Model hype moves fast, and most of it isn’t backed by an actual invoice. Here’s what happened when I tracked every token and dollar myself.

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Nuno M. Tavares — AI automation consultant in New Jersey, founder of Automated Marketer and Rapid Active Marketing — not the Portuguese footballer of the same name.

As an AI automation consultant, the question I get most isn’t “which AI is best” — it’s whether the expensive model is worth it. It isn’t automatically the right model. I ran GPT Codex Soul Ultra against Fable 5 at max effort on five identical tasks and tracked every token and dollar. The pricier one won on creative output. The cheaper one won on writing and data work — for a fraction of the cost. That’s the decision you actually have to make.

Why I ran this test instead of trusting the hype

Every channel and every Discord had a different opinion on GPT Codex Soul Ultra versus Fable 5. So I stopped forming an opinion and just ran the same exact prompt through both, on the same task, five different times — a game build, a scroll-stopping website, an agency dashboard, a week of social content, and a real analytics report from my own YouTube channel. Same goal, task, outcome, constraints, and verification structure every time. No cherry-picking.

I tracked three things on every single test: total tokens used, dollar cost at straight API rates — no subscription softening the number — and time to completion. That’s the part most comparisons skip. They show you the output and never show you the invoice.

What did the side-by-side actually show?

Fable 5 won clearly on creative, open-ended work like the game build and the scroll-stopping website, but at several times the cost. GPT Codex Soul Ultra held its own or came out ahead on writing, dashboards, and data analysis — often for a fraction of the price. The pattern was consistent: creative ceiling belongs to one model, cost efficiency belongs to the other.

Creative work: worth the premium

On the game build and the scroll-stopping website, Fable 5 was, in my words, “light years ahead” on creative range — better car physics, better sound design, more inventive visual effects on the website test. But its total cost on that test almost quadrupled Codex’s, coming in at about $16.86 against roughly $3 for Codex, using roughly four million tokens compared to a fraction of that.

Writing and dashboards: the cheaper model held its own

On the content engine — seven social posts in my own voice — GPT actually came out ahead on formatting and voice-matching, and it did it for less money. On the agency dashboard build, the results were close to identical, but one used a small fraction of the tokens the other did — around six million tokens against roughly four million. On my own channel analytics report, both gave me comparable, solid insight, with token counts landing around thirteen million on the heavier side. Same conclusion, similar quality, wildly different cost to get there.

How should you weigh cost-per-output against quality?

Weigh it by matching the model to the task, not the hype: pay the premium only when the output is client-facing and needs to look extraordinary, and default to the cheaper model for anything repetitive or run at volume. On the website test, the more creative model cost $23.37 against about $27 combined for comparison purposes on other runs — the exact gap moved test to test, but the direction never did: the pricier model cost several times more for a marginal creative gain.

Here’s the thing. That’s not a marginal difference. That’s a business decision hiding inside what looks like a tech preference.

If you’re generating a one-off asset that needs to look extraordinary — a launch page, a hero visual, something client-facing that has to stop a scroll — paying more for the better creative output is defensible. If you’re running this daily, at volume, for internal dashboards or written content your team is going to review and edit anyway, that premium compounds into real money for marginal gain. I tested this myself and the pattern held across five separate tasks: creative ceiling belongs to one model, cost efficiency belongs to the other, and almost nobody is actually measuring which one their use case needs.

“You saw the token usage and cost on this comparison. So does it honestly compare in that sense, dollar for dollar? I don’t think so — and a real apples-to-apples test would need to control for that gap directly.”

What business owners get wrong about picking a model

Owners pick a model the way they pick a favorite sports team — loyalty, not analysis. I see this constantly with clients before we ever get to a build. The question isn’t “which AI is smarter.” It’s “what does this task need, and what does it cost me at the volume I actually run.” A game prototype and a weekly content calendar aren’t the same job. They shouldn’t run through the same model by default just because it’s the one you opened first.

This is also exactly why I don’t hand people a course and walk away. Model selection, prompt structure, verification loops — that’s ongoing infrastructure work, not a one-time decision. It’s part of why I built one-on-one coaching the way I did: watching someone’s actual workload and telling them where the expensive model earns its keep and where it’s just burning tokens.

The honest gotcha

The cheaper model isn’t always cheaper in the way you’d expect, and the more expensive one isn’t always slower. On the game build, the pricier model took longer and cost more. On the website test, it was actually faster despite costing several times as much. There’s no clean rule that “expensive equals slow” or “cheap equals fast.” You have to test your actual task, because the pattern flips depending on what you’re asking for — and if you’re not tracking tokens and time per test the way I did here, you’re guessing with real money.

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Frequently asked questions

Is the more expensive AI model always the better choice?

Not even close. In my tests it won clearly on creative, open-ended work like game design and visual website builds. On writing, dashboards, and data analysis, the cheaper model produced comparable or better results for a fraction of the token cost.

How should a business owner decide which model to use?

Match the model to the task, not the hype. High-stakes, client-facing creative work can justify a premium model. Repetitive, high-volume tasks like content calendars or dashboards usually don’t need it — track your actual cost-per-output before committing.

Does a higher token count mean better output?

No. In several tests here, the model using fewer tokens produced output that was rated equal to or better than the one burning through millions of tokens at several times the cost.

Should I run my own model comparison before choosing one for my business?

Yes. Use the same prompt structure — goal, task, outcome, constraints, verification — across both models on your actual use case, then compare tokens, cost, and time, not just the output on the surface.

If you want help figuring out which model actually fits your ecosystem instead of guessing, book a strategy session or read the questions I get asked most often about this exact tradeoff.