Google

Nano Banana

Name: Nano Banana
Brand: Google
Price: 0.0387 USD
Availability: InStock

AI Image Editing Model

Mode:

Image #6 Image-to-Image

Gemini 2.5 Flash Image is optimized for image understanding and generation, offering a balance of price and performance with fast and efficient image generation and editing capabilities.

Nano Banana generated image of Cinematic wide shot of an elderly artisan in a sun-drenched Mediterranean wor...

Nano Banana generated image of A macro photography shot of an antique mechanical watch movement, intricate b...

Try Model Benchmark examples

33K

Context Window

Max Images per Request

Supported Modes

Text to Image Image Edit

Active

Capabilities

Batch System prompt

Details

Model ID

gemini-2.5-flash-image

Also known as: gemini-2.5-flash-image-preview

Creator

Google

Family

gemini-2.5-flash-image

Released

October 2025

Max Input Images

Max Output Tokens

32,768

Ready to integrate?

Access gemini-2.5-flash-image via our unified API.

Create Account

Available at 4 providers

Starting from

$0.039 /image via Gemini API, Vertex AI · +2 more

Prices shown are in USD · Some prices estimated from per-megapixel or per-token pricing

See all providers

Providers & Pricing (4)

Nano Banana is available from 4 providers, with per-image pricing starting at $0.0387 through fal.ai.

fal.ai

Image Edit

fal/gemini-2.5-flash-image-edit

Provider Model ID: fal-ai/gemini-25-flash-image/edit

$0.039 /image

View pricing details

Replicate

Text to Image Image Edit

replicate/gemini-2.5-flash-image

Provider Model ID: google/gemini-2.5-flash-image

$0.039 /image

View pricing details

Gemini API

Text to Image Image Edit

gemini/gemini-2.5-flash-image

Provider Model ID: gemini-2.5-flash-image

Input

Token

$0.300 per 1M

Output

Token image

$30.00 per 1M

View official pricing • As of Oct 02, 2025

Vertex AI

Text to Image Image Edit

vertex/gemini-2.5-flash-image

Provider Model ID: gemini-2.5-flash-image

Input

Token

$0.300 per 1M

Output

Token image

$30.00 per 1M

View official pricing • As of Oct 02, 2025

gemini-2.5-flash-image-preview API OpenAI-compatible

Integrate Nano Banana into your workflow via Lumenfall’s OpenAI-compatible API to perform scalable text-to-image generation and complex image editing tasks.

Base URL

https://api.lumenfall.ai/openai/v1

Model

gemini-2.5-flash-image

Code Examples

Image Edit

/v1/images/edits

curl -X POST \

  https://api.lumenfall.ai/openai/v1/images/edits \

  -H "Authorization: Bearer $LUMENFALL_API_KEY" \

  -F "model=gemini-2.5-flash-image" \

  -F "[email protected]" \

  -F "prompt=Add a starry night sky to this image" \

  -F "size=1024x1024"

# Response:

# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }

import OpenAI from 'openai';

import fs from 'fs';

const client = new OpenAI({

  apiKey: 'YOUR_API_KEY',

  baseURL: 'https://api.lumenfall.ai/openai/v1'

});

const response = await client.images.edit({

  model: 'gemini-2.5-flash-image',

  image: fs.createReadStream('source.png'),

  prompt: 'Add a starry night sky to this image',

  size: '1024x1024'

});

// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

console.log(response.data[0].url);

from openai import OpenAI

client = OpenAI(

    api_key="YOUR_API_KEY",

    base_url="https://api.lumenfall.ai/openai/v1"

response = client.images.edit(

    model="gemini-2.5-flash-image",

    image=open("source.png", "rb"),

    prompt="Add a starry night sky to this image",

    size="1024x1024"

# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }

print(response.data[0].url)

Parameter Reference

Required Supported Not available

Core Parameters

Parameter	Type	Description	Modes
`prompt`	string	Required. Text prompt for image generation	T2I Edit
`seed`	integer	Random seed for reproducibility	T2I Edit

Size & Layout

Parameter	Type	Description	Modes
`size`	string	Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9") WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.	T2I Edit
`aspect_ratio`	string	Aspect ratio of the output image (e.g. "16:9", "1:1") Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.	T2I Edit
`resolution`	string	Output resolution tier (e.g. "1K", "4K") `auto` `1K` Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.	T2I Edit

Output	`size`		`aspect_ratio` + `resolution`
Flexible
Auto	`"auto"`	—	Model chooses optimal dimensions

1K 10 sizes

Output	`size`		`aspect_ratio` + `resolution`
1183 × 887	`"1183x887"`	or	`"4:3"` + `"1K"`
916 × 1145	`"916x1145"`	or	`"4:5"` + `"1K"`
1145 × 916	`"1145x916"`	or	`"5:4"` + `"1K"`
1024 × 1024	`"1024x1024"`	or	`"1:1"` + `"1K"`
887 × 1182	`"887x1182"`	or	`"3:4"` + `"1K"`
836 × 1254	`"836x1254"`	or	`"2:3"` + `"1K"`
1254 × 836	`"1254x836"`	or	`"3:2"` + `"1K"`
768 × 1365	`"768x1365"`	or	`"9:16"` + `"1K"`
1365 × 768	`"1365x768"`	or	`"16:9"` + `"1K"`
1563 × 670	`"1563x670"`	or	`"21:9"` + `"1K"`

How these parameters work

size

Exact pixel dimensions

"1920x1080"

aspect_ratio

Shape only, default scale

"16:9"

resolution

Scale tier, preserves shape

"1K"

Priority when combined

size › aspect_ratio + resolution › aspect_ratio › resolution

size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.

