The most common mistake in AI engineering is reaching for the largest, slowest, most expensive model for every task. The better question isn't "which model is best?"it's "what does this task need?"
Most workloads fall into three buckets.
1. Fast intelligence (high-volume, latency-sensitive)
Classification, extraction, routing, search, and high-throughput agent steps. Here, speed and cost dominate quality. A fast, grounded model that answers in a few hundred milliseconds beats a slower one that's marginally smarter.
2. Adaptive orchestration (connected, multi-step work)
Most real product work, drafting, planning, using tools, coordinating across steps, wants a balance of quality and speed. This is the sensible default for anything that touches multiple tools or runs for a while.
3. Maximum depth (consequential decisions)
Research, code review, strategy, and multimodal work where being wrong is expensive. Reserve your deepest, most costly reasoning for these, not for classifying a support ticket.
Let routing do the work
Rather than hard-coding a model name that goes stale every few months, pick a profile that describes the outcome you need and let the platform route to the best available model. Taskclan's T1 profiles, Core, Flow and Max, do exactly this. See Models & T1 profiles for the details.