Gemini 3 Pro Image Preview API

Gemini 3 Pro Image Preview
Edit

Endpoint: POST https://fal.run/fal-ai/gemini-3-pro-image-preview Endpoint ID: fal-ai/gemini-3-pro-image-preview

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/gemini-3-pro-image-preview",
    arguments={
        "prompt": "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half, revealing both the dogs head above water holding a tennis ball in it's mouth, and it's paws paddling underwater."
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/gemini-3-pro-image-preview", {
  input: {
      prompt: "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half, revealing both the dogs head above water holding a tennis ball in it's mouth, and it's paws paddling underwater."
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/gemini-3-pro-image-preview \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half, revealing both the dogs head above water holding a tennis ball in it'\''s mouth, and it'\''s paws paddling underwater."
}'

Input Schema

string

required

The text prompt to generate an image from.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

integer

The seed for the random number generator.

Enum

default:"1:1"

The aspect ratio of the generated image. Default value: 1:1Possible values: auto, 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16

OutputFormatEnum

default:"png"

The format of the generated image. Default value: "png"Possible values: jpeg, png, webp

SafetyToleranceEnum

default:"4"

The safety tolerance level for content moderation. 1 is the most strict (blocks most content), 6 is the least strict. Default value: "4"Possible values: 1, 2, 3, 4, 5, 6

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

ResolutionEnum

default:"1K"

The resolution of the image to generate. Default value: "1K"Possible values: 1K, 2K, 4K

boolean

default:"false"

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.

boolean

default:"false"

Enable web search for the image generation task. This will allow the model to use the latest information from the web to generate the image.

Output Schema

list<ImageFile>

required

The generated images.

string

required

The description of the generated images.

Input Example

{
  "prompt": "An action shot of a black lab swimming in an inground suburban swimming pool. The camera is placed meticulously on the water line, dividing the image in half, revealing both the dogs head above water holding a tennis ball in it's mouth, and it's paws paddling underwater.",
  "num_images": 1,
  "aspect_ratio": "1:1",
  "output_format": "png",
  "safety_tolerance": "4",
  "sync_mode": false,
  "resolution": "1K",
  "limit_generations": false,
  "enable_web_search": false
}

Output Example

{
  "images": [
    {
      "content_type": "image/png",
      "file_name": "nano-banana-pro-t2i-output.png",
      "url": "https://storage.googleapis.com/falserverless/example_outputs/nano-banana-pro-t2i-output.png"
    }
  ],
  "description": ""
}

Endpoint: POST https://fal.run/fal-ai/gemini-3-pro-image-preview/edit Endpoint ID: fal-ai/gemini-3-pro-image-preview/edit

Try it in the Playground

Run this model interactively with your own prompts.

Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/gemini-3-pro-image-preview/edit",
    arguments={
        "prompt": "make a photo of the man driving the car down the california coastline",
        "image_urls": [
            "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input.png",
            "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input-2.png"
        ]
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/gemini-3-pro-image-preview/edit", {
  input: {
      prompt: "make a photo of the man driving the car down the california coastline",
      image_urls: [
        "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input.png",
        "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input-2.png"
      ]
    },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/fal-ai/gemini-3-pro-image-preview/edit \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "make a photo of the man driving the car down the california coastline",
  "image_urls": [
    "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input.png",
    "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input-2.png"
  ]
}'

Input Schema

string

required

The prompt for image editing.

integer

default:"1"

The number of images to generate. Default value: 1Range: 1 to 4

integer

The seed for the random number generator.

Enum

default:"auto"

The aspect ratio of the generated image. Default value: autoPossible values: auto, 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16

OutputFormatEnum

default:"png"

The format of the generated image. Default value: "png"Possible values: jpeg, png, webp

SafetyToleranceEnum

default:"4"

The safety tolerance level for content moderation. 1 is the most strict (blocks most content), 6 is the least strict. Default value: "4"Possible values: 1, 2, 3, 4, 5, 6

boolean

default:"false"

If True, the media will be returned as a data URI and the output data won’t be available in the request history.

list<string>

required

The URLs of the images to use for image-to-image generation or image editing.

ResolutionEnum

default:"1K"

The resolution of the image to generate. Default value: "1K"Possible values: 1K, 2K, 4K

boolean

default:"false"

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.

boolean

default:"false"

Enable web search for the image generation task. This will allow the model to use the latest information from the web to generate the image.

