Jais (Inception / G42)
The leading open-weight Arabic LLM family from G42's Inception, now in its Jais 2 generation
Overview
Jais is an Arabic-first large language model family from Inception, a G42 company in Abu Dhabi, developed with MBZUAI and Cerebras. The open-weight models range from compact sizes to a 70B Jais 2 generation trained on large Arabic-and-English datasets, and are widely regarded as the leading open Arabic LLMs. Models are available openly with developer access for building Arabic AI applications.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 4 Jun 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
Jais (Inception / G42) in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Jais is the reference open Arabic LLM, backed by serious compute (G42, Cerebras) and academic depth (MBZUAI), with the Jais 2 generation pushing into frontier-scale open weights for Arabic-and-English use. For Arabic-language products it is hard to beat among open models.
Its ASEAN relevance is narrow unless an organization needs Arabic specifically; for Southeast Asian languages purpose-built or multilingual frontier models will serve better. Within its niche, however, Jais is best-in-class and free to self-host.
About this listing
This entry was compiled from publicly available data including Jais (Inception / G42)'s official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Jais (Inception / G42) unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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