Google Gemini AI SDK vs OpenAI Agents SDK

Overview

This comparison examines two official AI agent SDKs from competing providers: Google Gemini AI SDK and OpenAI Agents SDK. Both are first-party SDKs designed to help developers build AI agents with their respective models.

While both serve similar purposes, they have different strengths reflecting their providers' capabilities and ecosystems.

At a Glance

Aspect	Google Gemini AI SDK	OpenAI Agents SDK
Provider	Google	OpenAI
Primary Language	Python, JS, Go, Java, C#	Python, TypeScript
Flagship Model	Gemini 3.1 Pro	GPT-4o
Unique Features	Deep Research, Computer Use, Multimodal	Structured outputs, Guardrails
Context Window	Up to 2M tokens	128K tokens
Multi-language	5 languages	2 languages
MCP Support	Native	Supported

Core Capabilities

Model Access

Google Gemini AI SDK:

from google import genai

client = genai.Client(api_key="YOUR_KEY")

# Access Gemini models
response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="Explain quantum physics.",
)

OpenAI Agents SDK:

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY")

# Access GPT models
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum physics."}],
)

Tool Calling

Google Gemini AI SDK:

def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Sunny, 25°C in {location}"

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="What's the weather in Paris?",
    config=types.GenerateContentConfig(tools=[get_weather]),
)

OpenAI Agents SDK:

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a location",
            "parameters": {...},
        },
    }],
)

Deep Research Agent (Gemini Exclusive)

from google.genai import types

client = genai.Client(api_key="YOUR_KEY")

research = client.agents.deep_research(
    prompt="Research the competitive landscape for AI agents.",
    config=types.DeepResearchConfig(
        max_iterations=15,
        include_visualizations=True,
        mcp_servers=["brave-search", "arxiv"],
    )
)
print(research.report)

Computer Use (Gemini Exclusive)

from google.genai import types

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents=[
        types.Part.from_image("screenshot.png"),
        "Click the login button.",
    ],
    config=types.GenerateContentConfig(
        tools=[types.Tool(computer_use=types.ComputerUse())],
    )
)

Structured Outputs (OpenAI Strength)

from openai import OpenAI
from pydantic import BaseModel

class Recipe(BaseModel):
    name: str
    ingredients: list[str]
    steps: list[str]

client = OpenAI()
response = client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Generate a lasagna recipe."}],
    response_format=Recipe,
)

Guardrails (OpenAI Exclusive)

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
    guardrails={
        "input": {"enabled": True, "policy": "prevent-harmful-content"},
        "output": {"enabled": True, "policy": "prevent-harmful-content"},
    },
)

Feature Comparison

Multimodal Capabilities

Feature	Gemini	OpenAI
Text	✅	✅
Image Input	✅	✅
Image Generation	❌ (via separate API)	✅ (DALL-E)
Audio Input	✅	✅
Audio Output	✅ (Live API)	❌
Video Input	✅	✅
Computer Use	✅	❌

Context Window

Model	Context Window
Gemini 3.1 Pro	2M tokens
Gemini 3.1 Flash	1M tokens
GPT-4o	128K tokens
GPT-4omini	128K tokens

Language Support

Language	Gemini SDK	OpenAI Agents SDK
Python	✅	✅
JavaScript/TypeScript	✅	✅
Go	✅	❌
Java	✅	❌
C#	✅	❌

MCP Support

Both SDKs support MCP (Model Context Protocol):

Gemini:

config=types.GenerateContentConfig(
    mcp_servers=[
        types.MCPServerConfig(name="github", config={"token": "ghp_xxx"}),
    ],
)

OpenAI:

# Via MCP server integration

Use Case Analysis

When to Choose Google Gemini AI SDK

✅ Deep Research Needs

• Built-in Deep Research Agent
• Multi-step research with citations
• Visualizations and reports

✅ Multimodal Applications

• Native multimodal processing
• Computer Use for GUI automation
• Live API for real-time voice

✅ Long Context Requirements

• Up to 2M token context
• Process entire codebases or books
• Complex reasoning over large documents

✅ Multi-Language Projects

• Go, Java, C# support
• Enterprise integration
• Google Cloud ecosystem

✅ Cost-Effective at Scale

• Gemini Flash is very cost-effective
• Good for high-volume applications

When to Choose OpenAI Agents SDK

✅ Structured Data Needs

• Excellent structured output support
• Pydantic integration
• JSON mode reliability

✅ Guardrails and Safety

• Built-in content filtering
• Enterprise-grade safety features
• Audit logging

✅ Ecosystem and Community

• Larger developer community
• More third-party integrations
• Extensive documentation

✅ Simplicity

• Clean, intuitive API
• Less configuration needed
• Faster to prototype

✅ DALL-E Integration

• Built-in image generation
• Text-to-image workflows

Performance Comparison

Metric	Gemini	OpenAI
Speed (Flash)	Very Fast	Fast
Speed (Pro)	Fast	Fast
Accuracy	High	High
Cost (Input)	Lower	Higher
Cost (Output)	Lower	Higher
Uptime	High	High

Pricing Comparison

Model	Input (per 1M tokens)	Output (per 1M tokens)
Gemini 3.1 Pro	$1.25	$5.00
Gemini 3.1 Flash	$0.075	$0.30
GPT-4o	$2.50	$10.00
GPT-4omini	$0.15	$0.60

Prices are approximate and subject to change.

Migration Path

From Gemini to OpenAI

# Gemini
from google import genai
client = genai.Client()
response = client.models.generate_content(model="gemini-3.1-pro", contents=prompt)

# OpenAI
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(model="gpt-4o", messages=[{"role": "user", "content": prompt}])

From OpenAI to Gemini

# OpenAI
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(model="gpt-4o", messages=[...])

# Gemini
from google import genai
client = genai.Client()
response = client.models.generate_content(model="gemini-3.1-pro", contents=prompt)

Conclusion

Choose Gemini SDK If...	Choose OpenAI SDK If...
You need Deep Research	You need structured outputs
You need Computer Use	You need guardrails
You need 2M context	You need DALL-E
You use Go/Java/C#	You want simplicity
You want lower costs	You want larger community
You need multimodal	You're already in OpenAI ecosystem

Both are excellent choices. The right decision depends on your specific needs, budget, and existing infrastructure. Many teams use both for different use cases.

Resources

• Google Gemini AI SDK Docs
• OpenAI Agents SDK Docs
• Gemini Models
• OpenAI Models