nano banana 2

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Nano Banana 2 (gemini-3.1-flash-image) Model Card #

Overview #

Nano Banana 2 (gemini-3.1-flash-image) is Google's flagship multimodal creative workhorse model. It delivers the optimal balance of speed, intelligence, high-fidelity visual composition, and 4K output generation, featuring native web search grounding, image search grounding, and video context understanding.

  • Model Code: gemini-3.1-flash-image
  • Release Date: February 2026
  • Status: General Availability (GA)
  • DeepMind Model Card: gemini-3-1-flash-image ↗

Technical Specifications #

Property Value
Input Modalities Text, Images (up to 14), Video (YouTube URLs, MP4s), PDF
Output Modalities Image and Text (Interleaved)
Input Token Limit 131,072 tokens
Output Token Limit 32,768 tokens
Supported Resolutions 512px (0.5K), 1K (1024px), 2K (2048px), 4K (4096px)
Aspect Ratios 14 discrete ratios (incl. extreme wide/tall 1:4, 4:1, 1:8, 8:1)
Thinking Process Supported (minimal [default], high)
Search Grounding Web Search Grounding + Google Image Search Grounding
Reference Anchors Up to 10 object anchors + up to 4 character resemblance anchors
Watermarking SynthID (Always On) + C2PA Metadata

Complete Resolution Grid & Token Consumption #

Aspect Ratio 512px (0.5K) Tokens 1K Resolution Tokens 2K Resolution Tokens 4K Resolution Tokens
1:1 512 × 512 747 1024 × 1024 1,120 2048 × 2048 1,680 4096 × 4096 2,520
1:4 256 × 1024 747 512 × 2048 1,120 1024 × 4096 1,680 2048 × 8192 2,520
1:8 192 × 1536 747 384 × 3072 1,120 768 × 6144 1,680 1536 × 12288 2,520
2:3 424 × 632 747 848 × 1264 1,120 1696 × 2528 1,680 3392 × 5056 2,520
3:2 632 × 424 747 1264 × 848 1,120 2528 × 1696 1,680 5056 × 3392 2,520
3:4 448 × 600 747 896 × 1200 1,120 1792 × 2400 1,680 3584 × 4800 2,520
4:1 1024 × 256 747 2048 × 512 1,120 4096 × 1024 1,680 8192 × 2048 2,520
4:3 600 × 448 747 1200 × 896 1,120 2400 × 1792 1,680 4800 × 3584 2,520
4:5 464 × 576 747 928 × 1152 1,120 1856 × 2304 1,680 3712 × 4608 2,520
5:4 576 × 464 747 1152 × 928 1,120 2304 × 1856 1,680 4608 × 3712 2,520
8:1 1536 × 192 747 3072 × 384 1,120 6144 × 768 1,680 12288 × 1536 2,520
9:16 384 × 688 747 768 × 1376 1,120 1536 × 2752 1,680 3072 × 5504 2,520
16:9 688 × 384 747 1376 × 768 1,120 2752 × 1536 1,680 5504 × 3072 2,520
21:9 792 × 168 747 1584 × 672 1,120 3168 × 1344 1,680 6336 × 2688 2,520

Capabilities & Key Features #

1. Google Search & Image Search Grounding #

Nano Banana 2 can query Google Search (both Web and Image Search) in real time to generate accurate images based on current events, sports scores, scientific diagrams, and real-world visual references.

python 
interaction = client.interactions.create(
    model="gemini-3.1-flash-image",
    input="An infographic summarizing yesterday's European championship match highlights",
    tools=[{"type": "google_search", "search_types": ["web_search", "image_search"]}],
    response_format={"type": "image", "aspect_ratio": "16:9", "image_size": "2K"},
)

2. Video-to-Image Generation #

Pass YouTube URLs or local video files via the Files API to generate context-matched posters, scene captures, or thematic thumbnails:

python 
interaction = client.interactions.create(
    model="gemini-3.1-flash-image",
    input=[
        {"type": "text", "text": "Generate a dramatic cinematic movie poster inspired by this trailer video"},
        {"type": "video_url", "url": "https://www.youtube.com/watch?v=EXAMPLE_VIDEO_ID"},
    ],
    response_format={"type": "image", "aspect_ratio": "2:3", "image_size": "4K"},
)

3. Controllable Thinking Process #

Control reasoning depth with thinking_level:

  • "minimal": Low latency, standard generation.
  • "high": Enhanced reasoning through composition, spatial arrangement, and complex lighting before pixel synthesis.

SDK Code Examples #

1. Python SDK (google-genai) - 4K High-Resolution Generation #

python 
import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.1-flash-image",
    input="A cinematic wide-angle shot of an ancient observatory on a mountain peak under the Aurora Borealis",
    generation_config={"thinking_level": "high"},
    response_format={
        "type": "image",
        "aspect_ratio": "16:9",
        "image_size": "4K",
    },
)

if interaction.output_image:
    with open("observatory_4k.png", "wb") as f:
        f.write(base64.b64decode(interaction.output_image.data))

2. Multi-Reference Character & Object Editing #

python 
import base64
from google import genai

client = genai.Client()

with open("character_ref.png", "rb") as f:
    char_bytes = f.read()

with open("suit_ref.png", "rb") as f:
    suit_bytes = f.read()

interaction = client.interactions.create(
    model="gemini-3.1-flash-image",
    input=[
        {"type": "image", "data": base64.b64encode(char_bytes).decode("utf-8"), "mime_type": "image/png"},
        {"type": "image", "data": base64.b64encode(suit_bytes).decode("utf-8"), "mime_type": "image/png"},
        {"type": "text", "text": "Place the character from the first image into the cyberpunk armor from the second image, standing in a rain-soaked futuristic street."},
    ],
    response_format={"type": "image", "aspect_ratio": "16:9", "image_size": "2K"},
)

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