Output Schema

list<ImageFile>

required

The edited images.

string

required

The description of the generated images.

Input Example

{
  "prompt": "make a photo of the man driving the car down the california coastline",
  "num_images": 1,
  "aspect_ratio": "auto",
  "output_format": "png",
  "safety_tolerance": "4",
  "sync_mode": false,
  "image_urls": [
    "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input.png",
    "https://storage.googleapis.com/falserverless/example_inputs/nano-banana-edit-input-2.png"
  ],
  "resolution": "1K",
  "limit_generations": false,
  "enable_web_search": false
}

Output Example

{
  "images": [
    {
      "content_type": "image/png",
      "file_name": "nano-banana-pro-edit-output.png",
      "url": "https://storage.googleapis.com/falserverless/example_outputs/nano-banana-pro-edit-output.png"
    }
  ],
  "description": ""
}

Google’s Nano Banana Pro (2) Preview delivers state-of-the-art image generation at $0.15 per image, trading cost efficiency for advanced reasoning capabilities and prompt understanding. Built on Gemini 3 Pro’s multimodal architecture, it handles complex natural language instructions that would trip up traditional diffusion models. Use Cases: Complex Scene Composition | Typography-Heavy Designs | Natural Language-Driven Editing

Performance

Nano Banana Pro prioritizes semantic understanding over raw speed, making it ideal for projects where prompt accuracy matters more than generation volume.

Metric	Result	Context
Resolution Range	Up to 4K (4096px)	4K outputs charged at 2x standard rate
Cost per Image	$0.15	6-7 generations per $1.00 on fal
Batch Generation	1-4 images	Per request via `num_images` parameter
Web Search Integration	Optional	Enable via `enable_web_search` for current information

Advanced Prompt Understanding Through Gemini 3 Architecture

Gemini 3 Pro Image leverages Google’s latest foundation model to interpret complex, conversational prompts rather than requiring carefully crafted keyword strings. What this means for you:

Natural Language Processing: Describe scenes conversationally (“meticulously placed camera on the water line, dividing the image in half”) instead of keyword stuffing
Typography Excellence: Tagged for realism and typography, making it particularly strong for text-heavy designs where letter accuracy matters
Flexible Output Control: Generate up to 4 images per request with selectable aspect ratios (21:9 to 9:16) and formats (JPEG, PNG, WebP)
Resolution Scaling: Choose 1K, 2K, or 4K output based on your quality-versus-cost tradeoff, with transparent 2x pricing for 4K

Technical Specifications

Spec	Details
Architecture	Gemini 3 Pro Image
Input Formats	Text prompts with optional web search grounding
Output Formats	JPEG, PNG, WebP
Resolution Options	1K, 2K, 4K (4K at 2x cost)
License	Commercial use via partnership

API Documentation | Quickstart Guide | Enterprise Pricing

How It Stacks Up

Gemini 2.5 Flash Image – Nano Banana Pro trades speed and cost efficiency for advanced reasoning and complex instruction following at 4x the cost (

0.15 vs

0.039). Gemini 2.5 Flash Image remains ideal for high-volume workflows where rapid iteration matters more than nuanced prompt interpretation. **AuraFlow (

0.012)** – Nano Banana Pro prioritizes semantic understanding and typography accuracy at 12.5x the cost (

0.15 vs $0.012). AuraFlow offers maximum cost efficiency for straightforward text-to-image generation where natural language complexity isn’t critical.

Gemini 3 Pro Image Preview — Image Generation

Limitations

num_images range: 1 to 4
output_format restricted to: jpeg, png, webp
safety_tolerance restricted to: 1, 2, 3, 4, 5, 6
resolution restricted to: 1K, 2K, 4K

Video Generation

Image Generation

Audio

Vision

3D

Gemini 3 Pro Image Preview API

Try it in the Playground

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Try it in the Playground

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Performance

Advanced Prompt Understanding Through Gemini 3 Architecture

Technical Specifications

How It Stacks Up

Limitations

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

Try it in the Playground

​Quick Start

​Input Schema

​Output Schema

​Input Example

​Output Example

​Performance

​Advanced Prompt Understanding Through Gemini 3 Architecture

​Technical Specifications

​How It Stacks Up

​Related

​Limitations

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Quick Start

Input Schema

Output Schema

Input Example

Output Example

Performance

Advanced Prompt Understanding Through Gemini 3 Architecture

Technical Specifications

How It Stacks Up

Related

Limitations