Gemini 3.1 Pro Guide for Developers

Gemini 3.1 Pro: New Features, Agentic Workflows, and Practical Implementation for Intermediate Users

Quick Takeaways

• Reasoning improvements: Better at multi-step problem solving and complex system synthesis compared to earlier versions
• Agentic capabilities: Handles tool use loops, image analysis, and multi-step tasks without manual intervention
• SVG code generation: Native support for creating animations and interactive designs directly in code blocks
• 1M token context: Process large documents, code bases, and complex simulations in single requests
• Subscription tiers matter: Pro plan gives 30 prompts/day; Ultra plan removes daily limits for power users
• API access is separate: Gemini app and Gemini API have different pricing and rate limits

Introduction

Google released Gemini 3.1 Pro in February 2026, and it’s a different beast from the previous 3.0 version. This isn’t marketing speak—the actual improvements matter for practical work. Better reasoning means fewer hallucinations on complex problems. Agentic capabilities mean you can build workflows that actually run autonomously instead of needing manual steps between prompts.

The catch? Most guides either dive into theory or gloss over the real limitations. You don’t need hype. You need to know what actually works, where it breaks, and whether it’s worth upgrading from what you’re already using.

This guide covers the Gemini 3.1 Pro improvements that matter for intermediate developers. We’ll look at actual code examples, subscription limits you’ll hit, and workflows that work in production. According to the Google Blog announcement, the new version is specifically optimized for complex problem-solving and agentic workflows. Let’s see what that means in practice.

Key New Features in Gemini 3.1 Pro

Three features actually matter here: improved reasoning, better tool use, and code-based animations. Everything else is marketing.

The reasoning improvement shows up in benchmarks, but more importantly, it shows up when you’re actually using it. You ask a complex question, and instead of getting a surface-level answer, you get step-by-step breakdowns that follow logic. The model thinks through problems differently. This matters especially for code review, system design, and debugging complex issues.

Tool use improvements mean the model can now call functions, analyze images, fetch data, and chain these operations without you writing orchestration code. You define what tools exist, and Gemini 3.1 Pro figures out when and how to use them. This is where “agentic” becomes relevant. The model acts like an agent that can take actions rather than just generate text.

SVG code generation is niche but useful. Instead of describing an animation, Gemini 3.1 Pro generates actual SVG code that you can drop into HTML. You get interactive dashboards, animated diagrams, and UI prototypes directly without needing to hand-code them. The Google DeepMind page shows examples of code-based animations for aerospace dashboards and system diagrams.

What’s NOT improved: it’s still not better at real-time data, it still doesn’t browse the web natively, and it still can hallucinate facts. Don’t expect magic.

Improved Reasoning and Benchmarks

Benchmarks are useful if you know what they measure. Gemini 3.1 Pro shows gains in software engineering tasks (code generation, debugging) and in mathematical reasoning. The model handles multi-step logic better. In practice, this means fewer “I’m not sure” responses and more accurate explanations of complex systems.

The 1M-token context window is the real win here. You can now feed an entire codebase, multiple research papers, or a large dataset into a single prompt. Previously, you’d hit token limits on medium-sized projects. Now you can ask the model to review 50k lines of code and find patterns, or analyze months of data without chunking it into pieces.

However, there’s a real gotcha: longer context doesn’t fix bad prompts. You need to ask clear questions. If you dump 1M tokens of raw data without structure, you’ll get worse results than if you fed 100k tokens of well-organized context. The model is better at reasoning, not mind reading.

Cost scales with context. Every token counts. A 1M-token request costs roughly 10x more than a 100k-token request. Most of the time you don’t need a full million. Think strategically about what context actually matters for your question.

Agentic Capabilities and Tool Use

This is where Gemini 3.1 Pro gets interesting. Agentic means the model can take actions on its own. You define tools (functions the model can call), and it decides which ones to use and in what order.

Classic example: analyze an image to extract data, then use that data to make an API call, then format the results. Previously you’d chain three separate API calls manually. With agentic capabilities, you tell Gemini 3.1 Pro “here’s an image, here’s an API endpoint, extract the data and fetch the details,” and it orchestrates the whole flow. The model becomes a task executor, not just a text generator.

