Gemini Model Versions Supported in the Google Skills Repository: Complete 2025 Guide

The Google Skills repository supports 9 Gemini model versions across 4 capability categories—text/chat, image generation, live realtime streaming, and embeddings—with gemini-3.6-flash as the default recommended model for most use cases.

The official Google Skills repository maintains a curated, enterprise-grade list of Gemini model versions designed for the Gemini Enterprise Agent Platform. These models are grouped by capability and represent the only versions guaranteed to be stable, fully-featured, and supported by the Gen AI SDKs used throughout the codebase. This guide covers every supported model, its intended use case, and how to invoke it correctly.

Supported Gemini Model Versions by Capability

The definitive model registry lives in skills/cloud/gemini-api/SKILL.md, which categorizes models by capability and specifies recommended defaults versus additional options available on explicit request.

Text and Chat Models

Model	Role	Context Window	Use Case
gemini-3.6-flash	Recommended default	~1 million tokens	Fast, balanced, multimodal text and chat
gemini-3.5-flash	Additional	~1 million tokens	Older flash model; use when explicitly requested
gemini-3.5-flash-lite	Additional	Standard	High-frequency, lightweight inference
gemini-3.1-pro-preview	Additional	~1 million tokens	Complex reasoning, coding, research tasks

The gemini-3.6-flash model serves as the primary workhorse for most text generation tasks. According to the source documentation, it offers optimal latency-quality tradeoffs for enterprise workloads. The gemini-3.1-pro-preview variant targets specialized scenarios requiring deeper reasoning capabilities.

Image Generation Models

Model	Quality Level	Internal Codename
gemini-3-pro-image	High	"Nano Banana Pro"
gemini-3.1-flash-image	Medium	"Nano Banana 2"
gemini-3.1-flash-lite-image	Fast, lower quality	"Nano Banana 2 Lite"

All three image generation models respond to multimodal prompts combining text with reference images. The gemini-3-pro-image model produces the highest fidelity outputs for production visual content.

Live Realtime API Model

Model	Capability
gemini-live-2.5-flash-native-audio	Streaming generation with native audio support

This specialized model enables real-time conversational interfaces with low-latency audio output. It streams tokens progressively rather than waiting for complete generation.

Embeddings Model

Model	Purpose
gemini-embedding-2	Text embeddings for retrieval and semantic search

The embeddings model generates dense vector representations optimized for RAG (Retrieval-Augmented Generation) pipelines and similarity-based search systems.

Deprecated and Legacy Gemini Model Versions

The skills/cloud/gemini-interactions-api/SKILL.md file explicitly lists deprecated model families that must not be used in new implementations:

• gemini-2.5-*
• gemini-2.0-*
• gemini-1.5-*
• gemini-1.0-*
• gemini-pro

These legacy families remain functional but will be removed from the platform. The Interactions API skill enforces runtime warnings when deprecated models are requested, though the underlying SDK does not block them.

Using Gemini Models with the Gen AI SDK

All supported Gemini model versions are accessed through the Google Gen AI SDK, which operates in Enterprise mode with GOOGLE_GENAI_USE_ENTERPRISE=true. The SDK automatically routes requests based on model name and location settings.

SDK Installation and Configuration

Python:

pip install google-genai

TypeScript/JavaScript:

npm install @google/genai

Go:

go get google.golang.org/genai

Java:

<dependency>
    <groupId>com.google.genai</groupId>
    <artifactId>google-genai</artifactId>
</dependency>

Environment variables (all platforms):

export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT=your-project-id
export GOOGLE_CLOUD_LOCATION=us-central1  # or "global" for automatic routing

Code Examples for Each Supported Model

Text generation with gemini-3.6-flash (Python):

from google import genai

client = genai.Client()
response = client.models.generate_content(
    model="gemini-3.6-flash",
    contents="Explain quantum computing in plain language."
)
print(response.text)

Text generation with gemini-3.6-flash (TypeScript):

