Does Nano Banana Pro Have Thinking Mode? 3 Steps to Enable AI Image Generation Reasoning Visualization

In AI image generation application development, developers often face the "black box problem" – unable to understand how the model conceives and generates images. This problem is particularly prominent when handling complex creative tasks, directly affecting the controllability of generation quality and debugging efficiency. Nano Banana Pro (i.e., Google's official gemini-3-pro-image-preview model) has recently launched the thinking process feature, making the reasoning process of AI image generation transparent and visible through the include_thoughts parameter.

In our communication with customers, we found that Nano Banana Pro, for better output results, after enabling the include_thoughts parameter, the model will use its knowledge base for deep thinking and reasoning, thereby generating images that better meet expectations and have higher quality. This article will detail how to enable and use Nano Banana Pro thinking mode to help you build more controllable and higher-quality AI image generation applications.

Technical Principles of Nano Banana Pro Image Generation Thinking Mode

Thinking Mode is an advanced feature of Nano Banana Pro (i.e., the gemini-3-pro-image-preview model), implemented based on Chain-of-Thought (CoT) reasoning technology. When this mode is enabled, the model will output its internal reasoning steps, design ideas, and creative conception process before generating the final image.

Core Technical Features

This feature is built on the generation mechanism of large multimodal models. Traditional image generation APIs only return the final image, while thinking mode allows the model to explicitly output creative reasoning steps during image generation through special prompt engineering and output structure design.

At the technical implementation level, Nano Banana Pro uses specially trained thinking tokens, which trigger the model to enter an "explicit creative reasoning" state. In this state, the model will:

1. Understand Requirements: Deeply parse the core intent of image generation prompts
2. Conceive Creativity: Build visual elements and composition schemes based on knowledge base
3. Evaluate Solutions: Check the rationality and aesthetic value of creative solutions
4. Generate Optimized: Generate high-quality images based on reasoning chain

This mechanism not only improves image generation quality, but more importantly, provides developers with a "visualization window" of the model's creative process, facilitating debugging and prompt optimization.

🎯 Technical Recommendation: When handling image generation tasks that require high-quality output such as product image generation, creative poster design, and e-commerce scene images, we recommend enabling Nano Banana Pro thinking mode through APIYI apiyi.com platform. The platform supports gemini-3-pro-image-preview model, provides unified API interfaces, facilitating quick verification of thinking mode's improvement effect on image generation quality.

3 Methods to Enable Nano Banana Pro Thinking Mode

Method 1: Official Python SDK Call (Recommended)

Using Google's official google-generativeai SDK is the most direct way. The following is a complete Nano Banana Pro image generation code example:

import google.generativeai as genai
from google.generativeai import types

# Configure API Key
genai.configure(api_key="YOUR_API_KEY")

# Create Model Instance (Nano Banana Pro)
model = genai.GenerativeModel('gemini-3-pro-image-preview')  # Nano Banana Pro model

# Build Image Generation Request Configuration
prompt = "A modern minimalist e-commerce product display scene, white background, soft natural light, product centered"
aspect_ratio = "16:9"  # Image aspect ratio

response = model.models.generate_content(
    contents=prompt,
    config=types.GenerateContentConfig(
        response_modalities=['Text', 'Image'],
        image_config=types.ImageConfig(
            aspect_ratio=aspect_ratio,
        ),
        # Key Configuration: Enable Thinking Mode
        thinking_config=types.ThinkingConfig(
            include_thoughts=True  # Enable Thinking Process Output
        )
    )
)

# Parse Response Content
for part in response.parts:
    if part.thought:
        print(f"Thinking Process: {part.text}")
    elif image:= part.as_image():
        image.save("generated_image.png")
        print("Image saved")

Key Parameter Description:

• gemini-3-pro-image-preview: Official model ID of Nano Banana Pro
• thinking_config: Thinking configuration object
• include_thoughts=True: Core switch, set to True to enable thinking mode
• part.thought: Determine whether it is thinking process or generated image by checking the thought attribute of response

Method 2: Direct REST API Call

If your project does not use Python, you can directly call Nano Banana Pro API through HTTP requests:

curl -X POST \
  https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: YOUR_API_KEY" \
  -d '{
    "contents": [{
      "parts": [{"text": "Generate a tech-savvy product launch main visual poster"}]
    }],
    "generationConfig": {
      "responseModalities": ["Text", "Image"],
      "imageConfig": {
        "aspectRatio": "16:9"
      }
    },
    "thinkingConfig": {
      "includeThoughts": true
    }
  }'

In the returned JSON structure, the parts array will contain multiple elements:

• Elements marked with "thought": true are thinking processes
• Elements without this mark are generated image data

💡 Selection Recommendation: If your project needs to test the effects of multiple AI image generation models simultaneously (such as comparing generation quality of Nano Banana Pro and DALL-E 3), we recommend unified calling through APIYI apiyi.com platform. The platform supports multiple mainstream image generation models, provides standardized response formats, facilitating quick comparison and switching.

