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

google/gemini-3-pro-image-preview

Nano Banana Pro is an AI image generation model that is a significant upgrade from its predecessor, built on Google's Gemini 3 Pro. It promises to move beyond simple pattern matching to a more reasoning-driven system with improved physics understanding, text rendering, and image consistency. Key features include faster processing, native 2K resolution, and the ability to edit existing images with greater control, aiming to produce more reliable and professional-grade results.

by [Google](/content/google/index.html)  
Input type: Output type  
Publish time: 2025-11-20

## Recent activity on Nano Banana Pro (Gemini 3 Pro Image Preview)

Tokens processed per day

## Throughput

(tokens/s)

| Providers       | Min (tokens/s) | Max (tokens/s) | Avg (tokens/s) |
| ------------------ | ----------------- | ----------------- | ----------------- |
| Google Vertex | 11.95              | 42.01              | 32.05              |
| SkyRouter         | 29.46              | 29.46              | 29.46              |

## First Token Latency

(ms)

| Providers       | Min (ms) | Max (ms) | Avg (ms) |
| ------------------ | --------- | --------- | --------- |
| Google Vertex | 24148    | 35738    | 30122.68 |
| SkyRouter         | 35906    | 35906    | 35906.00 |

## Uptime stats for Nano Banana Pro (Gemini 3 Pro Image Preview)

Uptime stats for Nano Banana Pro (Gemini 3 Pro Image Preview) across all providers

Uptime

| Providers         | Percent |
| ------------------ | ----------- |
| Google Vertex | 100.00% |
| SkyRouter         | -          |

Recent uptime: Apr 30, 2026 6:00 PM-

## Price

### Tiered Pricing

| Input                    | Price              |
| ------------------------- | ----------------- |
| 0 ≤ Input < 200k | $2 / M tokens |
| Output                   | $12 / M tokens |
| Cache read             | $0.2 / M tokens  |
| Web search            | $0.014 / counts |
| Image                     | $120 / M tokens  |
| reasoning               | $12 / M tokens  |

## Model limitation

| Parameter                | Value        |
| ------------------------- | ------------ |
| Context                 | 65.54K    |
| Max output              | 32.77K    |

## Supported Parameters

| Parameter                | Supported |
| ------------------------- | ---------- |
| max_completion_tokens | -            |
| temperature                | -            |
| top_p                       | -            |
| frequency_penalty      | -            |
| presence_penalty        | -            |
| seed                        | -            |
| logit_bias                 | -            |
| logprobs                | -            |
| top_logprobs         | -            |
| response_format      | -            |
| stop                         | -            |
| tools                       | -            |
| tool_choice             | -            |
| parallel_tool_calls | -            |

## Model Protocol Compatibility

| Protocol                   | Supported   |
| --------------------------- | ------------ |
| OpenAI Chat Completions   | -            |
| OpenAI Responses          | -            |
| OpenAI Embeddings        | -            |
| Anthropic Messages       | -            |
| Google Gemini             | -            |
| Google Imagen              | -            |
| Google Video               | -            |

## Sample code and API for Nano Banana Pro (Gemini 3 Pro Image Preview)

ZenMux normalizes requests and responses across providers for you.

```python
from google import genai
from google.genai import types

client = genai.Client(
    api_key="$ZENMUX_API_KEY",
    vertexai=True,
    http_options=types.HttpOptions(api_version='v1', base_url='https://zenmux.ai/api/vertex-ai')
)

# Text Generation
response = client.models.generate_content(
    model="google/gemini-3-pro-image-preview",
    contents="How does AI work?"
)
print(response.text);

# Image Generation
prompt = "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"

response = client.models.generate_content(
    model="google/gemini-3-pro-image-preview",
    contents=[prompt],
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"]
    )
)

# Handle text and image responses
for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        # Save the generated image
        image = part.as_image()
        image.save("generated_image.png")
        print("Image saved as generated_image.png")
```