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI();
const resp = await ai.models.generateContent({
  model: "gemini-3.6-flash",
  contents: "Explain quantum computing in plain language."
});
console.log(resp.text);

Image generation with gemini-3-pro-image:

from google import genai

client = genai.Client()
resp = client.models.generate_content(
    model="gemini-3-pro-image",
    contents=[
        genai.Image.from_uri("gs://my-bucket/sample.jpg"),
        genai.Text("Create a stylized version of this photo.")
    ]
)
resp.image.save("stylized.png")

Live realtime streaming with gemini-live-2.5-flash-native-audio:

from google import genai
import asyncio

client = genai.Client()
stream = client.live.create(
    model="gemini-live-2.5-flash-native-audio",
    audio=True
)

async def chat():
    await stream.send("Tell me a short story.")
    async for chunk in stream:
        print(chunk.text, end="")

asyncio.run(chat())

Embeddings with gemini-embedding-2:

from google import genai

client = genai.Client()
emb = client.models.generate_content(
    model="gemini-embedding-2",
    contents="Searchable document about machine learning."
).embedding
print(emb[:10])

Regional Deployment and Location Handling

By default, the Gen AI SDK uses location="global", which enables Google to route requests to the nearest available region with capacity. For compliance or latency requirements, specify an explicit region:

client = genai.Client(location="us-central1")

Available regions vary by model and are documented in the Gemini API skill configuration.

Reference Implementation Files

File Path	Purpose
skills/cloud/gemini-api/SKILL.md	Master model registry and SDK guidance
skills/cloud/gemini-interactions-api/SKILL.md	Interactions API with deprecation warnings
skills/cloud/agent-platform-inference/scripts/gemini_genai_sdk.py	Reference Gen AI SDK implementation
skills/cloud/agent-platform-inference/scripts/gemini_vertexai_sdk.py	Legacy Vertex AI SDK example
skills/cloud/agent-platform-inference/scripts/gemini_openai_sdk.py	OpenAI-compatible SDK example

Summary

• 9 Gemini model versions are officially supported across text/chat, image generation, live realtime, and embeddings capabilities
• gemini-3.6-flash serves as the default recommended model for text and multimodal tasks
• Legacy families (gemini-2.5-*, gemini-2.0-*, gemini-1.5-*, gemini-1.0-*) are deprecated and trigger runtime warnings
• All models require the Google Gen AI SDK in Enterprise mode with GOOGLE_GENAI_USE_ENTERPRISE=true
• Default location="global" enables automatic routing, with explicit regions available for specialized requirements
• Model enforcement logic resides in skills/cloud/gemini-interactions-api/SKILL.md, which implements the validation and warning system

Frequently Asked Questions

What is the default Gemini model version in the Google Skills repository?

The default recommended model is gemini-3.6-flash, a fast, balanced multimodal model with approximately 1 million tokens of context window. This is specified as the primary option for text and chat use cases in skills/cloud/gemini-api/SKILL.md.

Can I still use older Gemini models like gemini-1.5-pro?

Deprecated models including gemini-1.5-*, gemini-2.0-*, gemini-2.5-*, and gemini-pro remain technically functional but emit runtime warnings per the Interactions API skill. These families will be removed from the platform and should not be used in new code.

How do I choose between image generation models?

Select gemini-3-pro-image for highest quality outputs ("Nano Banana Pro"), gemini-3.1-flash-image for balanced quality and speed ("Nano Banana 2"), or gemini-3.1-flash-lite-image for fastest generation with acceptable quality tradeoffs ("Nano Banana 2 Lite").

What SDK is required to use these Gemini model versions?

All supported models require the Google Gen AI SDK (google-genai for Python, @google/genai for TypeScript/JavaScript, google.golang.org/genai for Go, com.google.genai:google-genai for Java). The SDK must operate in Enterprise mode with the environment variable GOOGLE_GENAI_USE_ENTERPRISE=true set.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}