Ideogram 4.0 is a 9.3 billion parameter text-to-image foundation model released by Ideogram as an open-weight resource on June 3, 2026. The model is engineered for high-fidelity graphic design and professional visual asset creation, specifically via its Quality mode, which prioritizes aesthetic refinement and precise prompt adherence. It is designed to bridge the gap between proprietary frontier models and the open ecosystem, excelling in typography, spatial reasoning, and photorealism.\n\nThe architecture consists of a 34-layer single-stream Diffusion Transformer (DiT) where text and image tokens share the same projections within a unified sequence. A notable design choice is the integration of Qwen3-VL-8B-Instruct, a vision-language model, as the primary text encoder. The DiT consumes hidden states from 13 intermediate layers of this encoder, enabling the model to achieve a deep understanding of complex spatial instructions and nuanced descriptive prompts.\n\n## Advanced Layout and Design Features\nIdeogram 4.0 introduces native support for structured JSON prompting, allowing for precise control over image composition. This capability enables users to define bounding-box coordinates for object placement and specify exact hex color palettes, ensuring brand consistency without the need for external ControlNet modules. The model supports native 2K resolution (2048 pixels) and provides robust multilingual text rendering, supporting scripts such as Latin, Chinese, and Arabic with high accuracy.\n\nThe model demonstrates strong performance on design-centric benchmarks, including the DesignArena and 7Bench leaderboards. Its training process involved a "describe-to-structure-to-recreate" loop, teaching the model to interpret scenes as structured data before pixel generation. This methodology results in professional-grade outputs with legible typography—achieving a 0.97 score on the X-Omni OCR benchmark—making it suitable for production-ready posters, logos, and marketing assets.\n\n## Prompting Best Practices\nTo obtain optimal results in Quality mode, users are encouraged to utilize the official structured JSON prompt schema. This involves separating instructions into a high_level_description for the general scene, a style_description for aesthetics, and a compositional_deconstruction block for specific element placement. For precise color matching, a color_palette array of hex codes should be included. Producing multiple iterations is recommended for complex subjects or intricate typography to select the highest-quality stochastic variation.