적합한 용도OpenAI's image model for interactive generation and editing.
GPTProto catalog rate, list price struck through
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.
적합한 용도OpenAI's image model for interactive generation and editing.
GPTProto catalog rate, list price struck through
적합한 용도Google's per-image model for generation and edits at 1K, 2K, and 4K.
GPTProto catalog rate, list price struck through
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.
# 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.
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.
Top up once, then open the model you are calling. The rate on this page is the catalog rate for that model's unit.
먼저 시험하는 금액입니다. 엔드포인트를 붙이고 새 모델을 돌려보기 좋습니다.
혼자 개발하거나, 작은 프로젝트를 실서비스에 올릴 때 많이 고릅니다.
이미 실서비스 트래픽을 돌리는 팀용입니다. 보너스 크레딧은 할인된 요금 위에 더해집니다.
OpenAI describes Flare for fast iteration and Sunburst for detailed compositions. Google's two per-image models have their own page.
Pick the model whose unit you can price for the brief in front of you.
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.
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.
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.
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.
Answers use the catalog rows above.
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.
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.
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.
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.
실재하는 공개 계정의 글입니다. 여기 있는 것은 만들어낸 것이 아닙니다.
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.

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.

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

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.

Made with Seedance 2.0 + GPT Image 2 on GPTProto. A Pixar-style commercial with the perfect glow.
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.
I route the calls through GPTProto so a fallback is a config switch instead of a weekend rewrite when a model disappears. It turns "my default model just got export controlled" from an incident into a config change.

I used to switch between different AI tools just to compare results. Now I just use GPTProto. GPT-5, Claude, Gemini, Kimi, and more — all in one workspace.
I route the calls through GPTProto so the model id and latency land in one place regardless of which provider is behind it. The win is having the log schema consistent across providers.

I created this 15-second cinematic product video with GPTProto using Seedance 2.0, and I was really impressed by how smooth the workflow was.

GPTProto routes the character prompt and shot list to a top model on one API key: 20 minutes … voices it and burns in captions from the same key: 20 minutes.
Text, images, and voice are configured separately. You can keep OpenRouter for text and use GPTProto for visuals.
The call layer underneath the router is GPTProto, so swapping a model does not require provisioning a new provider integration. Changing models becomes cheap enough that the question stops being "should we change."
모델 비교, API 설정, 이미지와 영상 작업 흐름을 정리했습니다.
브라우저에서 이미지와 영상을 만들거나, API 키 하나로 텍스트, 이미지, 영상 모델을 앱에 넣습니다. 할인 요금도 여기서 봅니다.