B 'Star' model was trained from scratch. The card claims MSA plus Gulf, Levantine and Egyptian dialects, and alignment with Islamic values and Arab culture.