Method 3: Call Through APIYI Platform

APIYI platform has encapsulated and optimized Nano Banana Pro, providing a more convenient calling method:

import requests

url = "https://api.apiyi.com/v1/images/generations"
headers = {
    "Authorization": "Bearer YOUR_APIYI_TOKEN",
    "Content-Type": "application/json"
}

payload = {
    "model": "gemini-3-pro-image-preview",  # Nano Banana Pro
    "prompt": "Generate an e-commerce homepage banner, theme is summer promotion, fresh blue-green tone, with product display",
    "aspect_ratio": "16:9",
    "thinking_enabled": True,  # APIYI platform simplified parameter, enable thinking mode
    "n": 1
}

response = requests.post(url, json=payload, headers=headers)
result = response.json()

# APIYI returned standard format
print("Thinking Process:", result['thinking_process'])
print("Image URL:", result['data'][0]['url'])

Advantage Comparison:

• Unified Interface: No need to learn different parameter naming conventions of various models
• Automatic Parsing: Platform automatically separates thinking process and generated images
• Cost Optimized: Provides more flexible billing methods, image generation billed per image
• Quick Switching: Supports one-click switching between different image generation models

🚀 Quick Start: Recommended to use APIYI apiyi.com platform to quickly experience Nano Banana Pro thinking mode. The platform provides out-of-the-box image generation API interfaces, no complex configuration needed, can complete integration in 5 minutes, and supports online debugging tools to view thinking process and generation effects in real-time.

Core Image Generation Scenarios of Nano Banana Pro Thinking Mode

Scenario 1: E-commerce Product Image Generation and Optimization

When handling e-commerce product display image generation, thinking mode can significantly improve image quality and commercial appeal. For example, generating product main images:

Input Prompt: "Generate a product main image for a high-end Bluetooth headset, requirements: pure white background, product placed at 45-degree angle, soft shadow, highlight metal texture, modern minimalist style, suitable for e-commerce platforms"

Output with Thinking Mode Enabled:

[Thinking Process]
1. Product Positioning Analysis:
   - Target: High-end Bluetooth headset
   - Style: Modern minimalist
   - Purpose: E-commerce main image
   - Key Selling Points: Metal texture, design sense

2. Composition Scheme Design:
   - Placement Angle: 45-degree tilt to show product three-dimensionality
   - Lighting Design: Soft top light + side fill light
   - Background Selection: Pure white (#FFFFFF) to enhance product feel
   - Shadow Treatment: Light gray soft shadow to enhance realism

3. Visual Element Optimization:
   - Metal Surface: Highlight reflection + fine texture
   - Color Balance: Maintain product original color, avoid distortion
   - Detail Display: Highlight charging port, logo and other details

4. E-commerce Adaptation Check:
   - Resolution: 2000×2000 px (meets mainstream platforms)
   - White Space: Reserve 10% safety margin on all sides
   - Visual Focus: Product occupies 60-70% of the frame

[Generated Image]
(Model generates final product image based on above reasoning)

Through thinking process, you can clearly see the model's creative reasoning steps, understand the design logic of image generation, and facilitate adjusting prompts to optimize results.

Scenario 2: Creative Poster and Banner Design

In creative poster generation tasks, thinking mode can display design ideas and creative concepts:

Input Prompt: "Design a main visual poster for a tech company annual meeting, theme 'Intelligence Creates Future', requirements: reflect sense of technology and future, color tone mainly blue-purple, include abstract AI elements, 16:9 horizontal format"

Thinking Process Output:

[Thinking Process]
1. Theme Interpretation and Creative Direction:
   - Core Concept: "Intelligence Creates Future" = Intelligent Creation + Future Vision
   - Emotional Conveyance: Technology, Innovation, Forward-looking
   - Target Audience: Tech professionals, relatively young
   - Usage Scenario: Annual meeting venue large screen display

2. Visual Style Definition:
   - Color Scheme: Deep blue gradient to purple (#1E3A8A → #7C3AED)
   - Design Style: Cyberpunk + Minimalism
   - Element Selection: Abstract neural network, light effects, geometric shapes
   - Typography Layout: Title centered upper, balanced white space

3. Core Element Composition:
   - Background Layer: Dark gradient + dotted particle light effects
   - Main Layer: Abstract AI neural network visualization
   - Text Layer: "Intelligence Creates Future" large title + annual meeting information
   - Decoration Layer: Halos, lines, tech textures

4. Detail Optimization Check:
   - Readability: Ensure text is clearly readable on complex background
   - Layering: Foreground, middle ground, background clearly distinguished
   - Aspect Ratio Adaptation: 16:9 horizontal format, suitable for projection and online sharing

[Generated Image]
(Model generates final poster design based on creative reasoning)

This visualized creative reasoning process is extremely valuable for design review and prompt optimization, helping designers understand AI's design logic.

