
GPT Image 2 is Rivya's OpenAI image model for structured prompts, reference-led edits, and predictable 1K, 2K, or 4K output. Use it when product visuals, campaign scenes, or brand layouts need clearer instruction following.

Example output
Example; generating model unverified · Preview for Minimalist White Product Showcase, focused on composition, editable details, visual hierarchy, and brand-safe production constraints.
Text to Image · Image to Image

Example output
Example; generating model unverified · Preview for Minimalist White Product Showcase, focused on composition, editable details, visual hierarchy, and brand-safe production constraints.
Best for
Supports
Online trial
No result yet. Submit a prompt to start a run.
Prompt starters
Model proof
Use the online trial above, then compare these example results to judge finish, rhythm, and task fit before you spend more credits in Studio.

Lead sample
Minimalist White Product Showcase image preview showing the subject, composition, style direction, and reusable output structure.
Input
Prompt + optional images
What to notice
Minimalist White Product Showcase image preview showing the subject, composition, style direction, and reusable output structure.
Credits per use
per_image · 1K=3 · 2K=5 · 4K=8 · ⌈total⌉
Why this model works
Text-to-image and image-to-image in one Rivya model page
1K, 2K, and 4K resolution tiers for clearer budget control
Up to 16 reference images for structured editing briefs
Best-fit tasks
Model proof
Decision fit
Decision fit
Best-fit tasks
Use it when the task looks like this
Quiet facts
Provider
OpenAI
Category
Image
Capabilities
Text to Image · Image to Image
Credit model
per_image · 1K=3 · 2K=5 · 4K=8 · ⌈total⌉
Input path
Prompt + optional images
Prompt setup
Up to 20,000 chars
Developer access
GPT Image 2 has callable Public API modes, but reference-media modes still need Files API upload support. Check the model reference before submitting.
GPT Image 2 FAQ
GPT Image 2 is best when you already have a structured brief: product visuals, campaign concepts, reference-led edits, or scenes where object placement and instruction hierarchy matter. It keeps the OpenAI image workflow but makes resolution the main budget control.
Yes. On Rivya, GPT Image 2 supports text-to-image and image-to-image, with up to 16 reference images to guide an edit.
Use 1K for early direction checks, 2K when you want a clearer draft, and 4K only after the layout and reference hierarchy are already working. Current integration constraints mean 4K should not be paired with 1:1 or Auto aspect ratio.
GPT Image 1.5 uses Medium and High quality tiers. GPT Image 2 uses 1K, 2K, and 4K resolution tiers, making it easier to match cost to the delivery stage. Both remain useful, so Rivya keeps them as separate model pages rather than replacing one with the other.
If you only need the cheapest rough visual, a lighter model may be enough. If you choose 4K before the prompt or references are stable, you may spend more credits on avoidable reruns.
You can draft a prompt, choose aspect ratio and resolution, add reference images, and test whether GPT Image 2 fits your task before moving deeper into the Rivya studio workflow.
You can inspect the page and prepare the prompt publicly, but submitting a generation and uploading reference images require signing in so Rivya can manage credits and task history.
Check the resolution first, then the aspect ratio, then the reference image priority. For 4K, avoid Auto and 1:1 because the current GPT Image 2 integration does not accept those combinations.
Stay on this page for a first structured test. Move to the full workspace when you need saved history, repeated variants, deeper asset management, or multiple rounds around the same visual direction.
Compare alternatives
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Why consider it
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Why consider it
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Google's premium image model with 4K output, 11 aspect ratios, and up to 8 reference images. Optimized for high-fidelity brand and campaign visuals with superior detail and color accuracy.
Why consider it
Consider it when the credit note fits your next run: per_image · 1K=8 · 2K=8 · 4K=14 · ⌈total⌉.