Gemini 3 Is Probably The Best Model Right Now. That's Not The Question.
The real decision isn't about power. It's about where hallucinations will cost you money.
24 Hours With Gemini 3: Hype, Reality, And What I’m Seeing So Far
TL;DR: On paper, Gemini 3 Pro is probably the strongest general-purpose model right now. Benchmarks show it beating GPT-5.1 and Claude on most of the hard reasoning, math, visual, and “agent” style tasks. But it is not “solved AI.” Hallucinations are still real, coding has tradeoffs, and tools like Antigravity and vibe coding are powerful but early. My own experience so far matches much of the noise online: it feels fast, sharp, and eager to work, with rough edges you still have to manage.
Is Gemini 3 actually the most powerful model right now?
If you look at the public numbers, the honest answer is, yeah, for the moment it probably is.
Across the current crop of reasoning and multimodal benchmarks, Gemini 3 Pro sits at or near the top almost everywhere. It does especially well on the harder tests that try to break “smart on paper” models: long chains of logic, tricky math, multi-step reasoning. On the visual side, it is also scoring better than GPT-5.1 and Claude at reading interfaces, images, and mixed media.
There is also a separate reliability study that compared factual accuracy across a bunch of models. Gemini 3 Pro came out number one there too, ahead of the usual suspects. More correct answers, fewer outright blunders.
But there is a catch. When Gemini 3 is wrong, it still tends to be confidently wrong. The hallucination rate in that same study was high. So it is more knowledgeable and better at hard problems, but it has not suddenly become cautious or self-aware.
So if your question is “Is this the strongest model on public benchmarks?” I’d say yes, right now it looks that way.
If your question is “Can I throw this at my legal work or financial analysis and stop worrying?” the answer is still no.
What Gemini 3 is already doing really well
The thing that jumps out, both in benchmarks and real use, is how steady it feels on complex reasoning. Multi-step problems that used to make other models drift off course are more likely to stick together. You can hand it a messy prompt that mixes text, diagrams, code, and a few weird constraints, and it keeps track of the argument more often than not.
That matters a lot if you live in finance, law, or operations. Models were already fine at emails and slide drafts. The limiting factor has been “Can I trust it to carry a complicated argument all the way through without falling apart at step seven.” Gemini 3 does not completely fix that, but the bar is higher.
The second big strength is visual and multimodal work. Anything involving screenshots of dashboards, product UIs, or charts feels better. It reads screens more accurately and uses that understanding in the answer, which is exactly what you need if you care about “agents that can actually use software” instead of just talking about it.
Then there is vibe coding. Google has been showing off this pattern where you describe the app, the style, maybe even the mood, and Gemini 3 builds a working version in one or two prompts. People are already posting examples of small games, web apps, and tools that got from idea to running code with far less back-and-forth than we are used to.
The new Gemini app and the generative UI work on top of this. Responses feel shorter, more direct, and less coated in fake compliments. And instead of a giant wall of text, you start seeing actual interactive layouts: comparison tables, simple calculators, little dashboards that show “here is what you asked for” in a way you can click on.
For a regular user, that just means less reading, more doing.
Antigravity: serious bet or agent hype 2.0?
Antigravity is the part that has the developer crowd buzzing. It is a free, agent-first IDE that ships with Gemini 3 Pro built in, plus other models as extra co-workers.
The mental flip is simple but important. Instead of “I have a code editor and there is a bot in the sidebar,” Antigravity treats the agents as the primary workers. The editor, terminal, and browser are tools that they can orchestrate. Along the way, they produce “artifacts,” like plans, screenshots, and notes, so you can see what happened without scrolling through a thousand log lines.
Inside Google and around it, people are pretty excited. The early posts from the team behind Antigravity are proud and very confident. Outside that bubble, developers are intrigued but cautious. Everyone remembers earlier “autonomous agents” that sounded amazing in demos and then got confused by real-world projects.
From a business perspective, Antigravity is important less because of any single feature and more because it shows Google’s strategy. They are not just trying to be the engine behind other people’s tools. They want to own the place where your engineers actually live.
If you run an engineering team, this is where I’d spend real time testing. Give Antigravity a small but real project, something like an internal dashboard or a little data tool, and see if the team feels like it helped them ship faster or just created more overhead. That tells you much more than any benchmark chart.
Where Gemini 3 is struggling so far
The big one is still hallucinations. Yes, it is more accurate overall, but it is not more humble. When it does not know something, it will still try to answer instead of saying “I don’t know.” In law, healthcare, audit, or anything where “probably correct” is not good enough, this is a real tax. You still need guardrails, source checking, and human review.
Coding is strong, but it is not a clean sweep. On classic coding benchmarks, Gemini 3, GPT-5.1, and Claude are in the same band. Gemini 3 tends to pull ahead on agent-style tests and long-horizon tasks. Claude still looks very good on software engineering benchmarks, and many developers still like how it handles big refactors and complicated existing codebases. A lot of early chatter looks like this: people doing quick scripts and demos are raving about Gemini 3, people inside giant monorepos are more split.
There is also a complexity problem for normal users. The Gemini story now includes: the app, Search, Workspace, Vertex, vibe coding, Antigravity, Deep Think, different model variants, and generative UI. If you live in this world every day, it is fun. If you are just a manager trying to get through your week, it is confusing. Do you ask Gemini in Search, in the app, inside Docs, or inside the IDE your team now uses. That is not obvious yet.
To be fair, Microsoft and OpenAI have similar complexity issues. But if we are talking about how Gemini 3 is landing after 24 hours, I would say power users are thrilled, and mainstream business users will need a clearer story.
How this looks from a corporate seat
If you are sitting in a CIO, CFO, or managing partner chair, here is how I’d frame it after watching day one.
On pure capability, Gemini 3 Pro is the new bar. The numbers are strong across reasoning, math, vision, and agent benchmarks, and they are not just marginal gains.
On reliability, no one has cracked it. Gemini 3 is more knowledgeable, but just as willing to bluff. You still have to treat it like a very sharp junior, not a partner who signs off on work.
On tools, Google now has a real, opinionated platform story. Antigravity, vibe coding, generative UI, and Gemini Agent in Workspace and Vertex make this feel like more than “just another chat bot.”
So the decision in front of you is not “Is Gemini 3 good.” It is clearly good.
The real decision is “Where does this fit relative to GPT-5.1 and Claude for our work.” Which model becomes your default co-worker, and which ones are specialists you bring in for certain jobs.
My early bias looks like this: Gemini 3 Pro is the obvious candidate when visual reasoning, long context, or agentic coding are central. GPT-5.1 still makes a lot of sense for high-volume, cost-sensitive workloads where speed and price matter more than squeezing out every last bit of reasoning performance. Claude remains the conservative choice when “please do not hallucinate into production” is the top constraint.
So yes, I’m with you, it feels like the most powerful general model right now.
But it is still a tool, dropped into the same messy human systems we already have. How much value you get will depend less on the launch event and more on how thoughtfully you plug it into real work.
I write these pieces for one reason. Most leaders do not need another ranking of Gemini 3 versus GPT-5.1 versus Claude; they need someone who will sit next to them, look at how work actually moves through the company, and say, “Here is where Gemini 3 belongs, here is where something else might still be better, and here is how we keep all of it safe.”
If you want help sorting that out for your company, reply to this or email me at steve@intelligencebyintent.com. Tell me what you sell, which tools you are already using, and where work is bogging down. I will tell you what I would test first, which model I would put on it, and whether it even makes sense for us to do anything beyond that first experiment.