Translation & Localization
Enterprise Arabic translation, not general-purpose machine translation
Good Arabic translation is either too coarse to publish or too heavy to run.
Standard multilingual models flatten Arabic's grammar, morphology and dialectal variation. Massive general-purpose LLMs translate better but are too heavy and too slow to run in production workflows at volume.
Delivered by
Mutarjim delivers state-of-the-art English-to-Arabic translation at 1.5B parameters - small enough to ship in production, beating GPT-4o mini on Tarjama-25. Lahjawi covers dialect-to-dialect and dialect-to-MSA translation across 15 Arabic dialects.
Why a small model is the point
State of the art at 1.5B parameters
Mutarjim is built on Kuwain 1.5B, Kawn Lab's own Arabic-centric language model, using a two-phase training approach on a curated bilingual corpus. It is small enough to ship in production, and it beats GPT-4o mini on English-to-Arabic.
Dialect is in scope
Lahjawi handles dialect-to-dialect and dialect-to-MSA translation across 15 Arabic dialects, which is where general multilingual models stop being useful entirely.
Benchmarked on something honest
Tarjama-25 is 5,000 expert-reviewed sentence pairs across diverse domains and lengths, built specifically to fix the domain narrowness and English-source bias that make existing Arabic-English benchmarks unreliable.
What it translates
Bidirectional, long-form, domain-heavy
Arabic-to-English and English-to-Arabic across medical, legal and technical content, preserving meaning through complex sentences rather than flattening them.
Fifteen Arabic dialects
Dialect-to-dialect and dialect-to-Modern Standard Arabic, so regional content is translatable material rather than an exception.
Diacritization as a preprocessing step
Sadeed resolves Arabic's default ambiguity by reading the grammar and meaning around each word. That is directly applicable to machine translation, search indexes and text-to-speech.
Sized for real workflows
Small enough for real-time deployment and low-resource environments, where a large general LLM is too slow or too expensive to run at volume.
The numbers behind it
- 1.5BParameters: Mutarjim rivals models 20× its size
- SOTAEnglish-to-Arabic on Tarjama-25, outperforming GPT-4o mini
- 15Arabic dialects covered by Lahjawi
What changes
Translation moves inside the workflow
It runs as a step in the pipeline rather than as a separate vendor engagement with its own turnaround.
Dialect content stops being untranslatable
Regional material becomes something the system can process rather than something routed to a human every time.
Ambiguity is resolved before it propagates
Diacritized text means search, TTS and downstream NLP stop inheriting the guesswork that undiacritized Arabic hands them.
How it deploys
Cloud API
Through Kawn Console: one account and one integration point for the whole model suite.
On-premises
For organisations with data sovereignty requirements. The compact model size is what makes this practical rather than theoretical.
Low-resource environments
Real-time deployment where a large general LLM would not fit the latency or cost budget.
By Industry
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