Model comparison
Compare GPT-Image-2.5 Sunburst and GPT-Image-2 using the same provider-sourced image generation rubric. No mystery score and no invented benchmark ranking.
Facts checked September 9, 2026
Set your usage. Your estimate updates as you type.
Example output: square 1,024 × 1,024 images. Prices depend on supported resolution and quality; edits and retries can cost extra.
Estimates exclude taxes, tools, cache storage/writes, free allowances and custom discounts. Image estimates cover output only, not prompt or reference-image charges. Quality modes differ by model. Unlisted settings are not treated as free.
| Model | Access | Pricing & comparisons |
|---|---|---|
| GPT-Image-2OpenAI | OpenAI | $2.50 text input · $4 image input · $15 image output / 1M tokens |
| GPT-Image-2.5 SunburstOpenAI | OpenAI | $5 text input · $8 image input · $30 image output / 1M tokens |
Quick take
OpenAI's quality-first GPT Image 2.5 model for demanding generation, precise edits, subject preservation, typography, and high-resolution creative work.
Sunburst is the quality-first option, not automatically the best value for every image. Test Flare after quality passes, inspect exact text and preserved details, and measure cost per accepted output.
OpenAI's current image model for detailed generation and editing, especially complex prompts, text-heavy visuals, and high-fidelity source images.
Do not treat the lowest per-image price as typical: quality, resolution, input images, and thinking/token use all change the final bill.
Compare the published facts
Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.
| Image generation | GPT-Image-2.5 Sunburst | GPT-Image-2 |
|---|---|---|
| Typical price basisA representative current provider price with its quality or resolution basis. | OpenAI: $5 text input · $8 image input · $30 image output / 1M tokens | OpenAI: $2.50 text input · $4 image input · $15 image output / 1M tokens |
| Maximum outputMaximum documented resolution or pixel area for the listed route. | Up to 3,840px per edge / 8.29MP | Up to about 4K / 8.29MP on fal |
| Core tasksWhether the model supports generation, editing, or both. | Generation and editing | Generation and editing |
| Reference imagesHow the model can use images to guide or edit an output. | Image inputs with improved subject preservation | High-fidelity image input |
| Text & layoutProvider-documented positioning for typography and structured composition. | Exact-text and structured-layout workflows | Designed for detailed text-heavy images |
| API accessDirect and notable third-party routes included in this guide. | OpenAI Images API, Responses tool | OpenAI, fal |
How to choose
Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.
OpenAI says direct API inputs and outputs are not used for model training by default. Standard abuse-monitoring retention and organization-specific data controls still apply.
Provider and API links
For direct OpenAI API use, API content is not used for training by default and standard retention controls apply. fal stores request input/output by default, but its no-store header can prevent payload storage; uploaded CDN files require separate handling.
Provider and API links
Frequently asked questions
GPT-Image-2.5 Sunburst: OpenAI's quality-first GPT Image 2.5 model for demanding generation, precise edits, subject preservation, typography, and high-resolution creative work. GPT-Image-2: OpenAI's current image model for detailed generation and editing, especially complex prompts, text-heavy visuals, and high-fidelity source images.
Consider GPT-Image-2.5 Sunburst when your priority is Precise product and subject-preserving edits. Consider GPT-Image-2 when your priority is Text-heavy graphics and infographics. Test both with your own data and provider route before committing.
No. This comparison aligns provider-published facts for the Image generation category. It does not claim a universal winner or combine incompatible third-party benchmark scores.
