Weighing Your Options for Model Selection

00:00 While the AI landscape changes literally every day, there’s one thing I’m sure will remain constant. There’s no universally best model or provider. Higher quality can often mean higher cost.

00:12 Faster responses can mean lower output quality, while more reliable systems may come with higher overhead. There’s a famous adage in engineering: fast, cheap, and good. You can only pick two.

00:24 When deciding on models and budget, consider what matters most for your application. Response quality? Cost per request or token?

00:33 Latency? Throughput or capacity? Or reliability? And no, all of the above is not an option. That’s the tricky part about priorities.

00:42 Here’s a few common examples. Batch processing: dealing with large quantities of data? Typically, you’ll want lower cost. A customer-facing chat application?

00:52 You’d want to prioritize low latency and a fast response time. Critical production systems you can’t afford to go down? Well, then reliability is paramount.

01:02 How about a premium feature? Think a coding agent that has the freedom to modify your local files at will or even push to GitHub. You’re probably going to want to prioritize output quality over everything.

01:13 If you think of it this way, there is no one-size-fits-all model.

01:18 So when choosing models with OpenRouter, prioritize your ideal behavior first with the user in mind. Decide which trade-offs are acceptable. What are you willing to sacrifice?

01:29 Latency? Price? Throughput? Use routing to enforce your priorities automatically, and make sure to configure fallbacks for when your preferred options become unavailable.

01:40 Thankfully, using OpenRouter, these decisions aren’t set in stone either, and you can continue to experiment until you find what works for you. Now let’s wrap things up in the summary.