I stopped asking which AI model is best and started asking what each job needs.
In a July 2026 live stream I had maxed out two top models in about 48 hours. My AI bill had gone from about $10 a month to about $1,200. That number changed how I pick models. The model names and prices below are as of July 2026 and they move fast, so lean on the method and recheck the vendor pages.
The business case
If you're a solo founder or a small team, every token you spend has to come back as revenue. Otherwise you're wasting money. I watch the tech industry spend heavily on AI tokens without seeing the return in dollars, and a small company can't afford that.
I sort the work first and pick the model second. Then I let the expensive model do the thinking, hand the typing to a cheaper one, and check everything before an automated test locks it in. The same cost discipline applies to what you charge your own customers.
Three layers of work
I see developers grab the newest model for everything, including questions a cheap model answers fine. I split AI work into three layers:
- Business: research, slides, docs, spreadsheets and CRM work.
- Coding: testing, migrations, pull requests and GitHub issues.
- Automation: scheduling and the jobs that run on their own.
You don't need the most expensive model for all three. I keep the top model in ChatGPT, the newest one at the time, for the most complex, multi-step problems. On the stream I called it the one that gets the job done and is far more token-efficient. Claude Fable 5 from Anthropic, which I use through the Claude app, was hungrier for tokens and clearly better at design.
Watch the quiet bills too. I barely touched GitHub Copilot that month, only for commit messages in VS Code, and the bill was already about $190. Most of it turned out to be VM overage. I started questioning the enterprise plan. I wrote more about measuring this in prove the AI ROI.
Plan big, build small
This is the pattern I credited to another YouTuber, and it saves tokens. I ask the strong model to plan the feature step by step. In the instructions I tell it the plan goes to a less capable AI model, so every step has to be specific. Then it creates the GitHub issues and links them together, and a cheaper model does the build.
When I tried it, the older model finished in one shot work it normally stumbled on, because the plan was that good. Without the plan, going back to the older model felt like I had to keep telling it what to do. I covered the same plan-first idea in my Spec Kit alternative for large codebases.
The biggest example was an accounting rewrite for my construction software. One epic, 12 tickets, integrations with QuickBooks Online and Desktop, Xero, FreshBooks, Wave, Zoho Books and others, with two-way sync of customers, invoices, payments and purchase orders. The instruction was "take this epic, implement all the details, tell me when it's done." It even wrote a Rust app for the QuickBooks Desktop piece. That run used the high effort setting, and the biggest part ran on extra-high for about an hour. A week earlier I would have built each integration separately.
My GitHub graph showed it. I averaged about 25 commits a day before, around 45 on a big day. I passed 100 commits in the roughly 48 hours after Claude Fable 5 came out, and kept about 75 a day on vacation, working only early mornings and late evenings.
Jump to
- 0:00 ChatGPT and Claude Fable 5 after maxing both out
- 7:48 Commit stats and the three layers of AI work
- 19:18 One-shotting a 12-ticket integration epic
- 24:00 Plan with the big model, build with a smaller one
- 27:34 Owning integrations, browser testing and design
- 41:02 Founder hot seat: focus, niches and tying products together
- 1:16:56 My AI coding tools, and why $29 beats $9
- 1:31:58 Replit lock-in and pricing a resume builder
I show the epic, the commit graph, the tests and the pricing math on screen in the full stream on my channel: my 2026 AI coding stack, live.
Own your integrations
In 2012 my team spent months on a QuickBooks Desktop integration and still fought two-way sync conflicts. Integration firms wanted $50,000 and up, which a startup can't spend before a client pays. n8n, Make and Zapier are the three big automation tools, and we connect to them. For about 90% of what I need, I now build the integration myself, and each one is a Cloudflare Worker. Facebook lead-form leads come straight into my system, which can send a text or WhatsApp message on its own. QuickBooks pulls in the customer's accounting data.
Test it twice
After a feature is built, the AI spins up a browser and clicks through it. The ChatGPT model was very good at this. Then I test it myself as a human. Only after that does the AI write an automated test, which runs in GitHub Actions on every deployment. I don't write those tests by hand, and I have the confidence to deploy.
Give design a spec
The LeadScore AI sign-up page design came from Claude Fable 5 with little effort. I took a theme from 21st.dev and handed its design language to the AI as a specification. That kept the design on track.
I don't plan to use the cheaper ChatGPT models. I'd rather pay for the best model and step the effort down from high to medium when a task is lighter.
Own the code
I've tried Lovable, Bolt, v0, Google Stitch and Google AI Studio. My main drivers are the ChatGPT desktop app, where I run Codex, and the Claude app, on the top plan of each. If you build on Replit or a platform like it, get a decent foundation, then export it and keep iterating somewhere you control. Over time you'll keep paying the platform for the same fixes, and I said AI may cost more next year.
Price for the math
A viewer had dropped from $29 to $9 a month, and I ran the numbers live. To reach $10,000 a month you need about 345 customers at $29, and about 1,112 at $9. That's 767 more customers for the same revenue. At three lives a week and two sign-ups per live, $29 takes about 57 weeks and $9 takes over three years. If a higher price doesn't sell, you can lower it. From $9 there's nowhere to go.
The stack exists to make money. Sort the work, pay for the top model where it earns it, and charge enough that the bill makes sense.
I work through this kind of thing live every week, with real code and real numbers. Come build with me on the weekly live.
About Dwain
I'm Dwain Browne, Founder of LeadScore AI and SnapSuite. I'm a Toronto-based software architect with 25+ years across software, sales, operations and entrepreneurship. SnapSuite helps contractors and trades teams get paid faster, and LeadScore AI shows you which leads deserve your next call. I also run Vibe Code to Profit, a community helping people build software with AI.