Price per token is the wrong scoreboard
Everyone compares AI models using the same number: price per token. Or worse, the newest model and the highest parameter count.
Once you are running AI in production, that is not really what matters. What matters is how much it costs to get one output that actually passes.
Take an email. An AI agent reads the message, figures out what the sender wants, and writes a 150-word reply. That reply has to understand the intent, answer the important points, sound professional, avoid making things up, and stay within the required length.
If it passes, great. If it fails, the agent tries again. And every retry costs money.
First-pass accuracy is not the same as cost to finish
Using our planning estimates, the first-pass success rates for this email task are about 90% for Fable 5, 88% for Opus 4.8, 87% for GPT-5.6 Sol, and 85% for GPT-5.5.
At first glance, Fable looks like the winner.
Now look at the cost of getting one email that actually passes. Claude Opus 4.8 lands at about $0.0085 per successful email. GPT-5.6 Sol is about $0.0098. GPT-5.5 is about $0.0100. Claude Fable 5 is about $0.0167.
Put another way, for every 100 successful emails that is roughly 85 cents with Opus, 98 cents with GPT-5.6 Sol, about one dollar with GPT-5.5, and $1.67 with Fable.
That is the interesting part. Fable has the highest estimated chance of getting the email right on the first try. A successful email still costs almost twice as much as Opus.
Choosing a model because it is newer and more accurate is not always correct. You can have better accuracy and still pay significantly more for the exact same completed task.
Retries and cost per attempt decide the real bill
The real cost depends on two things: what each attempt costs, and how often you have to try again.
Opus costs about $0.0075 per attempt. Once you account for expected retries, that becomes about $0.0085 per successful email. GPT-5.6 Sol starts at roughly $0.0085 per attempt and lands around $0.0098 per successful email. GPT-5.5 costs about the same per attempt, but with a lower estimated pass rate it ends up closer to one cent per successful email. Fable starts at about $0.015 per attempt. After retries, that becomes roughly $0.0167 per successful email.
These are planning estimates for one constrained email task, not a claim that one model always wins. The pattern still holds: newest and most accurate is not the same as cheapest path to a passing output.
Ask what passed, and what it cost to get there
Instead of asking "Which model is best?", ask a more useful question: "Did this output actually pass, and what did it cost me to get there?"
4loop evaluates the output before the workflow moves forward. If it passes, the agent continues. If it fails, you can retry the same model, switch to another model, run a second AI review, or send it to a human when the decision needs judgment.
The real economics of AI are not just cost per token. They are cost per successful outcome.
For this email task, the difference is about 85 cents versus $1.67 for every 100 successful emails, depending on the model. That might not sound like much at 100 emails. At 100,000, or 10 million, the gap gets expensive fast.
It is the model and the workflow that get you to a successful outcome for the lowest total cost.