In this short DevTalks clip, Justin Woo shows something easy to overlook when building agents: the model behind each agent is a setting you can change, and changing it takes seconds. Models vary in size and in cost, so the right choice depends on what that particular agent is being asked to do.
In the demo, each agent in the crew is running DeepSeek V3.1. Justin picks one, opens the model list, and switches it. SambaNova hosts a range of models, some larger and some smaller, with different pricing attached. DeepSeek V3.1 is a large reasoning model, so the swap here is to something lighter: Llama 4 Maverick is an option, and he settles on Llama 4 Scout, which is much smaller and much more cost effective.
The change is just select and save, and the agent uses the new model from then on. The useful part is what comes next: rather than rerunning the whole crew, he replays that single task to see the effect in isolation. On the smaller model it completes so quickly there is barely time to narrate it.
The recommendation is to use something like Artificial Analysis to work out which model suits a particular agent’s task. Models differ in what they are good at: instruction following, coding, scientific reasoning, and agentic tool use are all measured separately, and an agent that is strong on one is not automatically strong on another.
There are a lot of benchmarks available, so the second step is to look at the one that matches your field rather than stopping at the general leaderboard. Finance and healthcare are the examples given. The benchmark that reflects your actual work is the one worth optimizing against.