Browse models from Google
Gemini 2.5 Flash Image (also known as "Nano Banana") is now generally available. It is a cutting-edge image generation model with context understanding that supports image generation, editing, and multi-turn conversations.
Google: Nano Banana 2 (Gemini 3.1 Flash Image) is designed for speed and efficiency, enabling fast, interactive responses and high throughput.
Google: Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image) is Google's fastest and most cost-effective Gemini image model, built for high-frequency development pipelines and rapid visual exploration. It can complete text-to-image generation in about 5 seconds, which is about 2.7 times faster than Gemini 3.1 Flash Image, while maintaining the character consistency, precise editing capabilities and real-world knowledge of the Nano Banana series. A single embedded API handles text-to-image, image editing, and multi-image composition. As a multi-modal model, it can also generate text while returning images. Output resolution is 1K, supports 14 aspect ratios, and comes with an invisible SynthID watermark for easy identification as AI-generated. The best balance of quality and speed in the Nano Banana 2 series, it generates thousands of images at a fraction of the cost of heavyweight production models, making it ideal for prototyping, real-time applications and large-scale visual workflows.
Gemini 3.1 Pro is the next generation in the Gemini series of models, a suite of highly-capable, natively multimodal, reasoning models. Gemini 3 Pro is now Google’s most advanced model for complex tasks, and can comprehend vast datasets, challenging problems from different information sources, including text, audio, images, video, and entire code repositories
Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution loops, supporting text, image, video, audio, and PDF inputs.
Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.
Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Google: Nano Banana Pro (Gemini 3 Pro Image) is an AI image generation model based on Google Gemini 3 Pro, and is a major upgrade of its previous products. It aims to go beyond simple pattern matching, turn to a more rationally driven system, and make improvements in terms of physical understanding, text rendering, and image consistency. Its main features include faster processing speed, native 2K resolution, and the ability to edit existing images with higher precision, aiming to provide more reliable, professional-level generated results.
Google: Gemini 4 Argon is a cutting-edge model released by Google DeepMind on September 30, 2026. It is the first model in the Gemini 4 series to feature a new naming scheme. It is strong in programming, financial research, legal drafting, and autonomously fixing cybersecurity vulnerabilities. The model supports text, image, video, and file inputs, and has a context window of 1 million tokens. In benchmark tests, its DeepSWE v1.1 score was 77.9%, ahead of Claude Opus 5.5 and GPT-6 Astra, and ranked first in the Vals Index with 68.90%.
Google: Nano Banana 2.1 (Gemini Nano Banana 2.1) is an upgraded multi-modal inference image model designed for production-level image generation and editing. It features greater prompt following capabilities, multi-reference editing capabilities, and higher visual fidelity. The model understands text, image, video, and PDF inputs, supports alternate generation of text and images, and can reason about complex spatial and compositional instructions before generating images. Additionally, the model supports basic validation through Google Search and Google Image Search, enabling better long-tail entity recognition, visual understanding, and output accuracy.