Follows direction
Handle detailed prompts and multi-part layouts.
Create posters, infographics and precise edits with GPT Image 2 on Cinel. Strong prompt following and legible text, 2 credits per 1K image for members.
See prompt variations, character consistency, and refined visual outputs generated on Cinel.
Handle detailed prompts and multi-part layouts.
Change selected concepts while preserving the rest.
Produce clearer labels, signs and graphic copy.
An engineering and creative breakdown of model performance, architecture, and practical production workflows.
GPT Image 2 is OpenAI's image model released alongside ChatGPT Images 2.0, and it earns its reputation by doing what you actually asked. Give it a crowded brief — three products on a shelf, a price tag on the left, a headline across the top — and it keeps track of each instruction instead of drifting toward a generic "pretty picture". On Cinel you can run it from a prompt, guide it with reference images, and use it as an editor that changes one idea while leaving the rest of the frame alone. Members pay 2 credits for a 1K image, and it is one of the models covered by the free daily credit allowance.
Its defining trait is instruction fidelity. Many models capture the mood of a prompt but quietly drop details; GPT Image 2 is built to respect counts, positions, labels and hierarchy, which is why designers lean on it for layouts that carry information — infographics, menus, comparison charts, event posters and UI mock-ups. Legible in-image English is part of that package: short headlines, product names and button labels usually come out spelled correctly.
The second difference is editing discipline. When you supply a reference and ask for "replace the mug with a glass, keep everything else", GPT Image 2 tends to preserve lighting, composition and untouched objects. That makes it a safer choice for iterative client work than models that re-imagine the entire frame on every pass. Compared with its newer sibling GPT Image 2.5, it is the established, well-understood option: the same member price at 1K, with behaviour many teams have already tuned their prompts around.
| Item | GPT Image 2 |
|---|---|
| Developer | OpenAI (released with ChatGPT Images 2.0) |
| Modes | Text-to-image, image-to-image, reference editing |
| Model output tiers | 1K / 2K / 4K · low / medium / high quality |
| References | Cinel: up to 3 (non-members), up to 8 for members where supported |
| Outputs per request | Members: 1–4 |
| Cinel cost | 2 credits per 1K image (members) · 4 credits via daily credits |
| Strengths | Infographics, posters, in-image text, controlled edits |
GPT Image 2.5 is the newer OpenAI generation (September 2026) with stronger photoreal labels and steadier step-by-step edits at the same 2-credit member price — try it when a GPT Image 2 result is close but product text or realism needs another notch. Stay on GPT Image 2 if your prompts are already tuned for it or you want its well-known layout behaviour. Choose Seedream 4.5 when the deliverable must be 4K and you are combining many reference photos for an e-commerce catalogue.
GPT Image 2 is the image model OpenAI released together with ChatGPT Images 2.0. The ChatGPT app can add its own reasoning and search on top, so results inside ChatGPT may not match the standalone model exactly.
A 1K image costs members 2 credits. Paid with the free daily credit allowance it costs 4 credits. Higher resolutions, extra references or multiple outputs can raise the estimate, which Cinel shows before you submit.
It is one of the better models for legible English text. Keep each phrase short, put it in quotes, and specify font style and position. Always proofread before publishing.
Upload the picture as a reference, describe the single change you want, and state that everything else must stay the same. GPT Image 2 is designed to preserve untouched areas.
Pick GPT Image 2 for information-heavy layouts, exact object counts and text. Pick Nano Banana 2 when you need to blend several reference photos fast or export at 4K.
Members can request one to four outputs per prompt, which is handy for comparing layouts. Each output uses its own credits.