Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

google/gemini-3-pro-image-preview

66KContext Window

33KMax Output

Supported Protocols:reasoninginclude_reasoningmax_tokenstemperaturetop_pseedresponse_formatstopstructured_outputs

Normal

Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and high-fidelity visual synthesis. The model generates context-rich graphics, from infographics and diagrams to cinematic composites, and can incorporate real-time information via Search grounding. It offers industry-leading text rendering in images (including long passages and multilingual layouts), consistent multi-image blending, and accurate identity preservation across up to five subjects. Nano Banana Pro adds fine-grained creative controls such as localized edits, lighting and focus adjustments, camera transformations, and support for 2K/4K outputs and flexible aspect ratios. It is designed for professional-grade design, product visualization, storyboarding, and complex multi-element compositions while remaining efficient for general image creation workflows.

Capabilities

👁 Vision🧠 ReasoningText GenerationCode GenerationAnalysis & ReasoningReasoning

Technical Specs

Input Modality

Image、Text

Output Modality

Image、Text

Arch

—

Default Temperature

0.7

Default Top_P

Pricing

Pay per use, no monthly fees

Billing Type	Unit	Price
Text Input	—	$2.0000/M tokens
Text Output	—	$12.0000/M tokens
Cache Read	—	$0.2000/M tokens
Cache Write 1h	—	$0.3750/M tokens
Cache Write	—	$0.3750/M tokens
Reasoning	—	$2.0000/M tokens
Audio Cache Read	—	$0.2000/M tokens
Image Input	—	< $0.001/张
Audio Input	—	$2.0000/分钟
Web Search	—	$0.0140/次

Quick Start

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uniontoken.ai/v1",
    api_key="YOUR_UNIONTOKEN_API_KEY",
)

response = client.chat.completions.create(
    model="google/gemini-3-pro-image-preview",
    messages=[
        {"role": "user", "content": "Hello!"}
    ],
)

print(response.choices[0].message.content)