Google: Nano Banana 2 (Gemini 3.1 Flash Image Preview)

Generates photorealistic and stylized images from natural language prompts using a diffusion-based architecture with semantic understanding of complex scene compositions, object relationships, and visual styles. The model processes text embeddings through a latent diffusion pipeline optimized for inference speed, enabling high-quality outputs at reduced computational cost compared to prior Gemini generations.

Edits specific regions of existing images by accepting a base image, mask, and text description of desired changes. The model uses a masked diffusion approach where only masked regions are regenerated while preserving unmasked content, enabling seamless content-aware inpainting with semantic understanding of context and style matching.

Transforms an input image based on a text prompt describing desired style, composition, or content changes. The model encodes the input image into latent space, then applies guided diffusion conditioned on both the image embedding and text prompt to produce a transformed output that preserves semantic content while applying stylistic or compositional modifications.

Analyzes images to generate natural language descriptions, extract visual information, and answer questions about image content. The model uses a vision encoder to process image pixels, then generates text through a language decoder conditioned on visual embeddings, enabling detailed scene understanding, object detection, and contextual reasoning about image content.

Processes multiple images sequentially or in parallel through the API, with support for batching requests and managing rate limits. The implementation handles request queuing, error retry logic, and response aggregation, enabling efficient processing of image collections without manual orchestration or timeout management.

Supports iterative prompt refinement through API feedback loops, where users can adjust text prompts and regenerate outputs based on quality assessment. The model maintains semantic understanding across iterations, allowing users to guide generation toward desired results through natural language feedback without retraining or fine-tuning.

Exposes image generation and editing capabilities through REST API and language-specific SDKs (Python, Node.js, etc.), enabling integration into applications and workflows. The implementation provides standardized request/response formats, authentication via API keys, and error handling patterns consistent with Google Cloud and OpenRouter conventions.