GPT Image 1.5

OpenAI's latest image model. Outperformed by newer models on most tasks — see our selection guide.

Overview

Evolution of GPT Image 1.

Key Updates:

• Improved text rendering: Handles denser and smaller text with better legibility.

• Better image editing: Change specific elements without regenerating the entire scene.

• Identity preservation: Maintains facial likeness and composition across edits.

• Affordable quality: Low quality mode delivers decent results at minimal cost (1 credit per generation).

• Much faster than GPT Image 1: Up to 4x faster generations.

Ideal use cases

• Rapid iterations: Quick concept exploration using Low quality mode before committing to finals.

• Text-heavy graphics: Infographics, banners, and visuals requiring legible typography.

• Image editing and asset variations: Product variants, seasonal updates, localization without starting from scratch.

• Style transfers and try-ons: Clothing changes, filters, or hairstyle tests while maintaining subject consistency.

Weaknesses

• With the highest quality parameters, it is worse than Nano Banana Pro and Seedream 4.5 (for a similar cost).

• Limited aspect ratios (only 3 options vs. 10+ on other models).

• 3 reference images maximum.

• Yellow tint tendency, images can have a warm color cast by default. Pletor provides a dedicated parameter to remove this effect from generated visuals.

• Commercial aesthetic bias: outputs feel polished, less authentic for UGC-style content.

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How to use effectively

For fast iterations: Set quality to "Low" when exploring concepts. You'll get results in seconds at minimal cost, and the output quality is often sufficient for client reviews or internal alignment.

For text-heavy designs: Put exact copy in "quotes" and describe the typography style. Be specific: "Bold sans-serif, centered, high contrast" helps ensure legibility. Set quality to "High" for dense layouts.

For precise edits: Make one change at a time rather than rewriting entire prompts. Reference multiple input images by number: "Apply the style from image 1 to the subject in image 2."

For consistent characters: Always upload reference images when you need identity preservation across multiple generations. The model excels at maintaining facial likeness when given a clear reference.