Multimodal model access
Use Gemini models to generate text and images, analyze images, videos, and documents, and work with long-context inputs. The documentation states that PDF processing can cover files of up to 1,000 pages.
Gemini API
Freemium
Gemini API is a developer API for integrating Google’s generative AI models into applications. It supports text and image generation, multimodal analysis, conversational agents, structured outputs, tool use, and related media workflows through SDKs or REST.
什么是 Gemini API？
Gemini API is Google’s developer interface for using Gemini and related generative models in applications. Developers can send prompts and multimodal inputs to models for text and image generation, document understanding, conversational interactions, and agent workflows. Google AI Studio provides a browser-based environment for evaluating models and developing prompts before integration.
The API is intended to move from model experimentation to application development: users create an API key, choose a model, and call the service through a supported client library or REST. The site presents the Interactions API as the recommended interface and also documents the generateContent API.
Gemini API 能做什么？
Developer APIs and SDKs
Call the service through the Interactions API or generateContent API using client libraries for Python, JavaScript, Java, and Go, or use REST requests.
Structured outputs and function calling
Request JSON responses suitable for automated processing and connect model interactions to external APIs and tools through function calling.
Built-in tools and managed agents
Connect models to Google Search, URL Context, Google Maps, Code Execution, and Computer Use. Managed agents can plan and complete tasks in a hosted sandbox environment.
Specialized generation workflows
The broader model catalog includes image generation and editing with Nano Banana, video generation with Veo, and real-time voice applications with the Live API.
Prompt development and model evaluation
Use Google AI Studio to evaluate models, develop prompts, and turn ideas into code before integrating the API into an application.
使用场景
“Multimodal application features”
Add image, video, document, or PDF understanding to an application when text-only processing is insufficient, such as extracting meaning from user-submitted files.
“Conversational agents”
Build assistants and agentic workflows that maintain interactions, call external tools, and use capabilities such as search or code execution to complete tasks.
“Structured automation”
Generate JSON responses or invoke application functions so model output can feed downstream software rather than requiring manual copying from a chat interface.
“Creative media workflows”
Prototype image generation and editing, video generation, or real-time voice experiences by selecting the corresponding Google model or API.
“Production model integration”
Move an evaluated prompt or prototype from Google AI Studio into an application using an API key, supported SDK, or REST, then choose a pricing tier based on deployment needs.
常见问题
How do developers get started with the Gemini API?
Create an API key, choose a model, and call the API through a supported SDK or REST. Google AI Studio can be used to evaluate models and develop prompts before application integration.
Which programming languages are supported?
The documentation provides client-library examples for Python, JavaScript, Java, and Go. REST is also available.
What types of input and output does the API support?
The platform supports text and multimodal inputs, including images, videos, and documents. Depending on the selected model or API, it can produce text, images, audio or voice interactions, video, and structured JSON responses.
Can Gemini connect to external tools or application functions?
Yes. Function calling connects Gemini to external APIs and tools, while documented built-in tools include Google Search, URL Context, Google Maps, Code Execution, and Computer Use.
Is there a free way to try the API?
Yes. The pricing page lists a free tier with limited access to certain models, free input and output tokens, and Google AI Studio access. Paid tiers provide higher rate limits and additional production features.
快速信息
- Category
- Developer API / Generative AI Platform
- Provider
- Primary users
- Application developers and development teams
- Access methods
- Python, JavaScript, Java, Go, and REST
- Related tools
- Google AI Studio, Live API, Veo, and Nano Banana models
- Pricing model
- Free tier, paid tier, and enterprise option
Gemini API 替代品
OpenAI Platform
platform.openai.com
OpenAI Platform is a developer platform for building applications with the OpenAI API. It provides API access, model and pricing information, technical documentation, starter apps, and cookbooks for implementation guidance.
Google AI Studio
aistudio.google.com
Google AI Studio is a browser-based development environment for experimenting with Google’s generative models and moving prompts into applications through the Gemini API. It supports developers building text, image, video, audio, and agent experiences.
Google Gen AI Python SDK
googleapis.github.io
A Python SDK for integrating Google’s generative models into applications through the Gemini Developer API and Gemini Enterprise Agent Platform APIs. It provides client libraries, typed request helpers, and synchronous or asynchronous workflows for developers building with Google’s generative AI services.
Claude Platform
platform.claude.com
Claude Platform gives developers programmatic access to Claude models and managed agent infrastructure for building agents and applications. It includes a REST API, official client SDKs, a web Console, and documentation for direct integrations.
Cortex Docs
cortexdocs.dev
Cortex Docs is an open-source API tooling workflow that turns API specifications and Markdown into interactive documentation, typed SDKs, and MCP servers. It helps developers publish API interfaces for human users, applications, and AI agents from a shared project.
Instructor
python.useinstructor.com
Instructor is a developer library for extracting structured, validated data from large language models. It uses Pydantic schemas, automatic retries, streaming, and a consistent interface across cloud, local, and routed LLM providers.