Gemini API logo

Gemini API

api

by google

Google's developer API for building with Gemini models programmatically. Multimodal input, 1M+ token context windows, function calling, and generous free tier.

Key features

Multimodal Input
1M+ Token Context Window
Function Calling
Grounding with Google Search
Structured Outputs and JSON Mode
Code Execution
Pricing

From $undefined/undefined

Best For

Developers building AI applications: the Gemini API is the programmatic backbone for integrating Google's models into any software product, from chatbots to data pipelines

Verdict

Industry leading 1M+ token context window across the model family, far exceeding competitors (Claude: 200K, GPT: 128K)

What it does

Multimodal Input

Process text, images, audio, video, and PDFs in a single request. Gemini models natively understand all modalities without separate preprocessing.

Learn more

1M+ Token Context Window

Industry leading context windows of up to 1 million tokens across the model family. Process entire codebases, books, or hours of video in a single prompt.

Learn more

Function Calling

Connect Gemini to external APIs and tools by declaring function schemas. The model generates structured calls that your code executes, enabling agentic workflows.

Learn more

Grounding with Google Search

Ground model responses in real time Google Search results for up to date, factual answers with source citations.

Learn more

Structured Outputs and JSON Mode

Constrain Gemini to respond with structured JSON matching a provided schema. Ideal for automated pipelines and data extraction.

Learn more

Code Execution

Gemini can write and run Python code in a sandboxed environment to solve math, process data, or test logic before responding.

Learn more

Context Caching

Cache large prompts (system instructions, reference documents) and reuse them across requests at reduced cost. Significantly lowers expenses for repetitive workloads.

Learn more

Streaming

Receive partial responses as they are generated for lower perceived latency. Supported via server sent events and WebSocket connections.

Embeddings

Generate high quality vector embeddings for semantic search, clustering, and retrieval augmented generation (RAG) pipelines.

Thinking (Reasoning)

Gemini 2.5 Pro and 2.5 Flash include built in reasoning capabilities with configurable thinking budgets. The model reasons step by step before answering complex questions.

Learn more

Native Image Generation

Gemini 2.5 Flash Image and Gemini 3 Pro Image generate and edit images natively within the model, combining text understanding with visual creation.

Live API (Real Time Audio/Video)

Build real time voice and video agents with bidirectional streaming. Native audio models deliver natural pacing and voice quality.

Computer Use

Gemini 2.5 Computer Use model enables browser control agents that automate tasks by seeing the screen, clicking, and typing.

Batch API

Submit large batches of requests at 50% reduced cost. Ideal for offline processing, data pipelines, and bulk analysis.

Multi Language SDKs

Official SDKs for Python, JavaScript/TypeScript, Go, Java, and C#. Also available via direct REST calls.

Pricing

ModelInput / 1M tokensOutput / 1M tokens
Gemini 3.1 Pro PreviewLatest flagship. $4/$18 for prompts >200K tokens. Batch: 50% off. Preview model.$2.00$12.00
Gemini 3 Flash PreviewFrontier intelligence at Flash speed. Audio input: $1/M. Batch: 50% off.$0.50$3.00
Gemini 2.5 ProThinking model. $2.50/$15 for prompts >200K tokens. Up to 1M context.$1.25$10.00
Gemini 2.5 FlashHybrid reasoning with thinking budgets. Audio input: $1/M. 1M context.$0.30$2.50
Gemini 2.5 Flash LiteMost cost effective. Audio input: $0.30/M. Batch: 50% off.$0.10$0.40
Gemini 2.0 FlashBalanced multimodal workhorse. Audio input: $0.70/M. 1M context.$0.10$0.40
Gemini 2.0 Flash LiteSmallest model, built for scale. Batch: 50% off.$0.07$0.30
Gemini EmbeddingVector embeddings for search and RAG. Batch: $0.075/M.$0.15N/A

Pros & Cons

Pros

  • Industry leading 1M+ token context window across the model family, far exceeding competitors (Claude: 200K, GPT: 128K)
  • Extremely generous free tier with free input and output tokens on most models, making prototyping and learning nearly cost free
  • Aggressive pricing, especially at the Flash Lite tier ($0.075 input, $0.30 output per 1M tokens), among the cheapest production quality models available
  • Native multimodal understanding of text, images, audio, video, and PDFs in a single unified API
  • Official SDKs for five languages (Python, JavaScript, Go, Java, C#) plus REST, with consistent API design across all
  • Grounding with Google Search gives responses access to the most comprehensive search index in the world
  • Batch API provides 50% cost reduction for offline and bulk processing workloads
  • Context caching dramatically reduces costs for repetitive prompts with shared system instructions or reference documents
  • Rapid model evolution: Google ships new models frequently (Gemini 3.1 Pro, 3 Flash, 2.5 Pro, image models, TTS, robotics, computer use)
  • Seamless upgrade path from free tier to pay as you go to enterprise Vertex AI without changing code

Cons

  • Free tier data policy: content on the free tier may be used to improve Google products, which is a concern for sensitive applications
  • Preview model churn: many cutting edge models are labeled "preview" and may change behavior before becoming stable
  • Grounding with Google Search adds significant per query costs ($35/1,000 grounded prompts on most models) on top of token pricing
  • Output quality on the 2.5 Pro thinking model, while strong, trails Claude Opus and GPT o3 on certain nuanced writing and reasoning benchmarks
  • Rate limits on the free tier can be restrictive for production workloads, requiring an upgrade to paid for any serious deployment
  • The sheer number of models and pricing tiers (15+ models with different input/output/audio/caching prices) creates complexity when choosing the right configuration
  • Enterprise features require moving to Vertex AI on Google Cloud, which is a separate platform with its own pricing and learning curve

How to get started

1

Get an API key

Visit Google AI Studio and click 'Get API key' to generate a free API key. No credit card required. The key works immediately on the free tier.

2

Install an SDK

Install the official SDK for your language. Python: pip install google-genai. JavaScript: npm install @google/genai. Go, Java, and C# SDKs are also available.

3

Make your first API call

Set the GEMINI_API_KEY environment variable and run a simple generate_content call. The quickstart guide includes working examples in all supported languages.

4

Explore capabilities

Try multimodal input (send an image with your text), function calling, structured outputs, and streaming. The cookbook repository contains dozens of working examples.

5

Upgrade to paid when ready

When you need higher rate limits, context caching, or the Batch API, upgrade to the paid tier in Google AI Studio. For enterprise needs, move to Vertex AI on Google Cloud.

Deep dive

Detailed guides with comparisons, tips, and visuals for each feature.

Get notified about updates

We'll email you when this tool's pricing or features change.

Last updated: 2026-02-21