Key features

Native image generation & editing — generate images or edit existing photos via natural-language prompts. (Generate / Edit).
Multi-image fusion — combine multiple input images into one photorealistic scene.
Character consistency — keep the same subject or character appearance across edits and prompts. (Consistency).
SynthID watermarking — all outputs include an invisible SynthID to identify AI-generated content. (Watermark).

Technical details

Architecture & positioning: built on the Gemini 2.5 Flash family — designed as a low-latency “Flash” variant that trades a little model size/throughput for much faster per-call response and cost efficiency while retaining stronger reasoning than earlier Flash tiers.
Input formats & limits: accepts inline base64 images for small inputs and file uploads via the File API for larger images (recommended for >20 MB). Supports common MIME types (JPEG, PNG).
Modes of operation: text-to-image, image editing (inpainting / semantic masking), style transfer, multi-image composition, and interleaved text+image responses (useful for illustrated instructions, recipes, or mixed content).
Provenance & safety mechanisms: visible watermarks on AI outputs plus hidden SynthID markers and policy enforcement layers to limit explicit disallowed content.

Limitations & known risks

Content policy constraints: models enforce content policies (e.g., disallowing explicit sexual content and some illicit content), but enforcement is not perfect — generating images of public figures or controversial icons may still be possible in some scenarios, so policy checks are essential. )
Failure modes: possible identity drift in extreme edits, occasional semantic misalignment (when prompts are under-specified), and artifacts in very complex scenes or extreme viewpoint changes.
Provenance & misuse: while watermarks and SynthID are present, these do not prevent misuse — they assist detection and attribution but are not a substitute for human review in sensitive workflows.

Typical use cases

Product & ecommerce: place/catalog products into lifestyle shots via multi-image fusion.
Creative tooling / design: fast iterations in design apps (Adobe Firefly integration cited).
Photo editing & retouching: localized edits from natural language (remove objects, change color/lighting, restyle).
Storytelling / character assets: keep characters consistent across panels and scenes.

Pricing for Nano Banana

Explore competitive pricing for Nano Banana, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how Nano Banana can enhance your projects while keeping costs manageable.

Comet Price (USD / M Tokens)	Official Price (USD / M Tokens)	Discount
Per Request:$0.0312	Per Request:$0.039	-20%

Sample code and API for Nano Banana

Access comprehensive sample code and API resources for Nano Banana to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Nano Banana in your projects.

Python
JavaScript
Curl

from google import genai
from google.genai import types
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com"

client = genai.Client(
    http_options={"api_version": "v1beta", "base_url": BASE_URL, "timeout": 600000},
    api_key=COMETAPI_KEY,
)

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

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

# Output directory
OUTPUT_DIR = os.path.join(os.path.dirname(__file__), "..", "output")
os.makedirs(OUTPUT_DIR, exist_ok=True)

for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = part.as_image()
        output_path = os.path.join(OUTPUT_DIR, "gemini-native-image.png")
        image.save(output_path)
        print(f"Image saved to: {output_path}")

Versions of Nano Banana

The reason Nano Banana has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation.

version
gemini-2.5-flash-image
gemini-2.5-flash-image-preview

Nano Banana