💰 Cost Optimization: For projects that need to generate large amounts of marketing materials and product images, you can consider calling Nano Banana Pro through APIYI apiyi.com platform. The platform provides flexible billing methods and batch calling discounts, suitable for e-commerce, design teams, and marketing agencies to quickly iterate creative solutions.

Scenario 3: Image Editing and Style Transfer

In image editing and style optimization tasks, thinking mode can display modification strategies and aesthetic judgments:

Input Prompt: "Change the uploaded product image to Japanese minimalist fresh style, keep product subject unchanged, adjust background to light pink gradient, add soft natural light effects, suitable for social media sharing"

Thinking Process Example:

[Thinking Process]
1. Original Image Analysis and Target Identification:
   - Current Style: Commercial photography style, white background
   - Target Style: Japanese minimalist fresh, warm and soft
   - Keep Unchanged: Product subject form, color, details
   - Need Adjustment: Background color tone, lighting atmosphere

2. Color Scheme Design:
   - Background Color: Light pink gradient (#FFE4E1 → #FFF0F5)
   - Light Effect Color Temperature: Warm tone (color temperature 5500K)
   - Saturation: Reduce 15% to create softness
   - Brightness: Overall increase 10% to enhance freshness

3. Lighting Effect Optimization:
   - Main Light Source: 45-degree upper side soft light
   - Fill Light: Background light pink ambient light
   - Shadow Treatment: Soften edges, reduce contrast
   - Highlight Points: Add natural reflection on product surface

4. Style Adaptation Check:
   - Japanese Characteristics: Soft, warm, dreamy ✓
   - Product Fidelity: Subject clear, details complete ✓
   - Social Media Adaptation: Colorful but not distorted ✓

[Generated Image]
(Model generates style-transferred image based on editing strategy)

Best Practices and Performance Optimization of Nano Banana Pro Thinking Mode

Configuration Parameter Tuning Recommendations

After enabling thinking mode, some parameters need to be adjusted accordingly to achieve optimal image generation results:

1. Prompt Length Optimization

• Thinking process requires more detailed prompt information
• Recommended prompt length: 50-200 characters (including scene, style, detail description)
• Too short prompts will limit thinking depth

2. aspect_ratio Ratio Selection

• Product Images: Recommend 1:1 or 4:5 (suitable for e-commerce platforms)
• Poster Design: Recommend 16:9 or 9:16 (horizontal/vertical)
• Social Media: Recommend 4:5 or 1:1 (Instagram, Xiaohongshu friendly)

3. Timeout Setting

• Thinking mode + image generation response time is about 30-60 seconds
• Recommend setting timeout ≥ 90 seconds (including thinking reasoning time)

🎯 Technical Recommendation: When deploying Nano Banana Pro thinking mode in production environment, it is recommended to first conduct small-scale testing through APIYI apiyi.com platform, evaluate the impact of different prompt and parameter combinations on image generation quality and cost. The platform provides detailed call statistics and image quality analysis tools to help you find optimal configuration.

Cost Control Strategy

Thinking mode will increase generation time and cost, requiring reasonable control:

Enable by Scenario:

• High-value Commercial Images: Always enable (product main images, marketing posters)
• Batch Quick Generation: Do not enable (large amounts of simple materials)
• Creative Exploration Stage: Enable (test different style directions)
• Production Environment: Conditionally enable based on budget

Tiered Processing Strategy:

def should_enable_thinking(image_purpose, budget_level):
    """Determine whether to enable thinking mode based on image purpose and budget"""
    high_value_purposes = ['product_main', 'hero_banner', 'key_visual']

    if image_purpose in high_value_purposes:
        return True  # High-value images always enabled
    elif budget_level == 'premium':
        return True  # High-budget projects enabled throughout
    elif budget_level == 'standard':
        return random.random() < 0.5  # Standard budget 50% enabled
    else:
        return False  # Low budget not enabled

Cache Generation Results:

• For repetitive image needs, cache generated high-quality images
• Build material library to avoid regenerating similar content

💡 Selection Recommendation: If you need to frequently switch the thinking mode on/off state, APIYI apiyi.com platform provides dynamic configuration functionality, supporting control of whether to enable thinking mode for each call through parameters, allowing flexible strategy adjustment without code modification.

