OpenAI

A spatial-reasoning test. Each object has a precise relationship (inside, on top, behind, seen through the glass), so it measures whether a model follows explicit placement instructions and handles transparency and refraction rather than just approximating the scene.

Combines a strict isometric viewpoint, clean PBR-style materials, and embedded text in one tightly framed scene. It tests whether a model holds a consistent 45-degree perspective, renders the labels accurately, and keeps the composition centered and uncluttered.

A character-portrait test focused on fine materials. It checks how well a model renders engraved metal under warm torchlight, layered textures (leather, cloth, braided hair), and lifelike eyes and skin without drifting into a plastic, over-smoothed look.

This competition tests how well AI image models truly understand language versus how much they rely on visual habits from their training data. The prompt is deliberately simple on the surface but devilishly hard in practice. Most models default to the familiar trope of an astronaut riding a horse. By forcing the reversal, we measure three critical capabilities that separate good models from great ones:

• Strict instruction following (including negations)
• Accurate subject-object relationships and spatial hierarchy
• Resistance to strong dataset biases

A structured infographic test. The model must place six labeled steps in order, hold a consistent flat-vector icon style, and render several short labels (step names, crew names, Tranquility) accurately. It rewards layout discipline and punishes garbled text.

A logo and branding test. It checks accurate text (the accented Caffè and Est. 1720), a clean vector-emblem look rather than a photographic one, and a cohesive vintage style. Small text and crisp flat shapes are common weak spots for image models.

Tests whether a model can produce a believable documentary photo instead of a polished render. The difficulty is in natural skin and lighting, wet-pavement reflections, and the imperfect framing and motion blur that real street photography has but models tend to smooth away.

A multi-subject photorealism test. Four distinct animals must each keep correct anatomy and detailed fur while interacting naturally in one scene under warm directional light. The usual failure points are blended or malformed animals and fur that turns mushy.