A Task-Based Approach to Coarse Model Selection
JustinLin610 · x · 2026-07-16
This reply clarifies that the focus isn't on a new technical method, but rather on how to strategically use different models.
The author mentions trying to make MoE work truly like "expert collaboration" or using task routers to save costs, but found it hard to stabilize in practice. By comparison, this approach is more about establishing coarse-grained model selection criteria:
- Simple tasks: Q&A, simple agent tasks requiring only one or two tool calls, fixing minor bugs
- Complex tasks: Large-scale code projects, machine learning experiments, evaluations, etc.
The core idea is to use models of varying strengths and costs depending on the task scale, rather than trying to solve every scenario with a single unified routing mechanism.
Related event: Multi-Model Routing is Key to Cost-Effective AI Coding(3 posts)→
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