Model comparison
GPT-Image-2.5 Flare vs Nano Banana 2
Compare GPT-Image-2.5 Flare and Nano Banana 2 using the same provider-sourced image generation rubric. No mystery score and no invented benchmark ranking.
Facts checked September 9, 2026
Model comparison
Compare GPT-Image-2.5 Flare and Nano Banana 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 |
|---|---|---|
| Nano Banana 2Google | $0.67 | |
| GPT-Image-2.5 FlareOpenAI | OpenAI | $5 text input · $8 image input · $30 image output / 1M tokens |
Quick take
OpenAI's speed-first GPT Image 2.5 model for everyday generation and editing, positioned at GPT Image 2-level image quality with lower latency.
Flare's speed positioning is a provider claim, not a fixed latency guarantee. Run repeated tests with the same prompt, references, dimensions, and quality before moving production traffic.
Google's versatile image workhorse for fast generation, conversational edits, multiple references, grounded imagery, and outputs up to 4K.
There is no free API tier for this model, and grounded generation can add search charges beyond the image price.
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 Flare | Nano Banana 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 | $0.067 / 1K image |
| Maximum outputMaximum documented resolution or pixel area for the listed route. | Up to 3,840px per edge / 8.29MP | 0.5K, 1K, 2K, 4K |
| Core tasksWhether the model supports generation, editing, or both. | Generation and editing | Generation and conversational editing |
| Reference imagesHow the model can use images to guide or edit an output. | Image inputs with improved subject preservation | Multiple references and multi-turn edits |
| Text & layoutProvider-documented positioning for typography and structured composition. | Exact-text and structured-layout workflows | Improved multilingual text rendering |
| API accessDirect and notable third-party routes included in this guide. | OpenAI Images API, Responses tool | Gemini API, 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
Google says paid Gemini API content is not used to improve its products. Unpaid-service terms differ, so confirm that the project is attached to an active billing account before sending confidential material.
Provider and API links
Frequently asked questions
GPT-Image-2.5 Flare: OpenAI's speed-first GPT Image 2.5 model for everyday generation and editing, positioned at GPT Image 2-level image quality with lower latency. Nano Banana 2: Google's versatile image workhorse for fast generation, conversational edits, multiple references, grounded imagery, and outputs up to 4K.
Consider GPT-Image-2.5 Flare when your priority is Fast everyday image generation. Consider Nano Banana 2 when your priority is Conversational image editing. 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.