You’re Probably Using the Wrong AI Model for the Job
This is part ten of our Putting AI to Work in Your Tour Business series. Watch the full series here and read part one here.
There’s a decision you make a dozen times a week without realizing you’re making it. Every time you open ChatGPT, Claude, or Gemini and start typing, you’re choosing an AI model. Most operators choose the same one every time, by default, because they never noticed there was a choice to begin with.
That small, invisible decision affects two things you care about: the quality of what you get back, and what it costs you. And the framework for getting it right isn’t complicated.
The Cars in the Garage You Did Not Know You Had
Open up Claude and look in the bottom corner. There is a little dropdown: Opus, Sonnet, Haiku. ChatGPT and Gemini have the same idea under different names. Those are different models, and the versions and names are changing all the time. When you start a new task, whether you are brainstorming a tour, drafting an email, classifying a thousand reviews, or processing a document, you are probably reaching for the same model for all of it.
Match the model to the stakes of the task, not to whichever tab happens to be open.
Think of it like this. You didn’t even realize you had a whole garage of different cars. One is a Ferrari built for the hardest, fastest driving. One is a reliable everyday car that handles most of your trips. And one is a scooter that is cheap to run and perfect for short hops. If you only ever drive the Ferrari, you’re overpaying. If you only ever ride the scooter, you’re going to get stuck the moment the job gets heavy.
The Three Tiers, in Plain Terms
Every provider gives you roughly two or three tiers. The labels differ, the logic is the same.
The top tier is deep thinking. This is Claude Opus, the ChatGPT thinking mode, or Gemini Pro. These models pause, reason through a problem, and hand you a considered answer. They cost more, they take longer, and they’re what you want for the hardest, highest-stakes work.
The middle tier is the workhorse. Claude Sonnet, the standard ChatGPT and Gemini models. Fast enough for the bulk of the day-to-day work that fills your week, optimized for the tasks most of us ask for most often, and priced right in the middle. Honestly, this is where the majority of your AI use should probably live.
The bottom tier is cheap and fast. Claude Haiku, the ChatGPT mini models, Gemini Flash. These are light and built for volume. They are great at a narrow set of jobs like classifying, summarizing, or pulling a quick answer. You would never hand them your pricing strategy, but for high-volume tasks they are perfect, and you can run them all day and barely notice the cost.
How to Decide, Task by Task
Here’s the rule: The higher the stakes, the more powerful the model.
Reserve the top tier for the work where being wrong costs you. Things like quarterly planning, reworking your pricing, a big data analysis, and copywriting that has to land. When we were building out the AI brain in this series, we told you to use the most powerful Opus model inside Claude Cowork, because that was a high-stakes, complex job and we wanted the strongest reasoning available.
Send the mid-tier most of your daily operations. These are content drafts you know you will edit anyway, internal briefs, email triage, tour description edits, and meeting summaries. These models have been optimized around exactly this kind of work.
Push the bulk work down to the cheap, fast models. Summarizing a meeting transcript into a to-do list, tagging a batch of photos, writing alt tags for an SEO project. Volume tasks where speed and cost matter more than deep reasoning.
You don’t need a Ferrari to go to the grocery store, and a scooter is never going to pull a two-ton boat.
A Quirk Worth Knowing: You Often Cannot Switch Mid-Chat
Here’s where it gets a little odd. In most tools, you cannot change models partway through a conversation. So if you want your most powerful model to do the thinking and a cheaper model to do the execution, you need a workaround.
What I do is ask the powerful model to take a beat and build a plan first. Instead of dumping a half-formed request and letting it run off and execute, I ask it to analyze the project, give me an outline, break it into steps, think through the risks, and pull the context it needs from the AI brain. Then I ask it to save that plan. When I start a fresh session with a different model, I just point it at the saved plan by name, and it picks up right where the thinking left off.
That same approach lets you build smarter workflows. Picture a weekly piece of content. One model does the research. A more powerful model analyzes it and does the copywriting. Another model handles a later step. Then at the very end, you bring the powerful model back to run the final review against your checklist. Different models for different steps, each matched to what that step actually demands.
Two More Options Most Operators Have Not Considered
If you want to reach beyond what one provider offers natively, a marketplace like Replicate plugs into Claude Cowork or Claude Code and lets you tap specialized models from other companies. Image generation, audio from a voice provider, video, niche coding or transcription models. You don’t need to learn the ins and outs of it today. Just know it is there when you need something specialized that your main tool cannot do on its own.
There are also open-source models you can run locally, right on your own laptop or desktop. No monthly fee, and often no data leaving your machine. That matters for three reasons: privacy, because sensitive client data stays on one device; cost, because you sidestep some of the usage charges from the big providers; and independence, because no provider can change the terms on you. The trade-off is setup effort and performance tied to your hardware. For most operators it isn’t worth the hassle yet, but if you are hitting usage limits or running a lot of bulk work, it’s good to know the option exists.
Where This is All Heading
Part of the reason this decision layer matters right now is cost. AI is unusually cheap at the moment, and a big reason is investor money flooding in to get people using these tools. That will not last forever. As adoption keeps climbing and millions, then billions, of AI agents run around the clock, the price of running them is likely to rise. We will also get more efficient models that do the same work for less, but the era of nearly-free intelligence has an expiration date.
Honestly, this whole decision layer will probably fade. The smartest models are already starting to auto-route tasks to the right specialist behind the scenes. Before long you may just ask for an outcome and let the system pick the tier, the specialized model, and the connectors for you. Until then, understanding how these models differ saves you money and gives you insight into what those more powerful AI systems of the near future will be building on your behalf.
So next time you start a session, take a beat and ask: Is this deep thinking, high stakes, complex? Start with your most powerful model and ask it for a plan. You can even ask it which model to use. If it is a quick daily task, the mid-tier has you covered. And if it is fast, bulky, data-heavy work, that is what the small models are for.
If you want one-to-one help putting AI to work in your business, book a free strategy call with one of our coaches.



