{"id":21226,"date":"2026-05-06T16:57:29","date_gmt":"2026-05-06T16:57:29","guid":{"rendered":"https:\/\/kittlb-26937.roald-dfw.servebolt.cloud\/?p=21226"},"modified":"2026-05-07T16:51:46","modified_gmt":"2026-05-07T16:51:46","slug":"kittl-review-gpt-image-2-0","status":"publish","type":"post","link":"https:\/\/www.kittl.com\/blogs\/kittl-review-gpt-image-2-0\/","title":{"rendered":"Kittl Review: GPT Image 2"},"content":{"rendered":"\n

1. Summary<\/h2>\n\n\n\n

GPT Image 2 is the highest-scoring image model in our testing<\/strong>. Prompt adherence is best-in-class \u2014 it handles more simultaneous constraints than any other model we tested, with layout fidelity that holds up under complex prompts. If you’re working on text-heavy design assets, editorial photorealism, or any workflow where accuracy matters more than speed, this is the one.<\/p>\n\n\n\n

Top strengths<\/h3>\n\n\n\n
    \n
  1. Text rendering (Score 4.9\/5.0)<\/strong> \u2014 Renders Latin, Chinese, Japanese, Korean, Arabic, and Cyrillic text accurately, even at small sizes. Exceptional at replacing, modifying, and iterating on text inside an image without breaking the layout.<\/li>\n\n\n\n
  2. Native reasoning engine & spatial precision<\/strong> (Score 4.8\/5.0 prompt adherence \u00b7 4.7\/5.0 spatial reasoning)<\/strong> \u2014 Handles up to 8 simultaneous constraints in a single prompt with accurate object placement and counting. No other model in our testing matches this level of layout fidelity on complex prompts.<\/li>\n\n\n\n
  3. Material fidelity (Score 4.8\/5.0)<\/strong> \u2014 Metal, glass, fabric, and multi-material surfaces render with commercial-grade realism.<\/li>\n<\/ol>\n\n\n\n

    Top gaps<\/h3>\n\n\n\n
      \n
    1. Weak fluid dynamics<\/strong> \u2014 Liquid pours, splashes, and smoke simulations fall behind Nano Banana Pro and Nano Banana 2.<\/li>\n\n\n\n
    2. Generation speed<\/strong> (Score<\/strong> 3.0\/5.0<\/strong>) \u2014 40\u201390s per image; same speed as their previous model (GPT Image 1.5).<\/li>\n<\/ol>\n\n\n\n

      Best-fit use cases<\/h3>\n\n\n\n
        \n
      • Posters and packaging with text<\/strong> \u2014 Complex labels, ingredient lists, and typographic layouts land on the first try.<\/li>\n\n\n\n
      • Dense infographics and UI mockups<\/strong> \u2014 Spatial reasoning keeps complex layouts organized without manual re-prompting.<\/li>\n\n\n\n
      • Editorial product photography<\/strong> \u2014 Photorealistic skin, fabric, and metal at commercial-grade quality.<\/li>\n<\/ul>\n\n\n\n

        2. How We Tested<\/h2>\n\n\n\n

        Test environment<\/h3>\n\n\n\n
          \n
        • Platform: Kittl (internal) + OpenAI API (gpt-image-2).<\/li>\n\n\n\n
        • Date range: April 21 \u2013 May 2026.<\/li>\n\n\n\n
        • Model version tested: gpt-image-2 (API).<\/li>\n<\/ul>\n\n\n\n

          Prompt set<\/h3>\n\n\n\n
            \n
          • Total prompts: ~200.<\/li>\n\n\n\n
          • Categories covered: product photography, text-heavy layouts, multilingual labels, packaging mockups, character sheets, infographics, editorial portraits, UI mockups, fluid\/liquid stress tests.<\/li>\n<\/ul>\n\n\n\n

            Scoring scale<\/h3>\n\n\n\n

            Each capability is scored 1\u20135 against a fixed definition. Scores are absolute, not relative to other models.<\/p>\n\n\n\n

            5 = Executes without meaningful failure.
            4 = Executes reliably with minor inconsistencies.
            3 = Inconsistent or partial execution.
            2 = Frequently fails or requires significant workarounds.
            1 = Unusable for this capability.<\/p>\n\n\n\n

            Capability definitions<\/h3>\n\n\n\n

            <\/p>\n\n\n