You just demoed a brilliant AI feature. The boss is impressed, the team is buzzing, and the user smiles. Then the bill arrives. And suddenly, that beautiful demo becomes a profit black hole.
This is the dirty secret of AI products in 2025: stronger models don’t fix bad economics—they make them worse.
GPT-5.6 just made this impossible to ignore. OpenAI didn’t just release another flagship model. They dropped Sol, Terra, and Luna—three tiers with wildly different costs. Sol costs 5x more per token than Luna. And if you’re still defaulting every user request to the strongest model, you’re not building a product. You’re burning cash.
I’ve seen this firsthand. A startup I advised spent six months building an AI writing assistant. Every request went to GPT-4 (now Sol). The demo was flawless. But when users actually used it—daily, at scale—the unit economics collapsed. Each generation cost $0.03, and users weren’t willing to pay $0.10 per use. The product died.
The problem wasn’t the model. It was the product manager who never asked: does this task really need the best model?
Let me say this plainly: Defaulting to the strongest model is the single worst habit in AI product design. It’s lazy. It’s expensive. And it’s about to get you fired.
Here’s what you need to understand. GPT-5.6 isn’t a model—it’s a cost structure. Sol is for complex, high-stakes tasks. Terra is for everyday work. Luna is for cheap, fast, high-volume stuff. And the tiers within each (max, ultra) let you spend more reasoning when it matters.
But most product teams ignore this. They write ‘powered by GPT-5.6’ in the PRD and call it a day. That’s like buying a Ferrari to drive to the grocery store. Sure, it works. But you’re paying for speed you don’t need.
Product managers need to build a task routing map—not a feature list. Instead of ‘AI can write reports, make tables, generate code,’ you should have: ‘low-value tasks go to Luna, medium-value tasks go to Terra, high-value tasks go to Sol, and only the most critical ones use max or ultra.’
This isn’t technical. It’s a business decision. The product manager who can route tasks to the cheapest model that still delivers acceptable quality is worth more than the one who knows every API endpoint.
Let me give you a concrete example. A sales enablement tool I worked with processes meeting notes. 80% of meetings are routine—follow-ups, status updates. Those should go to Luna. 15% are strategic—they involve customer objections or new opportunities. Those go to Terra. 5% are critical—deals on the line, executive reviews. Those go to Sol with max reasoning. The result? Cost drops 60% without any user noticing the difference.
But here’s the twist: users don’t pay for the model. They pay for the outcome. They don’t care if you used Sol or Luna. They care if the answer is correct, fast, and usable. If you can deliver that with a cheaper model, you win. If you over-engineer, you lose.
This is where multi-agent systems become a trap. Everyone wants to build agent teams. But without clear task decomposition, acceptance criteria, and cost controls, you’re just adding more chaos. Multi-agent without accountability is just expensive confusion.
I saw a company demo an ‘AI analyst’ that used three agents: one to research, one to write, one to review. Sounded impressive. But when tasks weren’t clearly defined, the agents argued, duplicated work, and produced contradictory results. The fix? A simple routing rule: if the task has more than five variables, use a single agent with max reasoning. If it’s straightforward, use Luna.
So what’s the bottom line? Stop asking ‘which model can we use?’ Start asking ‘what is the cheapest model that can deliver a result the user will accept?’ That’s the real product work.
GPT-5.6 is a gift. It forces you to think about cost, quality, and task value. Ignore it, and your product will drown in operational costs. Embrace it, and you’ll build something that actually scales.
Here’s a checklist for your next AI feature:
- What is the task’s value to the user? (low, medium, high)
- What is the acceptable failure rate?
- What is the maximum cost per task?
- Which model tier can meet these constraints?
- How will you monitor and adjust routing over time?
Answer those five questions, and you’ll stop building demos. You’ll start building products.
FAQ
Q: Isn't the strongest model always better for user experience?
A: No. Users care about outcomes, not model specs. A cheaper model that delivers acceptable quality 95% of the time is better than a expensive one that delivers perfection 99% of the time—because the cost difference kills your unit economics. Users will churn if the price is too high, not if the model is slightly less capable.
Q: How do I actually implement task routing without making it complex?
A: Start simple. Categorize tasks into three buckets: low value (cheap, fast, high volume), medium value (standard quality needed), high value (must be perfect). Use a rules engine or a simple if-else logic in your backend. Monitor cost per task and adjust. You don't need an AI to route tasks—just a spreadsheet and a product manager who understands the cost trade-offs.
Q: Won't using weaker models hurt my product's reputation?
A: Only if you market it as 'we use the strongest model.' Instead, market the outcome: 'fast, accurate, affordable.' Users don't care about the engine under the hood; they care about the car getting them to their destination. If you can deliver 95% accuracy at 10% of the cost, that's a competitive advantage, not a weakness.