GPT Image 2.5 vs Nano Banana 2: I Ran the Same Brief. Then the Benchmark Broke.

A comparison of GPT Image 2.5 Sunburst and Nano Banana 2 is presented as a reproducible workflow test rather than a universal ranking, emphasizing that image-generation costs are driven not only by per-image pricing but also by review, reruns, and correction work. It recommends using identical prompts, framing, output counts, reviewers, and approval criteria while allowing each model’s best native resolution and quality settings, then inspecting outputs at full size for exact text, object counts, geometry, product details, preserved edits, and plausible real-world facts. The proposed tests cover a Moon Orchard beverage advertisement requiring precise packaging copy, a SoHo street scene that must be externally verified against maps or primary sources, and a hybrid physical-and-wireframe smartphone designed to test object continuity, alongside narrow product-image edits that assess whether unrelated visual details remain intact. The guide notes that completed outputs should be documented with prompts, settings, dates, endpoint information, and review notes, while blocked or unrun comparisons should not be presented as results. It also distinguishes image generation from motion production, advises treating location imagery and model-generated claims as unverified evidence, and calls for commercial checks involving trademarks, rights, likenesses, and local regulations.

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