Gemini 3 Deep Think Review: Is Google’s “System 2” Monster Worth the Ultra Price?

1. Introduction

Google has officially quit playing catch-up. For the last two years, the Mountain View giant felt like a slumbering titan swatting at agile startups. That narrative ended this morning. With the release of Gemini 3 Deep Think, Google isn’t just releasing another chatbot that guesses the next word in a sentence. They are releasing a digital scientist that pauses, reflects, and iterates.

The headline statistic is terrifyingly good: a 45.1% score on the ARC-AGI-2 benchmark. For the uninitiated, ARC is the “holy grail” of abstract reasoning, a test that breaks LLMs because it requires novel pattern recognition rather than rote memorization. Most models struggle to break 20%. Gemini 3 Deep Think didn’t just break the record; it doubled it.

But there is a catch. A big one. This level of intelligence is locked behind a paywall that costs more than a car lease for some people. At $250 a month, we have to ask: is this tool strictly for the elite, or is it the inevitable future of how we all work?

2. What is Gemini 3 Deep Think? Understanding “Inference Time Compute”

To understand why this model is different, we have to talk about how AI usually works versus how humans work. This is the distinction between System 1 and System 2 thinking AI.

Standard Large Language Models (LLMs) like GPT-4 or the base Gemini Pro operate on System 1. You give them a prompt, and they stream tokens immediately. It is an instinctive, “fast” reaction. It is like a chess player making a move in a blitz game, pure intuition based on training data.

Gemini 3 Deep Think introduces inference time compute. When you ask it a hard question, it doesn’t just speak. It spends computational resources exploring a search tree of possibilities. It generates multiple hypotheses, tests them against internal logic (similar to AlphaProof), and discards the bad ones before it ever prints a single character to your screen.

This is the equivalent of a grandmaster sitting on their hands for ten minutes, calculating twenty moves deep. The model isn’t just retrieving information. It is simulating outcomes. This shift from “training compute” (making the model big) to inference time compute (letting the model think longer) is the defining trend of 2025.

3. The Benchmark Breakdown: Gemini 3 vs. The World

Let’s look at the numbers. The data Google released, and the leaks corroborated by industry insiders like Jeff Dean, paint a picture of a model that has separated itself from the pack.

3.1 ARC-AGI-2 (Visual Reasoning)

This is the big one. Gemini 3 Deep Think hit 45.1%. To put that in perspective, GPT-5.1 sits at 17.6%. This benchmark consists of visual puzzles that look like IQ tests. You cannot memorize the answers because the test set is private. The model has to understand the “rule” behind a grid of colored pixels and apply it to a new grid.

There is a nuance here. The 45.1% score was achieved with “Tools On.” This means the model wrote Python code to solve the puzzles. Some Reddit purists called this cheating. I disagree. If I ask a human engineer to solve a complex matrix transformation, I expect them to use Python or MATLAB. Gemini 3 Deep Think using code execution to solve visual logic is not cheating. It is engineering.

3.2 Humanity’s Last Exam (HLE)

On general reasoning and knowledge, the model scored 41%. This benchmark is designed to be un-googleable. It tests synthesis of disparate facts. The gap here between the Deep Think model and the standard Gemini 3 vs GPT-5 matchup is smaller, but still significant. It suggests that “thinking” helps more with logic puzzles than it does with general knowledge retrieval.

3.3 GPQA Diamond

This tests PhD-level scientific proficiency. The score of 93.8% is absurdly high. We are reaching the point of benchmark saturation where the tests are no longer hard enough to distinguish between the top models. Below is the raw data comparing the current titans of industry.

4. Real-World Capabilities: Beyond the Charts

Charts are nice, but they don’t tell you what it feels like to use the machine. Early reports from users with Google AI plans suggest a split experience.

4.1 The Coding Paradox

You would expect a model with high reasoning to be a coding god. The reality is messier. Users have noted that Gemini 3 Deep Think is excellent at “visual” coding—specifically SVG manipulation and creating vector graphics from scratch. It creates perfect circles and complex geometries where GPT-5 often hallucinates disjointed lines.

But for large C++ codebases? The jury is out. Some developers prefer the “vibes” and context handling of Claude. Gemini 3 Deep Think can sometimes over-think a simple refactor, trying to re-architect a system when you just wanted a bug fix. It is the brilliant intern who tries to rewrite the kernel instead of fixing the typo.

4.2 The Hallucination Hangover

We haven’t solved hallucinations yet. A user noted that while the model is great at math proofs, it still fails at literature reviews. It took a 2007 handbook chapter and invented a new abstract for it, citing the wrong year. This is the danger of System 2 thinking AI. It can use its advanced logic to convince itself of a lie. It constructs a very plausible, highly reasoned path to a completely false conclusion.

