Ideogram just dropped its first open-weight image model, and it is aiming straight at the production design workflow. Ideogram 4.0 is a 9.3 billion parameter Diffusion Transformer trained from scratch, with native 2K output, a structured JSON prompting interface, and weights you can download and run locally. It is already live in Krea, Picsart Flow, and the Ideogram app, with API access alongside.

The headline numbers are aggressive. On LMArena, Ideogram is the top-ranked open-weight lab and a top-5 image generation lab overall, and on an internal blind benchmark of graphic designers Bradley-Terry scores rank Ideogram 4 second overall behind only GPT Image 2 medium. The team also claims it outperforms open-weight models at 20B, 32B, and 80B MoE scale, which is the kind of efficiency claim that is worth testing yourself.

JSON in, pixels out

The most distinctive thing about 4.0 is how you prompt it. The model was trained exclusively on structured JSON captions rather than plain text prompts, with every training image described by per-element styling and optional bounding boxes and color specifications. Plain prose still works, but the model was built to consume something more like a layout spec.

What that schema actually exposes:

• Bounding boxes in [y_min, x_min, y_max, x_max] normalised to 0 to 1000 coordinates with the origin at the top left
• Colour palettes of up to 16 hex colours per image, with 5 per element
• Typed text elements that carry both the literal string and a styling description for multi-font posters
• Composable obj and text entries under a compositional_deconstruction field

The samples in the technical report use six text bounding boxes to anchor a poster's title, subtitle, date, tagline, credit and RSVP, twenty-eight boxes to mark inventory items, and four boxes to place a woman, window, monstera and cat in one scene. The intent is clear: stop rolling the dice on layout and just declare it.