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))