How matching works

Shape matching – we pick the closest supported ratio. Ask for 7:1 on a model with 4:1 and 8:1, you get 8:1.

Scale matching – providers use different tier formats: K tiers (0.5K 1K 2K 4K) or megapixel tiers (0.25 1). If the exact tier isn't available, you get the nearest one.

Dimension clamping – if a model has pixel limits, we clamp dimensions to fit and keep the aspect ratio intact.

Media Inputs

Parameter	Type	Description	Modes
`image`	file	Required. Input image(s) to edit Supports PNG, JPEG, WebP. Up to 3 images per request.	T2I Edit

Output & Format

Parameter	Type	Description	Modes
`response_format`	string	How to return the image `url` `b64_json` Default: `"url"`	T2I Edit
`output_format`	string	Output image format `png` `jpeg` `gif` `webp` `avif` Gateway converts to requested format if provider doesn't support it natively.	T2I Edit
`output_compression`	integer	Compression level for lossy formats (JPEG, WebP, AVIF)	T2I Edit
`n`	integer	Number of images to generate Default: `1` Gateway generates multiple images in parallel even if provider only supports 1.	T2I Edit

Additional Parameters

Provider-specific passthrough fields, available only when the request is routed to the listed provider.

Parameter	Type	Description	Modes
fal
`limit_generations`	boolean	Experimental parameter to limit the number of generations from each round of prompting to 1. Set to `True` to to disregard any instructions in the prompt regarding the number of images to generate.	T2I Edit
`safety_tolerance`	string	The safety tolerance level for content moderation. 1 is the most strict (blocks most content), 6 is the least strict. `1` `2` `3` `4` `5` `6`	T2I Edit
`sync_mode`	boolean	If `True`, the media will be returned as a data URI and the output data won't be available in the request history.	T2I Edit

Parameter Normalization

How we handle parameters across different providers

Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:

Behavior	What happens	Example
`passthrough`	Sent as-is to the provider	style, quality
`renamed`	Same value, mapped to the field name the provider expects	prompt
`converted`	Transformed to the provider's native format	size
`emulated`	Works even if the provider has no concept of it	n, response_format

Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.

Full API Reference Authentication, endpoints, and more

Nano Banana Benchmarks

Nano Banana (Gemini 2.5 Flash Image) holds an Elo of 1251 in text-to-image generation, ranking 11th overall. It shows significant strength in image editing tasks, achieving an Elo of 1227 and securing the #5 position globally.

Lumenfall Arena

#17

Text-to-Image

1244 Elo

Lumenfall Arena

Image Editing

1215 Elo

Image Editing Landscape

Elo vs Cost

1 model without pricing omitted

Elo vs Speed

6 models waiting for enough speed data

Competition Results

Image Editing

Photorealism

View leaderboard

Nano Banana edited result for Bald man challenge

Original image before Nano Banana editing

Before After

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Man and Car in California

13 models

Image Editing

Source

Edit instruction

“Make a photo of the man driving the car down the California coastline”

Nano Banana edited result for Golden Hour Stroll

Before After

Golden Hour Stroll

13 models

Image Editing

Edit instruction

“Add dynamic motion to this photo: make hair blow in the wind, add leaves flying, energetic and lively feel.”

Nano Banana edited result for Neutral Expression to Genuine Smile

Before After

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

3 attempts – showing best result

Nano Banana edited result for Night Sky Transformation

Before After

#12

Night Sky Transformation

16 models

Image Editing

Edit instruction

“Change the scene to night: a deep, dark sky with subtle, glistening stars visible behind the mountain.”

3 attempts – showing best result

Image Editing

Anime

View leaderboard

#14

Studio Ghibli Anime Style

14 models

Image Editing

Source

Edit instruction

“Transform this photo into a Studio Ghibli–inspired illustration. Use soft pastel colors, hand-painted textures, gentle lighting, dreamy backgrounds, and a warm, nostalgic mood”

Image Editing

Portrait

View leaderboard

Before After

Bald man challenge

15 models

Image Editing

Edit instruction

“Give the person a full, thick head of natural hair with realistic texture, density, and a natural hairline. Preserve facial features and lighting.”

