On it's face, there is useful content here. But it's also clearly contextual.
It's absolutely the case that routing between frontier models can improve results, mainly because of the alloying effect. Ping ponging between different providers gives the task exposure to different data distributions, and can break models out of non-optimal feedback loops.
That's not to say that's always the right approach, just not clearly wrong. And a small pool does not necessarily 'improve' model routing. The real advice is just 'know why you're routing to each model'.
Especially with guidance to map to improve based on performance - with a large enough volume of tasks/requests, you'd want to maximize the initial pool size to expand the search space in order to determine which is best at each task.
I read this as "here are some thoughts on model routing" -- not first principles I'd advise everyone to live by.
If we define GPT 5.5 and Opus 4.8 as the frontier models for simplicity, there is some value in routing between them theoretically because two models will always have some differences.
However, when the models have the same generalist profile capabilities and are at the same performance and cost tier, making a decision for when to route between them and also making sure that that decision is correct, requires enormously granular information. While there are benchmarks that show differences between the models across different domains and tasks, the differences are generally not major and we also cannot assume that benchmarks that we know are optimized for, because if the new model wasn't presented together with good benchmarks the business would tank, really reflect real-world task performance at the request-level.
So routing between similar models is an information problem that is unlikely to be solved.
Routing between these two models is also likely to have a lower benefit than routing between GPT and DeepSeek on the cost vector. Routing to DS has clear, known and verifiable impact on cost. There is no need to guess.
Similarly, if we routed between GPT and a specialized math model, lets say Leanstral, that we can assume outperforms GPT by >50%, the benefits are also massively larger, and the routing decisions are also easy to make.
This is why the biggest pay offs come from routing between models that have a 2-10x difference in one of the cost-speed-quality factors, or specialized in a specific domain, or runs locally for data-security sensitive work.
On it's face, there is useful content here. But it's also clearly contextual.
It's absolutely the case that routing between frontier models can improve results, mainly because of the alloying effect. Ping ponging between different providers gives the task exposure to different data distributions, and can break models out of non-optimal feedback loops.
That's not to say that's always the right approach, just not clearly wrong. And a small pool does not necessarily 'improve' model routing. The real advice is just 'know why you're routing to each model'.
Especially with guidance to map to improve based on performance - with a large enough volume of tasks/requests, you'd want to maximize the initial pool size to expand the search space in order to determine which is best at each task.
I read this as "here are some thoughts on model routing" -- not first principles I'd advise everyone to live by.
If we define GPT 5.5 and Opus 4.8 as the frontier models for simplicity, there is some value in routing between them theoretically because two models will always have some differences.
However, when the models have the same generalist profile capabilities and are at the same performance and cost tier, making a decision for when to route between them and also making sure that that decision is correct, requires enormously granular information. While there are benchmarks that show differences between the models across different domains and tasks, the differences are generally not major and we also cannot assume that benchmarks that we know are optimized for, because if the new model wasn't presented together with good benchmarks the business would tank, really reflect real-world task performance at the request-level.
So routing between similar models is an information problem that is unlikely to be solved.
Routing between these two models is also likely to have a lower benefit than routing between GPT and DeepSeek on the cost vector. Routing to DS has clear, known and verifiable impact on cost. There is no need to guess.
Similarly, if we routed between GPT and a specialized math model, lets say Leanstral, that we can assume outperforms GPT by >50%, the benefits are also massively larger, and the routing decisions are also easy to make.
This is why the biggest pay offs come from routing between models that have a 2-10x difference in one of the cost-speed-quality factors, or specialized in a specific domain, or runs locally for data-security sensitive work.
Why have you put all the content on the right column like this on desktop.
Because CSS is more difficult than machine learning, apparently.
it's arty