Robert Ta and Hamel compare hybrid AI setups where expensive frontier models plan and cheap models execute. Fable 5 generated poor plans that forced workers to consume 6.7 billion tokens, costing $9,373 total. Opus 4.8 planning kept worker costs to $411. Match model capability to task complexity to avoid wasting tokens on routine work.
Few moments in a large task genuinely require frontier intelligence, such as the original decomposition, the design decisions, and certain trade-offs. Once a frontier planner has collapsed the ambiguity into a detailed explicit instruction, less expensive models simply have to follow it. This is a huge potential source of cost savings. In the run that used GPT-5.5 for both planners and workers, the workers alone cost $9,373. In the run where Opus 4.8 did the planning, we just read this, and Composer 2.5 did the work, the entire worker fleet cost $411. One detail worth noting comes from the comparing of the 2 hybrid runs. The Fable 5 planner ran up against a slightly smaller bill than the Opus 4.8 planner, despite roughly twice the per-token price, because it used far fewer planning tokens. But the Fable's run workers went through several times as many tokens, and the run as a whole came came out substantially more expensive. That's really interesting that the Fable 5 planner put up a smaller bill than the Opus 4.8 planner, but its plans seemed to be garbage because then the workers had run more loops is what I'm looking at this run, went through several times as many tokens and the whole run came out to be substantially—
Let's look at the graph again. So it's saying like, yeah, I think it's looking at the last one here. Like— No,
no, I think it's saying the hybrid runs. So in these cases, Fable 5 plus Composer 2.5 versus Opus 4.8 plus Composer. 2.5. And so you could see you came out with 6.7 billion tokens used of, and the workers, it's saying from the plan Fable 5 came up with, the workers just ate up a lot more cost and compute. That's interesting. And in this case, they're saying that this is the controlled comparison because it doesn't have an asterisk. So it's really curious to see this huge difference.
I mean, the thing that you should take away from this article really is like, don't use the frontier intelligence. on everything. It just sounds very commonsensical when you hear it come outta my mouth, but like a lot of people are not doing that. You're just getting into your coding agent and putting it on the highest mode possible and you're just going about your day.
Token maxing.
Yeah. And so I think, yeah, this just shows like, hey, there's a lot of money and time you can save by like using the right thing for the right task.
Yeah. So for the AI builders out there, Hamel, could you summarize again, like some of your thoughts of this token economics, like agent swarm economics article from Cursor? Like what should AI builders be thinking about when it comes to maximizing the value of their tokens with this agent swarm method?
Yeah, I mean, it looks like you should just use the right model for the task. Like there's just like the article says, like you don't need frontier intelligence for everything. So don't use it for everything because frontier intelligence is like significantly more expensive. Than just like one level down. If you go like a few levels down in intelligence, now all of a sudden it's like, I don't know, like 10, 20 times cheaper. So yeah, I mean, I think we already knew that, but it is good to quantify this.
