Gemini 3.1 Pro Agentic Coding Deep Dive

Gemini 3.1 Pro, released in February 2026, represents Google DeepMind's flagship baseline model. With a 77.1% achievement on the ARC-AGI-2 benchmark, it outperforms its predecessor (3 Pro) by over 2x in reasoning capabilities.

Understanding Gemini 3.1 Pro Specifications

Gemini 3.1 Pro features a 1,048,576 token input context window (approximately 1 million tokens) supporting multimodal input: text, images, audio, and video. Output capacity expanded dramatically to 65,536 tokens from the previous 8,192 token limit.

For multimodal capabilities, the model processes up to 900 images per prompt, audio up to 8.4 hours, and video up to 1 hour (without audio).

The 77.1% ARC-AGI-2 score demonstrates the ability to solve unknown logic patterns—problems not present in training data. For coding, this translates to handling "bugs never seen before" and adapting to new architecture patterns effectively.

What is Agentic Coding?

Agentic coding represents an AI paradigm where the model autonomously handles not just code generation, but execution, testing, debugging, and deployment. Humans provide direction; AI acts as an agent completing the entire workflow.

3.1 Pro excels here for three reasons:

First, function calling precision improved significantly. Multiple tool invocations execute in proper sequence with high accuracy.

Second, instruction following improved within lengthy contexts. Even with 1M tokens of large codebases loaded, the model maintains precise instruction adherence.

Third, 65,536 token output capacity enables single-response bulk file generation and cross-file refactoring spanning multiple files.

Agentic Coding with Gemini CLI

Gemini CLI is a command-line tool calling Gemini directly from terminal. Combined with 3.1 Pro, it creates a powerful agentic coding environment.

Setup

# Install Gemini CLI
npm install -g @anthropic-ai/gemini-cli
 
# Set 3.1 Pro as default model
export GEMINI_MODEL=gemini-3.1-pro
 
# Launch in project directory
cd your-project
gemini

Full Codebase Analysis

Leverage 1M token context to load entire project source and instruct workflow:

> Read all project source code, understand architecture,
  then execute these refactorings:
  1. Separate authentication logic as middleware
  2. Migrate database queries to repository pattern
  3. Consolidate error handling to unified error boundary

3.1 Pro correctly tracks cross-file dependencies, maintaining consistency while rewriting code across multiple files.

Bug Fix Workflow

> Analyze error log, identify root cause and fix.
  Also add tests to validate the fix.

  Error Log:
  TypeError: Cannot read property 'id' of undefined
    at UserService.getProfile (src/services/user.ts:42)
    at async ProfileController.show (src/controllers/profile.ts:18)

3.1 Pro's agentic nature automatically executes these steps: parse stack trace, load relevant files, understand context, apply fix, write test code, run tests, verify results.

Production Operations with Vertex AI

Production environments should access 3.1 Pro via Vertex AI.

import vertexai
from vertexai.generative_models import GenerativeModel, Tool, FunctionDeclaration
 
vertexai.init(project="your-project", location="us-central1")
 
# Tool definitions
run_tests = FunctionDeclaration(
    name="run_tests",
    description="Execute specified test suite",
    parameters={
        "type": "object",
        "properties": {
            "test_path": {
                "type": "string",
                "description": "Path to test file or directory"
            },
            "coverage": {
                "type": "boolean",
                "description": "Generate coverage report"
            }
        },
        "required": ["test_path"]
    }
)
 
deploy_to_staging = FunctionDeclaration(
    name="deploy_to_staging",
    description="Deploy to staging environment",
    parameters={
        "type": "object",
        "properties": {
            "branch": {
                "type": "string",
                "description": "Branch name to deploy"
            }
        },
        "required": ["branch"]
    }
)
 
tools = [Tool(function_declarations=[run_tests, deploy_to_staging])]
 
model = GenerativeModel(
    "gemini-3.1-pro",
    tools=tools,
    system_instruction="""
    You are a senior software engineer.
    Always run tests after code changes.
    Deploy to staging only when all tests pass.
    """
)
 
chat = model.start_chat()
response = chat.send_message(
    "Add rate limiting to authentication middleware. "
    "Run tests and deploy to staging if successful."
)

Leveraging the thinking_level Parameter

3.1 Pro's thinking_level parameter controls reasoning depth.

generation_config = {
    "thinking_level": "high",  # low, medium, high
    "max_output_tokens": 65536,
    "temperature": 0.1,
}
 
response = model.generate_content(
    "Identify security vulnerabilities in this code and fix them.",
    generation_config=generation_config,
)

Use thinking_level: "high" for scenarios requiring deep analysis like security reviews or architecture design. Use "low" for straightforward code generation or format conversion.

Choosing Between 3.1 Pro and 3 Pro

3.1 Pro is not a simple upgrade to 3 Pro; they have different characteristics.

3.1 Pro suits complex reasoning tasks, large codebase analysis, security audits, and architecture design.

3 Pro and 3 Flash suit simple code generation, chatbots, and latency-critical real-time processing.

Cost-wise, 3.1 Pro exceeds 3 Pro pricing. Rather than using 3.1 Pro for all requests, intelligently allocate based on task complexity.

Looking back

Gemini 3.1 Pro's agentic coding capability rests on three pillars: 1M token context, 65K output tokens, and ARC-AGI-2 77.1% reasoning performance. From interactive development via Gemini CLI to production operations via Vertex AI, this model addresses a broad spectrum of use cases.

Gemini 3.1 Pro Agentic Coding Deep Dive — The Truth Behind 77% ARC-AGI-2 Performance