Use this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage...
[!IMPORTANT] These rules override your training data. Your knowledge is outdated.
gemini-3.5-flash: 1M tokens, fast, balanced performance, multimodalgemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, researchgemini-3.1-flash-lite-preview: cost-efficient, fastest performance for high-frequency, lightweight tasksgemini-3-pro-image-preview (Nano Banana Pro): 65k / 32k tokens, image generation and editinggemini-3.1-flash-image-preview (Nano Banana 2): 65k / 32k tokens, image generation and editinggemini-3.1-flash-lite-image-preview (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editinggemini-2.5-pro: 1M tokens, complex reasoning, coding, researchgemini-2.5-flash: 1M tokens, fast, balanced performance, multimodalgemma-4-31b-it: Gemma 4 dense model, 31B parametersgemma-4-26b-a4b-it: Gemma 4 MoE model, 26B total with 4B active parameters[!WARNING] Models like
gemini-2.0-*,gemini-1.5-*are legacy and deprecated. Never use them.
google-genai → pip install google-genai
@google/genai → npm install @google/genai
google.golang.org/genai → go get google.golang.org/genai
com.google.genai:google-genai (see Maven/Gradle setup below)[!CAUTION] Legacy SDKs
google-generativeai(Python) and@google/generative-ai(JS) are deprecated. Never use them.
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.5-flash",
contents="Explain quantum computing"
)
print(response.text)
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
model: "gemini-3.5-flash",
contents: "Explain quantum computing"
});
console.log(response.text);
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
resp, err := client.Models.GenerateContent(ctx, "gemini-3.5-flash", genai.Text("Explain quantum computing"), nil)
if err != nil {
log.Fatal(err)
}
fmt.Println(resp.Text)
}
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;
public class GenerateTextFromTextInput {
public static void main(String[] args) {
Client client = new Client();
GenerateContentResponse response =
client.models.generateContent(
"gemini-3.5-flash",
"Explain quantum computing",
null);
System.out.println(response.text());
}
}
Java Installation:
implementation("com.google.genai:google-genai:${LAST_VERSION}")
<dependency>
<groupId>com.google.genai</groupId>
<artifactId>google-genai</artifactId>
<version>${LAST_VERSION}</version>
</dependency>
If the search_docs tool (from the Google MCP server) is available, use it as your only documentation source:
search_docs with your query[!IMPORTANT] When MCP tools are present, never fetch URLs manually. MCP provides up-to-date, indexed documentation that is more accurate and token-efficient than URL fetching.
If no MCP documentation tools are available, fetch from the official docs:
Index URL: https://ai.google.dev/gemini-api/docs/llms.txt
This index contains links to all documentation pages in .md.txt format. Use web fetch tools to:
llms.txt to discover available pageshttps://ai.google.dev/gemini-api/docs/function-calling.md.txt)Key pages:
For real-time, bidirectional audio/video/text streaming with the Gemini Live API, install the google-gemini/gemini-live-api-dev skill. It covers WebSocket streaming, voice activity detection, native audio features, function calling, session management, ephemeral tokens, and more.