We’ve seen two large and extremely capable swarms from OpenAI in the last few months:
No doubt we will soon see even larger swarms with even more impressive capabilities. But they are not cheap. It is estimated that the swarm of 10,000 agents cost about $20 million at API prices. So while they are very powerful, it will be some time before we see the million-fold reduction in cost needed for this level of power to be possible on a $20/month plan. We should be thinking of it as a grand demonstration of what is possible when money is little constraint — like AlphaGo — rather than a new level of performance for the same cost.
A good way to see AI swarms is as a new form of inference-scaling. The main form of inference-scaling at the moment is having the agent spend more and more time on the task — increasing the maximum length of its chain of thought before it needs to give a final answer. This buys more capability, but at an increasingly expensive price. As measured by performance on maths benchmarks, this performance increases only logarithmically with the amount of compute used. I’ve previously shown that climbing from about 20% to about 80% on a reasoning benchmark typically requires scaling up the length of the chain of thought (and thus the number of tokens, the amount of compute, and the cost) by roughly 100x.
How do capabilities scale if we instead increase the number of agents in the swarm? There isn’t much data on this — especially for frontier systems like OpenAI’s recent swarms. But OpenAI’s launch post for GPT 5.6 Sol includes some charts containing just enough information to allow one to tease-out an answer.
The chart below shows the performance of three sizes of swarm as their chains of thought are lengthened. Each swarm size displays the usual kind of steep diminishing returns to more reasoning tokens.
Note how the single-agent ‘swarm’ (in light blue) is the most efficient, reaching each level of capability for far fewer total reasoning tokens. Indeed, it appears to use about half as many total tokens as the 4-agent swarm, which uses about half as many as the 16-agent swarm. If we redraw this graph on a logarithmic x-axis, we can see this more easily:
Now we can clearly see that the scaling curve for each swarm-size has logarithmic returns to longer chains of thought (because they are straight lines when plotted on a logarithmic x-axis) and that they have roughly equal slope, meaning that the scaling dynamic remains the same for all these swarm sizes.
We can also see that the 4-agent swarm is stably requiring about twice as many total tokens as the 1-agent swarm to get the same performance, and that the 16-agent swarm is needing roughly twice as many again.
But we don’t yet have a chart that shows how capability increases if we just scale swarm size (leaving the chain of thought length fixed). The experiments OpenAI ran didn’t include this. They didn’t run different swarms of different sizes with exactly the same chain of thought length to see what would happen.
Luckily, we can simulate this from their data. Let’s use the same starting point they did — 1 agent with its lowest reasoning level. Then we ask what would happen if we used a 4-agent swarm with the same average tokens per agent (=4x the total tokens). We can find that point on the 4-agent curve. Because the curve is so straight, the interpolation should be quite reliable. We can then ask what would happen if we scaled up to 16 agents, without increasing the average tokens per agent, by finding the point on that curve with 4x as many total tokens. Let’s plot these in green on the same chart:
We can now see how much we get from purely increasing the number of agents ( swarm scaling ), and how it compares to purely increasing the length of the chain of thought ( duration scaling ). The swarm scaling gives a little over half as much gain in capability for the same scale-up of compute. Or put another way, you need to do the scale-up of compute twice to get to the same capability, squaring the total multiplier needed.
Economists have a nice way of thinking about this. They have studied how having many people work on a task can get it done sooner, but usually at the expense of more total person-hours of labour. 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Here is a parameter measuring how parallelisable the task is. They call it the ‘stepping on toes’ parameter. If = 1, you have a perfectly parallelisable task, with no stepping on toes and no efficiency penalty. But in reality is usually between 0 and 1 — allowing more people to help, but with diminishing returns. For example, if = 0.5 then 100 people working together get as much done as 1 person working for 100 = 10 times as long.