GPT Image 1.5 vs Nano Banana 2

1. State-of-the-Art Image Generation

• Produces high-quality, detailed images optimized for realism, style control and prompt fidelity.
• Designed to handle complex visual scenes, compositions and lighting conditions.

2. Natively Multimodal Architecture

• Understands and reasons over both text and images as inputs.
• Ideal for workflows like editing based on reference images, expanding sketches or mockups and visual concept development.

3. Flexible Output Resolutions & Quality Levels

• Supports multiple resolutions including 1024x1024, 1024x1536 and 1536x1024.
• Offers three quality tiers (Low, Medium, High) to balance cost, speed and maximum detail.

4. Multiple Pricing Models

• Pay-per-token for multimodal input: text tokens and image tokens.
• Pay-per-image generation for final output: low, medium and high quality tiers.
• Enables businesses to balance cost and output needs.

5. Broad Use Cases

• Product photography and marketing assets.
• Illustration, concept art and creative ideation.
• UX/UI mockups.
• Style-guided image creation.
• Generating reference images for design or storytelling.

6. Supported Across Major API Endpoints

• Available via Chat Completions, Responses, Realtime, Assistants and Images (generations/edits) endpoints.
• Allows tight integration into automated creative pipelines or user-facing apps.

7. Simplified Model Behavior for Stability

• No streaming, function calling, structured outputs or fine-tuning; focused solely on high-quality image generation.

8. Consistent Results via Snapshots

• Supports snapshots for version locking to ensure long-term reproducibility.

9. Ideal For

• Designers, marketers and creatives.
• Product teams needing image assets.
• App builders integrating image generation workflows.
• Agencies producing visual content at scale.

1. High-efficiency counterpart to Gemini 3 Pro Image

• Google describes Nano Banana 2 as the high-efficiency counterpart to Gemini 3 Pro Image.
• Optimized for speed and high-volume developer use cases rather than maximum pro-grade fidelity.

2. Native image generation + understanding

• Accepts text and image inputs and can output both text and images in a conversational workflow.
• Useful for quick iteration, editing, remixing, and interactive visual applications.

3. Strong throughput with practical image controls

• Supports up to 14 input images per prompt, 128 k input tokens, and 32,768 output tokens.
• Handles multiple aspect ratios and can generate or edit images while keeping latency and cost lower than higher-end image models.

4. Grounded, developer-friendly image workflows

• Supports Google Search grounding and Content Credentials (C2PA) for image outputs.
• All generated images include SynthID watermarking as part of Google's native image stack.