I’ve lost count of how many hours I’ve burned trying to decide which AI subscription actually deserves my money. Every comparison post online reads the same: benchmarks, charts, a shrug at the end. Nobody just tells you what to pay for and why.
Then I came across this post from a LinkedIn creator who stopped theorizing and just ran both tools side by side for real work. His conclusion is refreshingly blunt. He pays for both, but for completely different jobs, and he broke down exactly why in a way that finally made the choice obvious.
The whole thing boils down to one line from the original poster:
Pay Claude $100/mo, it does the work. Pay ChatGPT $20/mo, it makes the images.
That’s $120 a month total, and the expert says he’d spend it exactly the same way again. Here’s his full reasoning, broken down.
1. Port your skills before you test anything
This is the tip I wish I’d known months ago. Instead of rebuilding your entire setup from scratch when you try a new tool, the author says to just migrate what you already have. He hands ChatGPT any existing Claude skill with this prompt:
“Make a skill from this [Skill name]. Just update Claude to ChatGPT.”
That’s it. Your whole configuration transfers over. As the creator puts it, you start at expert level instead of fumbling through beginner mode for a week. It also means your comparison is actually fair, because you’re testing the same workflows on both platforms rather than a polished setup against a blank slate.
2. The 30-combo math, decoded
This is where most people get lost, and honestly the numbers explain why. The original poster counted it out: 2 models (ChatGPT 5.5 and 5.6) multiplied by 3 flavors (Sol, Terra, Luna) multiplied by effort levels (Light through Extra High) multiplied by 2 speed settings. That’s a menu nobody asked for.
His practical filters cut through it fast:
- Fast mode costs 1.5x the price. Ask yourself whether the speed is worth the premium on that specific task.
- Skip Terra entirely. The author’s reasoning made me laugh: nobody actually knows what a “medium task” is. Middle options exist to look complete, not to get picked.
- Sol-Ultra eats your usage. His fix is a two-step move: run a couple of turns on Sol-Ultra to plan the work, then switch down to Luna-High to execute it.
That last one is the real gem. You get the heavyweight model where thinking matters and the efficient model where volume matters. I’ve started doing the same thing and my usage limits stopped screaming at me by Wednesday.
3. The enterprise trap nobody screenshots
This section genuinely surprised me. The expert laid out numbers that most teams only discover after they’ve already signed.
ChatGPT Enterprise requires 150 seats minimum. So a 150-person company pays roughly $3,000 a month in seats plus another $17,000 a month in tokens. Run Sol on High across that team and the post’s author puts the bill at $37,000 a month.
Compare that to Claude under 150 seats: $100 flat. The catch is seat number 151, which flips you straight into pay-per-token territory.
His two rules of thumb:
- Under 150 people: take the flat seats today, before anything changes.
- Near 151: budget for tokens before you hire, not after.
Why this matters: your AI bill can jump 12x on a single new hire. That’s not a pricing tier, that’s a cliff. Knowing where it sits changes how you plan headcount.
4. Steal his team image setup
Simple, and it saves real money. The contributor doesn’t buy an individual seat for every person who occasionally needs image generation. One shared $20 ChatGPT account covers the whole team.
Think about how image generation actually gets used at most companies. Someone needs a header graphic for a deck. Someone else wants a quick mockup. It’s bursty, not constant. Paying per person for a tool that sits idle most of the week is how software budgets quietly balloon. Match the license model to the actual usage pattern and you keep the cash.
5. The tiebreaker if you’re still stuck
My favorite point, because it cuts against everything the comparison industry sells you. The author’s advice when you genuinely can’t decide: pick the one your team already opens daily.
He points out that every company has one AI superman, that one person who’s already deep in a tool and pulling others along with them. Follow that person. In his words, adoption beats every benchmark.
I think he’s completely right about this. A slightly weaker model that people actually use every day will outproduce a marginally better one that sits unopened in a browser tab. Tools only generate value at the point of habit.
How to apply this today
If you want to test his approach yourself, here’s the sequence:
- Port your existing skills or custom setups to the second tool using his prompt, so both start on equal footing.
- Ignore the middle-tier model options and pick one heavyweight plus one efficient workhorse.
- Plan on the expensive model, execute on the cheap one.
- Count your headcount against that 150-seat line before you sign anything.
- Share a single low-tier account for occasional tasks like image generation.
- When the analysis stalls, go with whatever your team already opens.
The broader lesson here goes beyond these two specific tools. As AI pricing splits into flat seats versus consumption billing, the smart move stops being which model is best and becomes which billing model matches how my team actually works. This industry pro figured that out by testing instead of reading.
Go read his full LinkedIn post for the complete breakdown and the numbers behind each point. Worth the five minutes.