Why Google’s Smartest AI Takes Two Minutes to Say “Hello”

Google just released Gemini 3.1 Pro, a model that boasts the highest reasoning scores in the industry. It also took 104 seconds to respond to the word “hi” on launch day. In an era where we measure AI latency in milliseconds, Google has intentionally built a turtle that thinks it’s a genius—and for most developers, it’s currently a productivity nightmare.

The Why: The Death of the Instant Response

For the last two years, the AI arms race has been about speed and “vibes.” We wanted ChatGPT to finish our sentences before we stopped typing. But Gemini 3.1 Pro represents a hard pivot toward System 2 thinking—deliberate, slow, and computationally expensive reasoning.

Google is chasing a metric called ARC-AGI-2, a benchmark designed to test an AI’s ability to solve novel problems it wasn’t specifically trained on. By hitting a 77.1% score, Google has technically leapfrogged OpenAI and Anthropic. The release follows Google’s January AI Blitz, where the company began transforming its ecosystem into a series of active agents. The problem? They did it by letting the model “think” in a loop for minutes—or hours—at a time. If you are building a customer-facing chatbot, this model is effectively useless. If you are solving the world’s most complex logistics problems, it might be the only tool that works.

How to Navigate the Gemini 3.1 Pro Transition

If you are a developer looking to integrate this model, you cannot treat it like a standard LLM. You have to re-architect your entire stack for asynchronous processing.

Decouple the UI from the API: Never call Gemini 3.1 Pro from a synchronous front-end request. You will hit a 504 Gateway Timeout before the model even finishes its first “thought.”
Implement Webhooks: Set up a job queue (like RabbitMQ or Redis). Send the prompt, give the user a “Processing” status, and wait for a webhook to push the result back.
Set Extreme Timeout Limits: Standard API timeouts are usually 30–60 seconds. Early reports show Gemini 3.1 Pro tasks running for 15,459 seconds (over 4 hours) before completion. Adjust your infrastructure to keep long-running sockets open or use batch mode.
Use “Small” Models for Routing: Use Gemini Flash or GPT-4o mini to determine if a query actually needs the heavy lifting of 3.1 Pro. This level of AI-driven software development requires precision routing to maintain efficiency. If someone says “hi,” don’t send it to the 104-second reasoning engine.

💡 Pro-Tip: Use the “Candidate Count” parameter sparingly. With a model this slow, requesting multiple versions of a response (n>1) can exponentially increase your “Time to First Token,” potentially doubling your already massive wait times.

The Buyer’s Perspective: Is Reasoning Worth the Wait?

Google is offering Gemini 3.1 Pro at $2 per million input tokens. That is roughly half the price of Anthropic’s Claude 4.0/Opus equivalents. On paper, Google wins the value war. This release builds upon the foundation of Google Cloud AI services already utilized by major government and enterprise sectors.

However, the “hidden tax” is the developer experience and the compute cost of idling. While the tokens are cheap, the time-to-value is abysmal. Anthropic Claude AI and OpenAI have focused on “Real-Time Directness”—they are better for coding assistants and creative writing. Google has built a “Research Specialist.”

The 1M token context window remains Google’s “killer feature,” allowing you to drop entire codebases or 1,500-page PDFs into the prompt. But be warned: combining a massive context window with “extended reasoning” creates a latency black hole that can swallow your entire afternoon.

FAQ

Q: Is the 104-second delay a bug?
A: Technically, no. It’s a byproduct of the model’s “chain-of-thought” architecture. This is the hallmark of Gemini 3 Deep Think, a model designed to prioritize accuracy over speed. It is verifying its own logic before it speaks, even for simple greetings.

Q: Can I use this for a live chat application?
A: Absolutely not. Your users will leave long before the model responds. Use Gemini 1.5 Flash for speed and 3.1 Pro for back-end data analysis.

Q: Why is the ARC-AGI-2 score so important?
A: It measures “fluid intelligence.” High scores suggest the AI can learn new tasks on the fly rather than just regurgitating patterns from its training data.

Ethical Note/Limitation: This model cannot reliably distinguish between a task that requires deep thought and a simple query, leading to massive energy and time waste for trivial interactions.