nano banana pro

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Nano Banana Pro (gemini-3-pro-image) Model Card #

Overview #

Nano Banana Pro (gemini-3-pro-image) is Google's state-of-the-art visual foundation model designed for professional studio asset production, complex multi-reference character and style consistency, high-precision typography rendering, and interleaved multi-image narrative composition.

  • Model Code: gemini-3-pro-image
  • Release Date: November 2025
  • Status: General Availability (GA)
  • DeepMind Model Card: gemini-3-pro-image ↗

Technical Specifications #

Property Value
Input Modalities Text, Images (up to 14), PDF
Output Modalities Image and Text (Interleaved)
Input Token Limit 65,536 tokens
Output Token Limit 32,768 tokens
Supported Resolutions 1K (1024px), 2K (2048px), 4K (4096px)
Aspect Ratios 10 standard professional ratios
Thinking Mode Enabled by Default (deep visual reasoning prior to synthesis)
Search Grounding Web Search Grounding supported
Multi-Reference Anchors Up to 14 total: up to 6 objects + up to 5 characters + up to 3 style references
Interleaved Output Supported (multi-scene storyboard / illustrated narrative)
Watermarking SynthID (Always On) + C2PA Metadata

Resolution Grid & Token Consumption #

Aspect Ratio 1K Resolution 1K Tokens 2K Resolution 2K Tokens 4K Resolution 4K Tokens
1:1 1024 × 1024 1,120 2048 × 2048 1,120 4096 × 4096 2,000
2:3 848 × 1264 1,120 1696 × 2528 1,120 3392 × 5056 2,000
3:2 1264 × 848 1,120 2528 × 1696 1,120 5056 × 3392 2,000
3:4 896 × 1200 1,120 1792 × 2400 1,120 3584 × 4800 2,000
4:3 1200 × 896 1,120 2400 × 1792 1,120 4800 × 3584 2,000
4:5 928 × 1152 1,120 1856 × 2304 1,120 3712 × 4608 2,000
5:4 1152 × 928 1,120 2304 × 1856 1,120 4608 × 3712 2,000
9:16 768 × 1376 1,120 1536 × 2752 1,120 3072 × 5504 2,000
16:9 1376 × 768 1,120 2752 × 1536 1,120 5504 × 3072 2,000
21:9 1584 × 672 1,120 3168 × 1344 1,120 6336 × 2688 2,000

Capabilities & Key Features #

1. Multi-Reference Character & Style Consistency #

Nano Banana Pro excels at maintaining exact facial, anatomical, and wardrobe consistency across varied poses, while simultaneously adhering to an artistic style defined by separate reference images:

  • Object Anchors (up to 6): Maintain exact physical proportions and logo placements.
  • Character Anchors (up to 5): Preserve identity across 360-degree viewpoints and emotional expressions.
  • Style References (up to 3): Match custom painterly or graphic art styles.

2. Built-in Visual Thinking Reasoning #

Nano Banana Pro runs pre-generation reasoning cycles that evaluate composition, lighting angles, focal depth, and spatial consistency before emitting pixels.

3. Interleaved Storyboard & Multi-Image Generation #

Can produce multi-panel graphic novels, children's book spreads, or technical step-by-step illustrations with matching text in a single generation call.

SDK Code Examples #

1. Studio-Quality 4K Asset Production with Search Grounding #

python 
import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3-pro-image",
    input="An authentic photographic architectural render of the newly constructed Grand Egyptian Museum at sunset, with precise structural accuracy.",
    tools=[{"type": "google_search"}],
    response_format={
        "type": "image",
        "aspect_ratio": "16:9",
        "image_size": "4K",
    },
)

if interaction.output_image:
    with open("gem_museum_4k.png", "wb") as f:
        f.write(base64.b64decode(interaction.output_image.data))

2. Multi-Reference Style & Character Consistency #

python 
import base64
from google import genai

client = genai.Client()

def read_b64(path):
    with open(path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

interaction = client.interactions.create(
    model="gemini-3-pro-image",
    input=[
        {"type": "image", "data": read_b64("references/character_face.png"), "mime_type": "image/png"},
        {"type": "image", "data": read_b64("references/character_body.png"), "mime_type": "image/png"},
        {"type": "image", "data": read_b64("references/art_style.png"), "mime_type": "image/png"},
        {
            "type": "text",
            "text": "Render the character from images 1 and 2 wearing a leather flight jacket and aviator goggles, standing beside a vintage plane. Recreate the entire scene in the exact watercolor and ink style of image 3.",
        },
    ],
    response_format={"type": "image", "aspect_ratio": "3:2", "image_size": "2K"},
)

if interaction.output_image:
    with open("character_rendered.png", "wb") as f:
        f.write(base64.b64decode(interaction.output_image.data))