Gemini 2.5 Flash Image(Nano Banana): Feature, Benchmark and Usage

[CometAPI-Official]

In late August 2025 Google (DeepMind) released Gemini 2.5 Flash Image — widely nicknamed “nano-banana” — a low-latency, high-quality image generation + editing model that’s been integrated into the Gemini app, Google AI Studio, the Gemini API and CometAPI. It’s designed to produce photorealistic images, preserve character consistency across edits, fuse multiple input images, and perform fine, localized edits through natural-language prompts. The model is available in preview / early GA and is already topping image leaderboards (LMArena) while shipping with safety mechanisms (SynthID watermarking and product-level filters).

What can you do with Gemini 2.5 Flash Image (use cases)?

Gemini 2.5 Flash Image is explicitly built for creative, productivity, and applied-imaging scenarios. Typical and emergent use cases include:

Rapid product mockups and e-commerce

Drag product photos into scenes, generate consistent catalog imagery across environments, or swap colors/fabrics across a product line — all while preserving the product’s identity. The multi-image fusion features and character/product consistency make it attractive for catalog workflows.

Photo retouching and targeted edits

Remove objects, fix blemishes, change clothing/accessories, or tweak lighting with natural-language prompts. The localized edit capability lets non-experts perform professional-style retouching using conversational commands.

Storyboarding and visual storytelling

Place the same character across different scenes and keep their look consistent (useful for comics, storyboards, or pitch decks). Iterative edits let creators refine mood, framing, and narrative continuity without rebuilding assets from scratch.

Education, diagrams, and design prototyping

Because it can combine text prompts and images and has “world knowledge,” the model can help generate annotated diagrams, educational visuals, or quick mockups for presentations. Google even highlights templates in AI Studio for use cases like real estate mockups and product design.

How do you use Nano Banana API ?

Below are practical snippets adapted from[ CometAPI API docs](https://apidoc.cometapi.com/guide-to-calling-gemini-2-5-flash-image-1425263m0) and Google’s API docs. They demonstrate the common flows: text-to-image and image + text to image (editing) using the official GenAI SDK or REST endpoint.

Note: in CometAPI’s docs the preview model name appears as gemini-2.5-flash-image-preview. The examples below echo the official SDK examples (Python and JavaScript) and a REST curl example; adapt keys and file paths to your environment.

REST curl example from CometAPI

Use Gemini’s official generateContent endpoint for text-to-image generation. Place the text prompt in contents.parts[].text.Example (Windows shell, using ^ for line continuation):

curl --location --request POST "https://api.cometapi.com/v1beta/models/gemini-2.5-flash-image-preview:generateContent" ^
--header "Authorization: sk-xxxx" ^
--header "User-Agent: Apifox/1.0.0 (https://apifox.com)" ^
--header "Content-Type: application/json" ^
--header "Accept: */*" ^
--header "Host: api.cometapi.com" ^
--header "Connection: keep-alive" ^
--data-raw "{    "contents": [{
      "parts": [
        {"text": "A photorealistic macro shot of a nano-banana on a silver fork, shallow depth of field"}
      ]
    }]
  }'}"
| grep -o '"data": "[^"]*"' \
| cut -d'"' -f4 \
| base64 --decode > gemini-generated.png

The response contains base64 image bytes; the pipeline above extracts the "data" string and decodes it into gemini-generated.png.

This endpoint supports “image-to-image” generation: upload an input image (as Base64) and receive a modified new image (also in Base64 format).Example:

curl --location --request POST "https://api.cometapi.com/v1beta/models/gemini-2.5-flash-image-preview:generateContent" ^
--header "Authorization: sk-xxxx" ^
--header "User-Agent: Apifox/1.0.0 (https://apifox.com)" ^
--header "Content-Type: application/json" ^
--header "Accept: */*" ^
--header "Host: api.cometapi.com" ^
--header "Connection: keep-alive" ^
--data-raw "{  \"contents\": [    {      \"role\": \"user\",      \"parts\": [        {          \"text\": \"'Hi, This is a picture of me. Can you add a llama next to me\"        },        {          \"inline_data\": {            \"mime_type\": \"image/jpeg\",            \"data\": \"iVBORw0KGgoA Note: This is a Base64 string\"          }        }      ]    }  ],  \"generationConfig\": {    \"responseModalities\": [      \"TEXT\",      \"IMAGE\"    ]  }}"

**Description:**First, convert your source image file into a Base64 string and place it in inline_data.data. Do not include prefixes like data:image/jpeg;base64,.The output is also located in candidates[0].content.parts and includes:An optional text part (description or prompt).The image part as inline_data (where data is the Base64 of the output image).For multiple images, you can append them directly, for example:

{
  "inline_data": {
    "mime_type": "image/jpeg",
    "data": "iVBORw0KGgo...",
    "data": "iVBORw0KGgo..."
  }
}

Below are developer examples adapted from Google’s official docs and blog. Replace credentials and file paths with your own.

Python (official SDK style)

from google import genai
from PIL import Image
from io import BytesIO

client = genai.Client()

prompt = "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"

# Text-to-Image
response = client.models.generate_content(
    model="gemini-2.5-flash-image-preview",
    contents=[prompt],
)

for part in response.candidates[0].content.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = Image.open(BytesIO(part.inline_data.data))
        image.save("generated_image.png")

This is the canonical Python snippet from Google’s docs (preview model ID shown). The same SDK call pattern supports image + prompt editing (pass an image as one of the contents).More details refer to [gemini doc.](https://ai.google.dev/gemini-api/docs/image-generation?hl=zh-cn)

Conclusion

If your product needs robust, low-latency image generation and, especially, reliable editing with subject consistency, Gemini 2.5 Flash Image is now a production-grade option worth evaluating: it combines state-of-the-art image quality with APIs designed for developer integration (AI Studio, Gemini API, and Vertex AI). Carefully weigh the model’s current limitations (fine text in images, some stylization edge cases) and implement responsible-use safeguards.

Getting Started

CometAPI is a unified API platform that aggregates over 500 AI models from leading providers—such as OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude, Midjourney, Suno, and more—into a single, developer-friendly interface. By offering consistent authentication, request formatting, and response handling, CometAPI dramatically simplifies the integration of AI capabilities into your applications. Whether you’re building chatbots, image generators, music composers, or data‐driven analytics pipelines, CometAPI lets you iterate faster, control costs, and remain vendor-agnostic—all while tapping into the latest breakthroughs across the AI ecosystem.

Developers can access [Gemini 2.5 Flash Image](https://www.cometapi.com/gemini-2-5-flash-image/)(Nano Banana CometAPI list gemini-2.5-flash-image-preview/gemini-2.5-flash-image style entries in their catalog.) through CometAPI, the latest models version listed are as of the article’s publication date. To begin, explore the model’s capabilities in the [Playground](https://api.cometapi.com/chat) and consult the [API guide](https://apidoc.deerapi.com/调用-gemini-2-5-flash-image-指南-7305430m0) for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

Ready to Go?→ [Sign up for CometAPI today](https://www.chatbase.co/auth/signup) !