There Is No "Best AI." There's Only the Right One for the Job.
A practical routing guide for matching the right AI model to the right task, based on 3 years of daily testing.
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Stop Asking “Which AI is Best?” and Start Asking “Best for What?”
TL;DR: These models aren’t interchangeable anymore. Claude is where I go to write and think clearly. ChatGPT is the most flexible all-purpose workbench. Gemini is my choice when I’m drowning in inputs and need a map fast. Most professionals should be using at least two. Here’s how I decide which one to open.
Someone asked me last week which AI model they should use. I gave the same answer I give every time: “What are you trying to do?”
They looked at me like I’d dodged the question. I hadn’t. It’s actually the only answer that makes sense right now.
Here’s what’s happened over the past year that most people haven’t fully absorbed: the major AI models have stopped trying to be the same thing. ChatGPT, Claude, and Gemini have each developed real, measurable strengths. And real weaknesses. The gap between what they’re each good at has gotten wider, not narrower.
Think about it this way. You wouldn’t use a Phillips head screwdriver on a flat head screw. Both are screwdrivers. Both are useful. But they’re built for different problems. That’s where we are with AI models in early 2026.
My Usage Shifted Dramatically. Yours Should Too.
Six months ago, ChatGPT handled 70% to 80% of my daily work. Today? Claude handles about 60%. Gemini gets 30%. ChatGPT gets the remaining 10%.
That’s not because ChatGPT got worse. It’s because Claude got meaningfully better at the things I do most, writing and analysis, and Gemini’s deep research pulled ahead for the work where I’m dealing with hundreds of pages of documents.
I’m not alone in this shift. Nate B. Jones, who runs one of the most respected AI newsletters and tests these models daily against real work, published his personal AI stack recently. He’s landed in a similar place: ChatGPT for analysis and logical structuring, Claude for writing and spreadsheet work, other specialized tools for browsing and coding. His big takeaway, and I keep coming back to this, is that benchmark scores don’t equal usefulness. What matters is how the model performs on your actual work.
Nathaniel Whittemore (NLW) from the AI Daily Brief has been tracking this same shift. His January Pulse Survey showed something I found interesting: 46% of respondents now pick Claude as their primary model. That’s a power-user audience, so don’t read it as a general population survey. But it tells you something about where serious usage is heading. A year ago, ChatGPT owned that spot by a mile.
The Three Personalities
Let me give you the simplest version I can of how these three models differ. I’ve used all of them daily for the past several years and run them side-by-side in live demos for law firms and other professional services organizations. Here’s what I’ve learned.
Claude: The Thoughtful Colleague
I’ll start with Claude because it’s where I spend most of my time now. The writing is better than the other models. Not a little better. Noticeably better. Give it examples of your work and it matches your voice in a way that ChatGPT and Gemini just don’t. It also reasons carefully through complex problems, and here’s the thing I really appreciate: when it’s not sure about something, it tends to tell you instead of making up a confident-sounding answer.
I’ve tested it head-to-head with the others on legal document analysis, and Claude consistently catches things they miss. Its deep research produces shorter, tighter reports (around 7 pages versus 30+ from the others), which I prefer because I actually read them.
Use it for any writing that will carry your name. Client memos. Reports. Articles. Also strong for careful analysis where precision matters more than speed.
The downsides? It can be too cautious. Sometimes it hedges when you want a direct answer. And while its Projects feature is excellent for organizing ongoing work, it’s still catching up on product breadth compared to ChatGPT’s wider toolbox.
On context, Claude gives most users a 200K token window, and unlike competitors, nearly all of that is actually available in the conversation. On the developer side, Opus 4.6 now supports 1M tokens in beta, so the ceiling is moving fast. Claude also recently added memory across conversations, though it doesn’t feel as central to the product as ChatGPT’s memory does.
ChatGPT: The All-Purpose Workbench
ChatGPT is the model I reach for least now, but I still reach for it every day. Here’s why: when I need heavy-duty reasoning, the Pro Thinking mode at the “heavy” setting still produces the deepest analytical output I’ve seen from any model. Period. For complex problems where I need the AI to really grind on something, that’s still my go-to.
The other thing ChatGPT does well is everything else. It has the widest set of integrations and add-ons of any model. The new Atlas browser. Image generation that’s the most consistent I’ve used inside a general-purpose assistant. And memory that actually works. Tell it something once and it remembers next time you show up. If you only want to learn one model and need it to do a bit of everything, ChatGPT is still the safest bet.
Where it falls short is writing. The default voice is generic and it loves bullet points. It tries to be everything to everyone, which means it rarely stands out at any one thing the way Claude does at writing or Gemini does at research.
And the context numbers are worth understanding. GPT-5 supports up to 400K tokens natively through the API, but inside the ChatGPT app the practical window depends on your plan and mode. Plus subscribers get about 32K. Pro and Enterprise users get 128K. Thinking mode stretches to 256K total. The full 400K is really an API story for now.
Gemini: The Research Powerhouse
This is the model I underestimated for the longest time. But when you need to throw a mountain of information at an AI and have it make sense of things, Gemini wins. Google’s 1 million token context window is real, and it matters. You can feed it entire codebases, full legal case files, or a dozen research papers in a single conversation. Gemini is the most widely available way to get that kind of capacity in a consumer product right now.
