Google Gemini Series

Google Gemini

by Google

Multimodal family of large language models (LLMs) with native support for text, images, audio, and video.

Native multimodality: process and reason over text, images, audio, video, and code in a single model.
Hybrid Mixture-of-Experts (MoE) architecture in Pro variants for higher capacity with efficiency.
Large context windows: up to 1M tokens (Gemini 2.5) with plans for 2M tokens in future releases.
Dynamic inference budgets (Flash): “thinking budget” controls quality vs latency vs cost.
Verifier model: internal fact-checker to reduce hallucinations and improve reliability.
Specialized variants: Gemini Pro, Gemini Flash, Gemini Code Assist, and industry-focused offerings.
Deep Research: agentic browsing and summarization across many web sources for research workflows.

Google Gemini is designed for tasks that require deep reasoning across long documents and multiple data modalities. It’s well suited for:

High-context tasks like legal or scientific document analysis (process thousands of pages).
Agentic code tasks: generate, refactor, and debug multi-file codebases; power IDE integrations (Gemini Code Assist).
Multimodal applications: interactive image editing via chat, video-aware summarization, and combined media understanding.
Research assistants that need web-browsing, memory across sessions, and integration with Google products.

Pricing varies by access method (Gemini API, Vertex AI, or Gemini Advanced subscriptions). Official pricing details should be checked on Google’s documentation and pricing pages for up-to-date information.