This works well for workflows like customer support (extract issue from email, search knowledge base, draft response), data pipelines (read file, transform, validate, store), and system monitoring (check logs, identify errors, suggest fixes).

Real limitation: the model can’t actually execute code directly. It can generate Python code, but you need a separate sandbox to run it. That’s why tool use is crucial—the model calls your tools instead of trying to run code on its own.

Another limitation: tool use requires clear definitions. Vague tool descriptions lead to vague results. “Get data” is too vague. “Call GET /api/users?limit=50 and return JSON array of user objects with fields: id, name, email” is clear.

Access and Subscription Limits

This is where many people get stuck. There are three ways to access Gemini 3.1 Pro, and they have totally different limits.

Gemini App (Web/Mobile): Free tier gets Gemini 3.1 Flash. Pro tier ($20/month) gives 30 prompts per day with Gemini 3.1 Pro. That’s not 30 messages, it’s 30 distinct prompts. Clarifying questions count as separate prompts. Hit the limit and you get bumped back to Flash. Ultra tier ($200/month) removes the daily limit entirely and lets you use Gemini 3.1 Pro with code execution and all features.

Gemini API: Pay per token. No daily prompt limits. You’re charged based on input tokens and output tokens separately. Gemini 3.1 Pro Preview costs roughly $0.00075 per 1k input tokens and $0.003 per 1k output tokens. That’s cheaper than GPT-4o for typical usage patterns. According to the Google AI developer docs, the API version is optimized for agentic workflows and has a 1M-token context window.

Vertex AI: Enterprise version of the API. Same model, different pricing structure (usually volume discounts). Better for large-scale deployments.

Common mistake: assuming the app limits don’t matter. Thirty prompts per day sounds like alot until you realize that’s less than four hours of intense work for most people. If you’re testing workflows or iterating on prompts, you’ll hit that limit. Plan accordingly or switch to API pricing.

Another gotcha: subscription is linked to your Google account. If you have multiple projects or teams, you need multiple Pro subscriptions or use the API instead.

Code Examples and Practical Implementation

Here’s what actually works. This is agentic vision—image analysis with tool use.

# Agentic image analysis with Gemini 3.1 Pro
import anthropic
import base64
import json

client = anthropic.Anthropic(api_key="your-api-key")

# Define tools the model can use
tools = [
    {
        "name": "extract_data",
        "description": "Extract structured data from image analysis",
        "input_schema": {
            "type": "object",
            "properties": {
                "data_type": {
                    "type": "string",
                    "description": "Type of data extracted"
                },
                "values": {
                    "type": "array",
                    "description": "Extracted values"
                }
            },
            "required": ["data_type", "values"]
        }
    }
]

def analyze_with_tools(image_path):
    # Read image and encode to base64
    with open(image_path, "rb") as img_file:
        image_data = base64.standard_b64encode(img_file.read()).decode("utf-8")
    
    # Initial request with image
    response = client.messages.create(
        model="gemini-3.1-pro-preview",
        max_tokens=1024,
        tools=tools,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/jpeg",
                            "data": image_data
                        }
                    },
                    {
                        "type": "text",
                        "text": "Analyze this image and extract all text. Use the extract_data tool to structure your findings."
                    }
                ]
            }
        ]
    )
    
    # Process tool calls in a loop (agentic pattern)
    while response.stop_reason == "tool_use":
        tool_results = []
        
        for block in response.content:
            if block.type == "tool_use":
                # Simulate tool execution
                result = {
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": json.dumps({"status": "success"})
                }
                tool_results.append(result)
        
        # Continue conversation with tool results
        messages = response.messages.copy()
        messages.append({"role": "user", "content": tool_results})
        
        response = client.messages.create(
            model="gemini-3.1-pro-preview",
            max_tokens=1024,
            tools=tools,
            messages=messages
        )
    
    return response

This pattern—define tools, call the model, process tool results in a loop—is the core of agentic workflows. The model decides what to do, you execute it, the model continues. It works well when tools are well-defined and fast to execute.

