// Cross-vendor image comparison

GPT Image 2.6 FlarevsNano Banana 2.1

Di GPTProtoAggiornato il 5 novembre 2026

GPT Image 2.6 Flare (OpenAI) and Nano Banana 2.1 (Google). GPT Image 2.6 Flare is OpenAI's fast-iteration image model. Nano Banana 2.1 is Google's per-image model in the same catalog. The cards keep those billing units apart.

Read each card in its own unit. Flare shows text input and image output per 1M tokens. Nano Banana 2.1 shows the 1K and 4K image rates. Price a Flare call from the response usage, and price Nano Banana 2.1 from the size you request.

Try GPT Image 2.6 Flare on GPTProto Prova Nano Banana 2.1 su GPTProto

GPT Image 2.6 Flare↗OpenAI · gpt-image-2.6-flare

Ideale perOpenAI's image model for interactive generation and editing.

GPTProto catalog rate, list price struck through

Text input~~$5.00~~$4.75/M−5%

Image output~~$30.00~~$28.50/M−5%

Vedi la pagina modello

Nano Banana 2.1↗Google · gemini-nano-banana-2.1

Ideale perGoogle's per-image model for generation and edits at 1K, 2K, and 4K.

GPTProto catalog rate, list price struck through

Immagine 1K~~$0.0336~~$0.02016/img−40%

Immagine 4K~~$0.113~~$0.0678/img−40%

Vedi la pagina modello

// Fianco a fianco

GPT Image 2.6 Flare vs Nano Banana 2.1Catalog rates.

Each price is the text-to-image scene from the GPTProto catalog at request time. A token row and a per-image row stay on their own lines.

Voce

GPT Image 2.6 Flare

Nano Banana 2.1

Fornitore

OpenAI

Google

ID modello

gpt-image-2.6-flare

gemini-nano-banana-2.1

Billing

Per token

Per image

Nel catalogo dal

November 5, 2026

8 ottobre 2026

Disponibilità

Text input / 1M

$4.75~~$5.00~~ listino ufficiale

—

Image input / 1M

$7.60~~$8.00~~ listino ufficiale

—

Image output / 1M

$28.50~~$30.00~~ listino ufficiale

—

Immagine 1K

—

$0.02016~~$0.0336~~ listino ufficiale

Immagine 2K

—

$0.03024~~$0.0504~~ listino ufficiale

Immagine 4K

—

$0.0678~~$0.113~~ listino ufficiale

Catalog multiplier

0.95

0.6

Routes

Da testo a immagineModifica immagine

Uso API

# Submit
curl --request POST "https://gptproto.com/api/v3/openai/gpt-image-2.6-flare/text-to-image" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY" \
    --header "Content-Type: application/json" \
    --data '{
        "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
        "n": null,
        "quality": "auto",
        "size": "auto",
        "enable_sync_mode": false,
        "response_format": "url"
    }'

# Result
curl --request GET "https://gptproto.com/api/v3/predictions/$result_id/result" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY"

# Submit
curl --request POST "https://gptproto.com/api/v3/google/gemini-nano-banana-2.1/text-to-image" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY" \
    --header "Content-Type: application/json" \
    --data '{
        "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
        "aspect_ratio": "16:9",
        "size": "1k",
        "output_formart": "png",
        "enable_base64_output": false,
        "enable_sync_mode": false
    }'

# Result
curl --request GET "https://gptproto.com/api/v3/predictions/$result_id/result" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY"

List price is the platform rate divided by the catalog price multiplier. The page does not turn a token rate into a cost per image.

List rate beside the GPTProto rate. Each row scales to its own list price.

UfficialeGPTProto

GPT Image 2.6 FlareOpenAI

Text input / 1M$5.00

Text input / 1M−5%$4.75

Image input / 1M$8.00

Image input / 1M−5%$7.60

Image output / 1M$30.00

Image output / 1M−5%$28.50

Nano Banana 2.1Google

Immagine 1K$0.0336

Immagine 1K−40%$0.02016

Immagine 2K$0.0504

Immagine 2K−40%$0.03024

Immagine 4K$0.113

Immagine 4K−40%$0.0678

// Price

Two catalog rates.Two units.

GPT Image 2.6 Flare is billed per token. Nano Banana 2.1 is billed per image. The table is the catalog. The struck-through figure is the list price, and the bold figure is the GPTProto rate.

OpenAI's image guide says equal token rates can still produce a different cost per image, because quality and size change how many tokens a render uses. See Image generation. This catalog does not publish that token count, so the page leaves a per-image dollar amount for the token-billed model off the table. Price that call from the usage field on the response, then compare the total with the size row on the per-image model.

On this catalog, GPT Image 2.6 Sunburst lists the same text-input, image-input, and image-output rates. Read that pair on GPT Image 2.6 Sunburst vs Nano Banana Pro.

Google's two per-image models are on Nano Banana 2.1 vs Nano Banana Pro. The image-count calculator stays off this page because the two models here do not share a unit.

// Ricarica

One balance.
Either model.

Top up once, then open the model you are calling. The rate on this page is the catalog rate for that model's unit.

$15

Credito semplice · nessun bonus

Per provare. Serve a collegare l’endpoint e testare modelli nuovi.

