What Is gpt-image-2? Capabilities, Pricing Logic, and Use Cases
A full breakdown of the gpt-image-2 image model: what it can do, how it differs from earlier models, typical use cases, and how to use it well in an illustration workflow.
What gpt-image-2 is
gpt-image-2 is a new-generation image model focused on high quality, strong consistency, and precise instruction following. Compared with earlier models it is markedly better at layout whitespace, brand consistency, and reference-image fidelity.
Core capabilities
- Text-to-image: generate from a written description
- Image-to-image / reference editing: keep a subject, style, or layout consistent from uploads
- Multiple ratios: 1:1, 16:9, 9:16 and other common formats
- Quality tiers: trade speed against fidelity as needed
Typical use cases
Article headers, social covers, YouTube thumbnails, product shots, ad creatives, posters, slide visuals — essentially every content-image need.
On quality and cost
Higher quality tiers consume more compute and therefore cost more. In practice, draft at medium quality and finalize at high quality for the best value.
How to get the most out of it
A strong model still needs strong instructions. Handing your article context and reference images to an agent that writes scene-optimized prompts beats one-line prompting every time. Related reading: text-to-image vs image-to-image.
Try it
Peituka runs on gpt-image-2 — open it to experience the full description-to-image flow.
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