요금제+7% 보너스
// Cross-vendor image comparison

GPT Image 2.6 FlarevsNano Banana 2.1

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.

// 나란히 비교

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.

항목
GPT Image 2.6 Flare
Nano Banana 2.1
제공업체
OpenAI
Google
모델 ID
gpt-image-2.6-flare
gemini-nano-banana-2.1
Billing
Per token
Per image
카탈로그 등록
November 5, 2026
2026년 10월 8일
가용성
Text input / 1M
$4.75$5.00 공식 정가
—
Image input / 1M
$7.60$8.00 공식 정가
—
Image output / 1M
$28.50$30.00 공식 정가
—
1K 이미지
—
$0.02016$0.0336 공식 정가
2K 이미지
—
$0.03024$0.0504 공식 정가
4K 이미지
—
$0.0678$0.113 공식 정가
Catalog multiplier
0.95
0.6
Routes
텍스트에서 이미지이미지 편집
텍스트에서 이미지이미지 편집
API 사용
# 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.
공식GPTProto
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
1K 이미지$0.0336
1K 이미지−40%$0.02016
2K 이미지$0.0504
2K 이미지−40%$0.03024
4K 이미지$0.113
4K 이미지−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.

// 충전

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
정액 충전 · 보너스 없음

먼저 시험하는 금액입니다. 엔드포인트를 붙이고 새 모델을 돌려보기 좋습니다.

최대 절약 · +6%
$1,000
+6% 보너스 크레딧

이미 실서비스 트래픽을 돌리는 팀용입니다. 보너스 크레딧은 할인된 요금 위에 더해집니다.

// 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.

항목
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.
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.

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.

// 자주 묻는 질문

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.

// 사용자 이야기

우리 말만 믿지 마세요.사용자 이야기를 보세요.

실재하는 공개 계정의 글입니다. 여기 있는 것은 만들어낸 것이 아닙니다.

BL
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.

KK
K
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.

SNSanskriti Naruka
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…"

CFCaden Flux
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.

ZZara
Zara
X (Twitter) · @ZaraIrahh
#Creative

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

MO
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.

// 시작

다음 1억 토큰,정가로 낼 필요 없어요.

브라우저에서 이미지와 영상을 만들거나, API 키 하나로 텍스트, 이미지, 영상 모델을 앱에 넣습니다. 할인 요금도 여기서 봅니다.

  • ✓OpenAI 호환
  • ✓200개 이상 모델
  • ✓공식보다 10–30% 저렴
  • ✓높은 가동률, 자동 페일오버
✓클립보드에 복사했습니다