The deep research reports are the most thorough of the three. Sometimes 48 pages with 100+ sources. And because it plugs directly into Google Workspace, it can search your Gmail, Drive, Docs, and Sheets to pull from your actual business data. Not just the open web. That’s a big deal if your organization runs on Google.
The downside? The output can feel verbose and a little corporate. If you’re looking for a writing partner, this isn’t it. Creative work is its weakest area by a wide margin.
And here’s something that trips people up: the full million-token experience varies more than you’d expect. On the free tier, the context is much smaller. Google AI Pro subscribers get the advertised 1M in the Gemini app. But in practice, long threads can still feel like the model “forgets” sooner than the headline number suggests (a lot of estimates put the real conversation window at 32,000 to 64,000 tokens before it starts to “forget” in the gemini app). The app is juggling your conversation history, file uploads, and internal overhead all at once. The most reliable way to use the full long context is still through Google AI Studio or direct API access. I wrote about this gap a few weeks ago, and it’s worth understanding before you assume you’re getting the full million.
A Simple Decision Filter
When a client asks “which one should I use?” I walk them through three questions.
First: What’s the task? This is where most of the decision gets made. Writing something that carries your name? Claude. Thinking through a complex problem or brainstorming? ChatGPT. Processing a mountain of documents or doing deep research? Gemini.
Second: How much context do you need? Short question, single document? Any model works. Thousands of pages of medical records, financial statements, or a full codebase? Gemini’s context window gives you a real advantage at the consumer tier. Claude is the runner-up here and makes most of its capacity available in the conversation itself. ChatGPT’s usable window in the app varies by plan and mode, and it’s often smaller than the headline spec suggests.
Third: What’s your data situation? This is the question most executives forget to ask, and it matters the most. If the work involves sensitive client material, your first filter isn’t which model writes the prettiest output. It’s which platform your organization has approved, what data handling terms are in place, and whether the plan you’re on keeps your data out of model training. The best model is the one you’re actually allowed to use for the work in front of you. Approved data first, then choose the model.
If your organization lives in Google Workspace, Gemini’s native integration is a real advantage. Microsoft shops should look at Copilot (built on OpenAI’s and Claude’s models). If you’re not locked into either, Claude’s clean interface and Projects feature are worth a serious look.
What This Looks Like in Practice
Last month I gave a live demo to a 150-person law firm. Same case files, same prompt, five different model configurations. The task was analyzing workers’ comp medical records from a defense perspective, looking for pre-existing conditions, causation problems, and inconsistencies.
Claude identified the sharpest defense themes and organized its analysis in a way attorneys could actually use. ChatGPT’s heavy thinking mode produced the deepest analytical breakdown, but the output needed restructuring. Gemini processed the full file set faster and found details the others missed by connecting information across documents that were far apart in the record.
No single model won. They each contributed something different. And that’s exactly the point.
The Honest Tradeoffs
Let me be honest about the downsides of a multi-model approach, because there are some. It costs more. A pro subscription to each of the Big Three runs about $60 a month at the consumer tier. Premium tiers push that to $100 to $400 a month. For most professionals, two models is the sweet spot.
There’s also a learning curve. Each model interprets prompts differently, fails in different ways, and has quirks you only discover after a few weeks of daily use. Getting good with one takes time. Getting good with three takes more time than most people have.
And these rankings won’t hold forever. Models update constantly. What I’m telling you today could shift in a week or in six months. ChatGPT could close the writing gap. Claude could expand its tooling. Gemini could learn to write like a human instead of a committee. The specific picks will change. But the habit of matching the right model to the right task? That’s permanent.
What to Do This Week
Pick the two models that match your most common tasks. If you write a lot, Claude plus one other. If you research a lot, Gemini plus one other. If you need a dependable all-rounder, start with ChatGPT and add a specialist.
Run your hardest real task through two models side by side. Same input, same instructions. Compare the outputs. You’ll see the differences immediately.
Set up one Claude Project or ChatGPT Custom GPT for your most repetitive workflow. Give it your style guidelines, your templates, your examples. This is where the real time savings come from.
Check your data handling terms before routing client work to any model. Best output means nothing if the platform isn’t approved for the data.
The era of “one AI to rule them all” is over. The people who figure out the routing habit first are going to wonder how they ever worked any other way.
Pick your tools. Learn them. Use the right one.
Why I write these articles:
In this article, we looked at why picking a single “best” AI model is the wrong frame for senior leaders, and what to do instead. The market wants you to choose sides. The better move is a routing habit: know what each tool does well, match it to the task, and check your data policy before anything else. The noise is loud right now, but the decision itself is simpler than vendors want you to believe.
If you want help sorting this out:
Reply to this or email me at steve@intelligencebyintent.com. Tell me what your team’s current AI setup looks like and where the friction is. I’ll tell you what I’d test first, which part of the Claude/Gemini/ChatGPT stack fits your workflow, and whether it makes sense for us to go further than that first conversation.
Not ready to talk yet?
Subscribe to my daily newsletter at smithstephen.com. I publish short, practical takes on AI for business leaders who need signal, not noise.
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