Gemini 3 Pro Developer Preview: Specs, API, and Agentic Coding with Antigravity

Gemini 3 Pro represents a fundamental redesign of Google's AI architecture. Built on advanced transformer technology with novel attention mechanisms, the model achieves unprecedented performance while maintaining efficiency.

Technical Specifications

1M Token Context Window Deep Dive

The 1 million token context window in Gemini 3.0 Pro enables groundbreaking applications:

• 📚 Entire Codebases: Analyze complete repositories (up to ~400K lines of code) in a single prompt
• 📄 Long Documents: Process full books, legal contracts, or research compilations without chunking
• 🎬 Extended Video: Understand up to 1 hour of video content with full temporal context
• 💬 Deep Conversations: Maintain context across hundreds of turns in a conversation
• 🔍 Multi-document Analysis: Compare and synthesize information across dozens of documents simultaneously

Architectural Innovations

Gemini 3 Pro introduces several architectural breakthroughs:

Google AI Studio provides a powerful no-code interface for experimenting with gemini 3 pro, while Google Antigravity offers a production-ready platform for building autonomous AI agents.

Getting Started with Google AI Studio

Agentic Capabilities with Antigravity

Google Antigravity is a community platform for exploring autonomous agents powered by Gemini 3.0 Pro. Unlike traditional API usage, Antigravity enables:

• 🤖 Autonomous Task Completion: Agents that can plan, execute, and verify complex multi-step workflows
• 🔧 Tool Integration: Native support for function calling, code execution, and external API access
• 🔄 Iterative Refinement: Agents that learn from feedback and improve their outputs
• 🌐 Multi-Agent Collaboration: Coordinate multiple specialized agents for complex tasks
• 📊 Built-in Monitoring: Track agent performance, token usage, and decision-making processes

Building Your First Agentic Application

from google.antigravity import Agent, Tool
from google.generativeai import GenerativeModel

# Initialize Gemini 3 Pro
model = GenerativeModel('gemini-3-pro-preview-11-2025')

# Define custom tools
code_executor = Tool(
    name="execute_code",
    description="Run Python code in a sandboxed environment",
    function=execute_python_code
)

# Create an autonomous coding agent
coding_agent = Agent(
    model=model,
    tools=[code_executor, "web_search", "file_reader"],
    thinking_level=2,  # Enable step-by-step reasoning
    max_iterations=10
)

# Give the agent a complex task
result = coding_agent.run(
    "Create a web scraper that extracts product prices "
    "from an e-commerce site and saves them to a CSV file"
)

print(result.output)
print(f"Agent completed in {result.iterations} iterations")

Thinking Levels: Configurable Reasoning Depth

One of Gemini 3 Pro's unique features is configurable reasoning depth via the "thinking level" parameter:

The Gemini 3.0 Pro API introduces several breaking changes and new parameters compared to previous versions. Here's what developers need to know.

API Endpoint Changes

New Request Parameters

Complete API Request Example

curl https://generativelanguage.googleapis.com/v2/models/gemini-3-pro-preview-11-2025:generateContent \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -d '{
    "contents": [{
      "parts": [{
        "text": "Solve this math problem: If x^2 + 5x + 6 = 0, what are the values of x?"
      }]
    }],
    "generationConfig": {
      "temperature": 0.7,
      "topK": 40,
      "topP": 0.95,
      "maxOutputTokens": 2048,
      "thinkingLevel": 2
    },
    "safetySettings": [{
      "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
      "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    }]
  }'

Structured Output with JSON Schema

Gemini 3 Pro introduces native JSON mode for guaranteed structured outputs:

{
  "responseFormat": {
    "type": "json_schema",
    "json_schema": {
      "name": "product_extraction",
      "schema": {
        "type": "object",
        "properties": {
          "product_name": {"type": "string"},
          "price": {"type": "number"},
          "currency": {"type": "string"},
          "availability": {"type": "boolean"}
        },
        "required": ["product_name", "price"]
      }
    }
  }
}

Prompt Caching for Long Contexts

When working with large contexts that don't change frequently, enable caching to reduce costs and latency:

• ✓ Cache up to 1M tokens for reuse across multiple requests
• ✓ 90% cost reduction for cached tokens
• ✓ 4-hour cache TTL (automatically renewed with each use)
• ✓ Ideal for: documentation, codebases, knowledge bases

Veo 3 (also known as google veo3) is Google's state-of-the-art video generation model, tightly integrated with Gemini 3 Pro for seamless multimodal creation.

Gemini 3 + Veo Integration

The combination of veo gemini 3 enables unprecedented creative workflows:

Veo 3 Technical Capabilities

• 🎥 Resolution: Up to 1080p output at 30 FPS
• ⏱️ Duration: Generate videos up to 2 minutes long
• 🎨 Style Control: Cinematic, animation, documentary, and more
• 🎭 Character Consistency: Maintain character identity across shots
• 🎵 Audio Sync: Synchronized audio generation with video content

Example: End-to-End Video Creation

from google.generativeai import GenerativeModel
from google.veo import VideoGenerator

# Step 1: Use Gemini 3 to create detailed video prompt
gemini = GenerativeModel('gemini-3-pro-preview-11-2025')
prompt_refinement = gemini.generate_content(
    "Create a detailed video prompt for: "
    "A futuristic city at sunset with flying cars"
)

# Step 2: Generate video with Veo 3
veo = VideoGenerator('veo-3')
video = veo.generate(
    prompt=prompt_refinement.text,
    duration=30,  # seconds
    style="cinematic",
    resolution="1080p"
)

video.save("futuristic_city.mp4")

Gemini 3 Pro has been rigorously tested across industry-standard benchmarks, demonstrating state-of-the-art performance in reasoning, mathematics, and general intelligence.

LMArena Overall Rankings

As of November 2025, gemini-3-pro-preview-11-2025 ranks #1 on LMArena's overall leaderboard:

GPQA Diamond (Graduate-Level Science)

The GPQA Diamond benchmark tests AI models on PhD-level questions in physics, chemistry, and biology:

MathArena Apex (Advanced Mathematics)

MathArena Apex evaluates complex mathematical problem-solving including calculus, linear algebra, and proof generation:

Additional Benchmark Results