4.3 Engineering Drawings

One of the most interesting niche use cases appearing on forums is interpreting engineering drawings. Because of its high visual reasoning (ARC score), Gemini 3 Deep Think is surprisingly good at looking at a schematic and explaining the logic of the assembly. This is a massive unlock for hardware engineers who have been largely ignored by the text-heavy LLM revolution.

5. Gemini 3 Deep Think vs. Claude Opus 4.5

If you are deciding where to spend your budget, the choice between Gemini 3 vs Claude Opus 4.5 comes down to your personality type.

Claude Opus 4.5 is the liberal arts major who double-majored in computer science. It follows instructions with nuance. It captures tone perfectly. If you want to write a blog post, draft an email, or get a philosophical summary of a book, Claude is still the king of “vibes.” It feels human.

Gemini 3 Deep Think is the STEM researcher locked in a basement. It is blunt. It is less likely to roleplay and more likely to correct your math. If you are doing hard science, complex logic puzzles, or agentic workflows where a wrong answer breaks the chain, you want Gemini.

It is worth noting that Gemini 3 Deep Think dominates in “brute force” logic tasks where there is a distinct right or wrong answer. Claude wins in the gray areas.

6. The “Tools On” Controversy: Did Google Cheat?

There is a lot of noise about the 45.1% ARC score being “Tools On.” Let’s shut this down. The goal of AGI (Artificial General Intelligence) is problem-solving. Humans solve problems by using tools. If I ask you to multiply 34,231 by 98,222, and you do it in your head, you are smart. If you pull out a calculator, you are efficient.

Gemini 3 Deep Think uses a Python sandbox as a cognitive prosthetic. When it sees a visual puzzle, it doesn’t just guess the pixel arrangement. It writes a script to test a hypothesis about the pattern. “If I rotate this grid 90 degrees, does it match?” It runs the code. It sees the result. It iterates.

This isn’t cheating. This is exactly how we want AI to behave. We want models that can verify their own intuition with deterministic code. This is the only way we will ever trust these systems in critical infrastructure.

7. Pricing Analysis: Is Google AI Ultra Worth the $250 Tag?

This is the friction point. The Google AI Ultra price is $250 per month. That is a staggering jump from the standard $20.

Is it worth it? For 99% of people, absolutely not. But Google knows this. This plan isn’t for the person who uses ChatGPT to write birthday cards. This is an enterprise bundle masquerading as a subscription.

Here is the breakdown of what that $250 actually buys you:

If you are already paying for 2TB or 5TB of Google One storage, the math starts to look different. 30TB of storage is enterprise-grade. If you are a video editor or a photographer with massive archives, the storage alone almost justifies the Google AI plans pricing.

The addition of Veo 3 (Google’s answer to Sora) makes this a creative suite. But if you just want a chatbot? Stick to Pro. The Google AI Ultra price is only justifiable if you are a “power user” in the truest sense—someone who needs the storage, the video rendering, and the heavy reasoning compute.

8. User Experience and Latency

There is a tactile difference in using Gemini 3 Deep Think. You hit enter, and you wait. Unlike the instant dopamine hit of standard models, Deep Think makes you watch it work. Google has implemented a UI that shows the “thought process” (though likely a sanitized summary). You see it considering paths. You see it backtracking.

For some, this builds trust. You know it isn’t just hallucinating the first thing that comes to its statistical mind. For others, the latency is a dealbreaker. In a world of instant search, waiting 60 seconds for an answer feels like an eternity. But we have to adjust our expectations. If you are asking a question that requires inference time compute, you are asking for work to be done. You wouldn’t expect a human to write a research paper in three seconds. We shouldn’t expect it from Gemini either.

9. How to Access Gemini 3 Deep Think

If you have burned a hole in your wallet and subscribed to Ultra, here is how you toggle the monster on:

1. Open the Gemini App (web or mobile).
2. Ensure your subscription is active (The badge should say Ultra).
3. In the model dropdown, select Gemini 3 Pro.
4. Look for the “Deep Think” toggle in the prompt bar.
5. Turn it on.

Be warned: your query limit is lower here. You cannot spend all day chatting about the weather. Use your prompts for the problems that actually matter.

10. Conclusion: A New Era for Scientists, A Wait-and-See for Everyone Else

Google has reclaimed the reasoning crown. The benchmarks don’t lie, and the Gemini 3 Deep Think architecture is a legitimate leap forward in AI capability. By integrating search and code execution into the reasoning process, they have built a system that mimics the slow, deliberate thought process of an engineer.

But the Google AI Ultra price places this firmly out of reach for the casual enthusiast. This is a tool for the builders, the scientists, and the people who are pushing the boundaries of what code can do.

If you are a researcher trying to solve novel math problems, or a developer trying to generate complex SVGs, buy it. It is the best tool on Earth right now. For everyone else? Wait. These features always trickle down. Today’s $250 luxury is next year’s free tier. But for now, if you want to see what the bleeding edge looks like, you have to pay the toll.

The next step is yours: Are you upgrading to Ultra, or are you waiting for GPT-5 to respond? Let me know in the comments.