Before After

Neutral Expression to Genuine Smile

14 models

Image Editing

Edit instruction

{
  "action": "image_edit",
  "reference": "uploaded neutral portrait",
  "change": "Warm genuine Duchenne smile: lips curved up, slight natural teeth, soft eye crinkles, subtle cheek raise",
  "details": "Realistic smiling skin (dimples if present, soft cheek shadows), slightly brighter eyes; keep exact eye shape/color/iris",
  "preserve_exact": "Face identity/structure, eyes/nose/lips/eyebrows, hair, skin texture/pores/freckles, makeup, clothing, head pose, background, lighting, shadows, framing",
  "no_changes": "No face shape change, no new features, no gaze shift, no hair/clothing/lighting/background edits",
  "style": "Ultra-photorealistic 8K portrait, sharp face focus, natural soft lighting, realistic skin glow"
}

3 attempts – showing best result

Uncategorized

#10

Over-the-top cartoon caricature

13 models

Image Editing

Source

Edit instruction

“Create a caricature of me and my job. Make it exaggerated and humorous, incorporating my profession as a tv show anchor and my love for dogs and hockey.”

Top Matchups

See how Nano Banana performs head-to-head against other AI models, ranked by community votes in blind comparisons.

vs Nano Banana Pro

Challenge: Man and Car in California

0% W · 100% L

vs Nano Banana Pro

Challenge: Bald man challenge

25% W · 25% L · 50% T

vs GPT Image 1.5

Challenge: Vintage Cafe Logo

0% W · 100% L

vs ImagineArt 1.5 (Preview)

Challenge: Geometric Composition

33% W · 33% L · 33% T

vs Wan 2.6

Challenge: Golden Hour Stroll

0% W · 67% L · 33% T

Nano Banana is best for

See all Use Cases

This model is a top performer for portraiture and photorealism, where it ranks 1st and 2nd respectively with win rates exceeding 54%. Its effectiveness notably declines in creative categories like anime, where it maintains only a 5.3% win rate.

#1 of 15

Portrait

Edit · 68.2% win rate

#4 of 19

Photorealism

Edit · 56.7% win rate

Gallery

View all 7 images

Nano Banana FAQ

How much does Nano Banana cost?

Nano Banana starts at $0.0387 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.

What can Nano Banana do?

Nano Banana supports Batch and System prompt. It accepts text and image input and produces text and image output.

How do I use Nano Banana via API?

You can use Nano Banana through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "gemini-2.5-flash-image". Code examples are available in Python, JavaScript, and cURL.

Which providers offer Nano Banana?

Nano Banana is available through Replicate, Vertex AI, Gemini API, and fal.ai on Lumenfall. Lumenfall automatically routes requests to the best available provider.

Overview

Gemini 2.5 Flash Image is a multimodal model developed by Google designed for high-velocity image generation and visual reasoning. It functions as an efficient mid-tier option in the Gemini lineup, prioritizing low latency and cost-effectiveness while maintaining the ability to process both text and image inputs. This model is distinctive for its dual-purpose nature, acting as both an image generator and a visual analysis tool within a single architecture.

Strengths

Rapid Iterative Generation: Optimized for speed, the model excels at “flash” generation cycles where low latency is required for real-time applications or high-volume batch processing.
Instruction Following: Strong adherence to system prompts allows for precise control over stylistic constraints and compositional requirements during the image creation process.
Multimodal Reasoning: Unlike pure-play diffusion models, it can ingest existing images as context to perform editing, variations, or descriptive analysis.
Resource Efficiency: Offers a significantly lower price point ($0.039 starting price) compared to larger-parameter models, making it viable for large-scale production deployments.

Limitations

Visual Complexity: While fast, it may lack the intricate fine-detail rendering (such as complex micro-textures or hyper-realistic human anatomy) found in larger, “Pro” tier models.
Compositional Nuance: In very dense scenes with numerous specific spatial requirements, the model may occasionally prioritize speed over exact adherence to complex spatial arrangements.
Niche Stylization: Without specialized LoRA support or fine-tuning, it may struggle with highly specific or avant-garde artistic styles compared to dedicated community-driven generation models.

Technical Background

Released in October 2025, Gemini 2.5 Flash Image is built on the Gemini 2.x transformer-based architecture family. It utilizes a unified multimodal training approach that treats visual tokens and text tokens within the same latent space, enabling seamless transitions between understanding an input image and generating a visual response. The model is specifically tuned for distilled inference, reducing the computational overhead typically associated with large-scale vision-language models.

Best For

This model shines in scenarios requiring high-throughput asset generation, such as e-commerce product background variations, social media content scaling, and rapid prototyping for UI/UX concepts. It is also well-suited for applications that combine image analysis with immediate visual feedback, such as describing a scene and then modifying it based on user feedback.

Nano Banana (Gemini 2.5 Flash Image) is available for testing and deployment through Lumenfall’s unified API and interactive playground, allowing you to integrate its fast generational capabilities into your existing workflows alongside other models in the Gemini family.

Try Nano Banana in Playground

Generate images with custom prompts — no API key needed.

Open Playground