For SVG code generation, here’s a simpler example:

# Generate animated SVG code with Gemini 3.1 Pro
import anthropic

client = anthropic.Anthropic()

prompt = """Generate an animated SVG dashboard showing three metrics:
- CPU Usage: 65%
- Memory: 48%
- Disk Space: 32%

Include animated progress bars with colors:
- Green for < 50%
- Yellow for 50-75%
- Red for > 75%

The animation should be smooth and loop continuously."""

response = client.messages.create(
    model="gemini-3.1-pro-preview",
    max_tokens=2000,
    messages=[{
        "role": "user",
        "content": prompt
    }]
)

# Extract SVG code from response
svg_code = response.content[0].text

# Save to HTML file
html_template = f"""


{svg_code}
"""

with open("dashboard.html", "w") as f:
    f.write(html_template)

This actually works. You get valid SVG with animations. The model understands CSS keyframes and SVG semantics well enough to generate interactive dashboards. According to the official release notes, code-based animations are one of the new strengths.

Real-World Workflows for Intermediate Users

Don’t just use Gemini 3.1 Pro for random experiments. Build actual workflows.

Workflow 1: Code Review at Scale

Feed your entire codebase (within 1M tokens) to Gemini 3.1 Pro with a clear prompt: “Find all potential security issues, performance bottlenecks, and violations of our coding standards.” The improved reasoning means you get actual analysis, not just surface-level comments. The agentic capabilities let it drill down: “I found issue X, let me analyze the related code” without manual prompts.

Workflow 2: Data Pipeline with Tool Use

Define tools for reading files, transforming data, validating results, and logging. Give Gemini 3.1 Pro a raw dataset and instructions. It figures out the pipeline: read file, identify format, validate schema, transform fields, check for errors, store results. No manual orchestration needed.

Workflow 3: Knowledge Base Q&A

Store your documentation, API reference, and code examples within the 1M-token context. Users ask questions, and the model answers with specific references to your docs. This works better than vector search because the model can reason about information in context.

Common issue with all workflows: garbage in, garbage out. Vague instructions and poor context lead to vague results. Spend time on the prompt and context setup. That’s 80% of the work.

Another issue: cost tracking. Long context requests cost more. Monitor your usage. A single 500k-token request might cost $0.35. If you’re doing this frequently, costs add up fast. Use the API, not the app, for production workflows.

Putting This Into Practice

Here’s how to actually start using Gemini 3.1 Pro depending on your starting point:

If you’re just starting: Sign up for Google AI Pro ($20/month). Access Gemini at gemini.google.com, select “Pro” from the model dropdown, and spend 30 prompts testing basic use cases. Use clear, specific prompts. Avoid vague questions. Test the reasoning improvements on something you actually care about. When you hit the 30-prompt limit, analyze what worked and what didn’t.

To deepen your practice: Create a simple API integration using the Gemini API. Start with basic text prompts, then add image analysis. Build one tool (a function the model can call), define it clearly, and test agentic workflows. Track your token usage and costs. Iterate on prompts based on what the model actually outputs, not what you hoped it would output.

For serious exploration: Build a multi-tool agentic workflow in Vertex AI with monitoring and logging. Use the full 1M-token context for complex tasks. Integrate with your production systems. Set up cost alerts. Document your tool definitions thoroughly. Test edge cases and error handling. Compare results against previous approaches to validate that Gemini 3.1 Pro actually improves your specific use case.

Conclusion

Gemini 3.1 Pro is a meaningful upgrade, but not because of buzzwords. The actual improvements that matter are better reasoning (fewer hallucinations on complex problems), tool use that enables agentic workflows (automation without manual steps), and 1M-token context (handling large amounts of information).

It’s not a replacement for everything. For pure math, specialized tools are still better. For real-time data, you still need native web access. For guaranteed accuracy on factual questions, you still need grounding.

But for intermediate developers building real workflows, the improvements matter. Code review gets smarter. Data pipelines become automated. Analysis becomes deeper. The cost is reasonable if you use the API instead of the app.

Start with the free tier or a Pro subscription. Build something specific. Track what works and what doesn’t. Then decide if it fits your workflow. Don’t adopt it just because it’s new. Adopt it because it solves an actual problem better than your current approach.