Output Parsing and Error Handling

Correctly parse the response structure of thinking process and images:

import time
from PIL import Image
import io

def parse_thinking_image_response(response):
    """Parse response containing thinking process and images"""
    thoughts = []
    generated_images = []

    for part in response.parts:
        if hasattr(part, 'thought') and part.thought:
            # Parse thinking process
            thoughts.append({
                'content': part.text,
                'timestamp': time.time()
            })
        elif hasattr(part, 'inline_data'):
            # Parse generated image
            image_data = part.inline_data.data
            image = Image.open(io.BytesIO(image_data))
            generated_images.append(image)

    return {
        'thinking_process': thoughts,
        'images': generated_images,
        'total_thinking_steps': len(thoughts)
    }

# Error Handling
try:
    response = model.models.generate_content(
        prompt,
        config=types.GenerateContentConfig(
            thinking_config=types.ThinkingConfig(include_thoughts=True)
        )
    )
    result = parse_thinking_image_response(response)

    # Save image
    if result['images']:
        result['images'][0].save("generated_with_thinking.png")
except Exception as e:
    print(f"Generation Failed: {e}")
    # Fallback: Retry with thinking mode disabled
    response = model.models.generate_content(prompt)
    result = {'images': [response.as_image()], 'thinking_process': None}

Common Questions and Answers About Nano Banana Pro Thinking Mode

Question 1: Does Thinking Mode Support All Gemini Image Models?

Answer: Thinking mode currently only supports gemini-3-pro-image-preview (Nano Banana Pro) model. Although Gemini 1.5 Pro/Flash text generation supports thinking mode, it does not support image generation. Before use, be sure to confirm the model ID is gemini-3-pro-image-preview. If you need to test the effects of different image generation models, you can use APIYI apiyi.com platform's model comparison tool, which supports calling multiple image generation models simultaneously and displaying generation quality differences.

Question 2: How Much Will Thinking Process Increase Costs?

Answer: After enabling thinking mode, image generation costs typically increase by 20%-40%, mainly from additional thinking reasoning calculations. Specific costs depend on prompt complexity and thinking depth. It is recommended to evaluate actual consumption through APIYI platform's cost estimation tool during development stage and set reasonable budget limits.

💰 Cost Optimization: APIYI apiyi.com platform provides batch discounts for calls with thinking mode enabled, and supports setting token usage alerts to help you better control costs.

Question 3: How to Judge the Quality of Thinking Process?

Answer: Key indicators for evaluating Nano Banana Pro thinking process quality include:

• Design Logic: Whether creative reasoning steps are reasonable and conform to aesthetic principles
• Requirement Understanding: Whether the core intent of the prompt is accurately understood
• Detail Completeness: Whether key elements such as lighting, color, composition are considered
• Result Matching: Whether generated images match the description in thinking process

You can quantitatively evaluate the value of this feature by comparing image generation quality differences between "enabling thinking mode" and "not enabling".

Question 4: Will Thinking Mode Affect Response Speed?

Answer: There will be some impact. After enabling thinking mode, the model needs to perform additional creative reasoning, and response time typically increases by 30%-50% (approximately 15-25 seconds increase). For real-time interactive image generation applications (such as online design tools), it is recommended to enable in high-value scenarios; for batch offline generation tasks, this delay is usually acceptable and can significantly improve image quality.

🚀 Quick Start: If your project has high requirements for image generation speed, APIYI apiyi.com platform supports concurrent calls and intelligent load balancing, which can significantly improve batch generation efficiency. The platform also provides image quality scoring tools to help you find the best balance between speed and quality.

Summary and Technical Outlook

Nano Banana Pro (gemini-3-pro-image-preview) thinking mode, through simple configuration of the include_thoughts=True parameter, opens the "black box" of AI image generation creative reasoning for developers, significantly improving image generation quality and controllability. This feature demonstrates great value in scenarios such as product image generation, creative design, and image editing.

Core Points Review:

1. Enable image generation thinking mode through ThinkingConfig configuration object
2. Distinguish between thought (thinking process) and generated images when parsing responses
3. Dynamically control whether to enable based on image purpose and budget
4. Reasonably set prompt detail level and aspect_ratio parameter
5. Monitor generation costs and implement scenario-based optimization strategies

As Google continues to optimize the Nano Banana Pro model, thinking mode functionality is expected to support:

• Multi-Solution Generation: Display multiple creative paths and generate corresponding images for selection
• Aesthetic Scoring: Provide aesthetic value assessment for each thinking step
• Interactive Optimization: Allow developers to adjust creative direction during reasoning process

🎯 Technical Recommendation: It is recommended to include Nano Banana Pro thinking mode in your image generation toolbox, especially in scenarios requiring high-quality visual content such as e-commerce, marketing, and design. You can quickly integrate this feature through APIYI apiyi.com platform. The platform provides complete calling examples, image quality assessment tools, and technical support to help you get started quickly and run stably in production environment.

Enable Nano Banana Pro thinking mode to make the creative process of AI image generation transparent and visible, building higher-quality and more controllable intelligent image generation applications!