The race to build image models that render the Arab world correctly

Anyone who has tried to generate an image of a Gulf setting with a general model knows the failure pattern. Ask for a Saudi family at a meal and you get clothing that is approximately right and wrong in the details, architecture borrowed from somewhere else in the region or from a Western idea of it, food that does not belong, and social arrangements that a local viewer reads as immediately off. The output is not offensive so much as unusable, in the specific sense that no designer could put it in front of a client. The cause is not architectural. It is that the training data barely contains the material. General-purpose image models under-represent Arabic, Islamic and regional visual concepts, including regional landmarks, clothing styles, foods and everyday scenes, because the image corpora these models learn from are dominated by Western sources. A model cannot render accurately what it has seen a thousand examples of when it has seen a million of something else. Two distinct answers to this are now being built in the region, and they are different kinds of project. The research-led answer is visible in Qatar's Fanar 2.0 stack, which includes Oryx-IG, an image generation model built specifically for cultural alignment. Its approach is described as taxonomy-driven data acquisition combined with direct preference optimisation. Both halves matter. Taxonomy-driven acquisition means deciding in advance what concepts the model must handle, regional landmarks, clothing, foods, everyday scenes, and collecting data against that list rather than hoping scale covers it. Preference optimisation means training on human judgements about which of two outputs is more culturally correct, which is the only mechanism that captures the kind of correctness that cannot be written as a rule. The commercial answer is the Adobe and HUMAIN work. The two companies are preparing a jointly developed image-generation model built using Adobe Firefly Foundry and tuned to Saudi cultural context, visual references and creative needs, described as the first image model jointly developed with Foundry. It arrives attached to the largest distribution event in the region's creative software market: more than 27 million eligible Saudi citizens and residents aged 13 and over are to receive twelve months of free access to Firefly Standard and Express Premium, expected to become available by the end of 2026. The difference between these two is not quality, which nobody outside can currently assess, since neither model has been independently evaluated and the Adobe and HUMAIN model has not shipped. It is where they sit. A research model advances the field and is evaluated by researchers. A model inside Adobe's tools reaches the people who make the region's advertising, packaging and media, inside the software they already use, with rights clearance and production workflow attached. Distribution is a different form of impact from capability. The evaluation problem underneath both is real and unsolved. Cultural accuracy is not a metric in the way image fidelity is. Existing academic work on cultural sensitivity and expectation alignment in text-to-image models points in the same direction: the judgement of whether an image is right requires people from the culture depicted, and it varies within a culture as well as between cultures. The ImageEval shared task at ArabicNLP 2026 is building dedicated tracks for culturally grounded Arabic image understanding and generation, which is the beginning of a common yardstick rather than a finished one. That absence has a practical consequence for buyers. When a vendor claims a culturally tuned model, there is currently no standard result to point at. The only available test is the one an organisation can run itself: generate the twenty images your business actually needs, in the settings you actually work in, and have people from the market judge them. That takes an afternoon and is more informative than any benchmark that exists today. There is a commercial reason to expect more of these models regardless of how the research develops. Advertising, retail, entertainment and government communication in the Gulf all need locally correct imagery in volume, and the current alternative is photography, which is slower and more expensive. A model that reliably produces usable Gulf imagery has an obvious market, and the market is willing to pay. The caution is about what cultural tuning means when it is done at a national level. A model tuned to represent a country will encode a particular version of it, chosen by whoever assembled the taxonomy and whoever expressed the preferences. That is unavoidable, since representing everything equally is not a coherent target, and it is worth naming rather than treating tuned as a neutral synonym for accurate. Open weights help here, because a model whose weights are available can be examined and adapted by people who disagree with the choices. HUMAIN released M3 as an open-weig