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Jagged capabilities: no single model dominates all tasks

From The State of Model Routing — NVIDIA, Cognition, OpenRouter · ≈5:49

“I would also like to encourage everyone to think there are jagged capabilities in most models, right?”

“So, not it's not necessary that model A if scores higher on a coding benchmark, is just plain better at every task that is.”

“I would I would encourage everyone to think, "Hey, models are strong at different things, rather than like there's one model to rule them all."”

What’s in it
  • Explains why one 'best' coding model doesn't exist across all tasks
  • Breaks down jagged capabilities across data viz, modeling, and prediction work
  • Argues effective AI routing requires knowing each model's specific strengths
Clip transcript
have been from the Frontier model. >> I would also like to encourage everyone to think there are jagged capabilities in most models, right? So, coding is not one domain. Within, let's say data visualization, there'll be scikit-learn, there'll be matplotlib, there'll be something else. It largely comes down comes down to the training corpora that went into each of the models, so right? So, one model, while you're trying to do X type of work, let's say data visualization, and the other type is Y, that means let's say model building. Let's say you're trying to have a data science work stream, where you're trying to optimize for some kind of prediction, and then visualizing your results. Within that task, different models will have different strengths. So, not it's not necessary that model A if scores higher on a coding benchmark, is just plain better at every task that is. So, routing is a task of intimately intimately understanding of behavior of and strengths and weaknesses of different models, and then applying them thusly, right? I would I would encourage everyone to think, "Hey, models are strong at different things, rather than like there's one model to rule them all." >> I see. And by the way, real quick, thank
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