Più popolare

$100

+4% crediti bonus

L’importo che scelgono spesso chi sviluppa da solo o sta portando un progetto piccolo in produzione.

Risparmio massimo · +6%

$1,000

+6% crediti bonus

Per i team che hanno già traffico di produzione. Il credito bonus si somma al prezzo già scontato.

// Fit

Which jobeach model is for.

OpenAI describes Flare for fast iteration and Sunburst for detailed compositions. Google's two per-image models have their own page.

Voce

GPT Image 2.6 Flare

Nano Banana 2.1

Catalog role

Fast iteration

Per-image generation

Price it from

Response token usage

1K, 2K, or 4K row

// Choose

Start from the joband the unit.

Pick the model whose unit you can price for the brief in front of you.

GPT Image 2.6 Flaregpt-image-2.6-flare

Fast iteration

You want the fast-iteration model OpenAI pairs with Images 2.6.
You can price the call from the token usage on the response.
You need text input, image input, and image output as separate catalog rates.

Open GPT Image 2.6 Flare

Nano Banana 2.1gemini-nano-banana-2.1

Per-image generation

You want a size price before the call returns.
The brief is a 1K, 2K, or 4K image on the Google row.
You are comparing a flat image price with a token-billed model.

Open Nano Banana 2.1

When both belong in the test

Run the same brief on both model pages when you need a token-billed render and a size-priced render. Keep each invoice on its own unit.

// Models

Same catalog.Different jobs.

Choose Flare when the work is fast iteration and you can settle the token total after the response. Choose Nano Banana 2.1 when you want the catalog price for a 1K, 2K, or 4K image before you call it.

OpenAI's Images 2.6 notes describe Flare for interactive generation and editing, and Sunburst for detailed compositions and edit consistency. Those notes are the vendor's description. This page does not add a latency or quality score of its own.

The rates above are the text-to-image scene. Check GPT Image 2.6 Flare and Nano Banana 2.1 before an edit call.

// How to read this

Use the rowthat matches the call.

A token rate and a per-image rate answer different questions. The token rate prices 1M tokens. The image rate prices one generated image at a named size. The page keeps those purchases on separate rows.

For the token-billed model, multiply the usage on the response by the matching row: text input, image input, or image output. For the per-image model, use the 1K, 2K, or 4K row for the size you send.

The bars under the table compare each row with its own list price. A full-width bar is the list rate. The shorter bar is the GPTProto rate for that same row.

// Next

Open the modelyou plan to call.

Start with GPT Image 2.6 Flare or Nano Banana 2.1. The other Images 2.6 pair is GPT Image 2.6 Sunburst vs Nano Banana Pro.

Open GPT Image 2.6 Flare Open Nano Banana 2.1

// Domande

Pricing questionsfor this pair.

Answers use the catalog rows above.

01Why are the prices in different units?+

GPT Image 2.6 Flare and Nano Banana 2.1 are sold on different catalog meters. One side is USD per 1M tokens. The other side is USD per image at 1K, 2K, and 4K. The table keeps those meters on separate rows.

02Which model is cheaper per image?+

The catalog does not publish how many tokens one GPT Image render uses, and that count changes with size and quality. Price the token-billed call from the usage field on the response. Price the Google call from the size row. Then compare those two totals for the brief you actually ran.

03Do Flare and Sunburst share a token rate?+

On this catalog request, Flare and Sunburst list the same text-input, image-input, and image-output rates. The difference on the two pages is the Google model beside them, and the job OpenAI describes for each Images 2.6 model.

04Does the card's discount apply to every row?+

Catalog multiplier is 0.95 for GPT Image 2.6 Flare and 0.6 for Nano Banana 2.1. The card percent is the gap between the list price and the GPTProto rate. This table is the text-to-image scene, so check the model page before an edit call.

// Testimonianze

Scopri come altri usano GPTProto.

Creator e sviluppatori condividono i modelli e i flussi di lavoro che hanno creato.

Blogstra

Reddit · r/Bloggers

#Routing

For a team already juggling DeepSeek, Kimi, Qwen, or other providers, a shared routing layer can be reasonable if it removes duplicated work and the fallback behavior is tested. GPTProto is one implementation of this approach.

X (Twitter) · @ChillaiKalan__

#Cost

I've been testing GPTProto recently, and it's honestly made my creative workflow much simpler. Instead of paying for multiple subscriptions, I can access several leading AI models from one place.

Sanskriti Naruka

X (Twitter) · @snskritinaruka

#Video

I turned this single prompt into a cinematic fantasy video using GPTProto. "Continuous 15-second cinematic shot, 4K resolution, hyper-realistic dark fantasy photorealism…"

Caden Flux

X (Twitter) · @Caden_Flux

#Video

I challenged myself to create a cinematic AI cooking short in just 15 seconds. I used GPTProto to bring the entire workflow together, from image generation to video, all in one place.

Zara

X (Twitter) · @ZaraIrahh

#Creative

Made with Seedance 2.0 + GPT Image 2 on GPTProto. A Pixar-style commercial with the perfect glow.

Many-Operation2625

Reddit · r/LLMDevs

#Integration

What worked for me was pointing the cloud connections at GPTProto so the frontend only sees one endpoint and I just change the model name to swap. I am not rebuilding a connection from scratch every time.