GPT Image 2.5 vs GPT Image 2.0: We Gave Both the Same Campaign Brief. Which One Held It Together?

Comparing GPT Image 2 with GPT Image 2.5 Sunburst should focus on production reliability rather than isolated attractive samples, using identical prompts, inputs, canvas settings, and approval criteria to assess subject preservation, product and label fidelity, setting, composition, usable negative space, and completion time. The proposed methodology uses paired one-output tests across product, lifestyle, story, travel, fragrance, and hospitality scenes, while treating the results as limited spot checks that require additional seeds before a workflow migration. GPT Image 2 remains a practical baseline for established workflows, while GPT Image 2.5 may be preferable for high-value or tightly constrained edits when it demonstrably preserves protected details such as products, faces, layouts, or reference characters. List prices and speed claims should not determine the decision because actual costs vary by quality, dimensions, inputs, rejected runs, and human review time; the recommended measure is cost per approved asset. Generated images still require manual verification of text, packaging, trademarks, legal copy, and technical quality, and they should support rather than replace design production, brand approval, source management, and legal review.

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