Model Tiering

Automatic Model Tiering Skill

When to activate

• Optimizing token costs during multi-step coding agent execution.
• Dynamically allocating model weights during large refactoring runs.
• Resolving complex planning tasks before generating implementation code.
• Triggering fallback configurations when high-reasoning tasks fail on smaller models.

When NOT to use

• Fast interactive chats where switching models adds noticeable latency.
• Explicit developer model overrides (e.g. --model sonnet).

Instructions

To route tasks dynamically, classify developer queries into one of the three following tiers:

1. The Reasoning Tier (Opus/Thinking Model)

• Scope: Large architectural changes, safety audits, complex algorithm designs, cross-cutting concerns.
• Criteria: High structural risk, requires reasoning over long context windows.
• Implementation:

  pythonCopy code
  def route_to_reasoning(task_ctx):
      return {
          "model": "claude-3-opus",
          "temperature": 0.2,
          "max_tokens": 4096,
          "system_prompt": "Focus entirely on planning and mathematical correctness."
      }
  

2. The Planning Tier (Sonnet)

• Scope: Middle-layer APIs, refactoring local functions, layout designs, and creating step-by-step implementation tasks.
• Criteria: Mid-level complexity, follows existing architecture patterns.
• Implementation:

  pythonCopy code
  def route_to_planning(task_ctx):
      return {
          "model": "claude-3-5-sonnet",
          "temperature": 0.3,
          "max_tokens": 4096
      }
  

3. The Coding Tier (Haiku)

• Scope: Writing boilerplate code, documentation, straightforward unit tests, script modifications.
• Criteria: Single-file changes, low architectural complexity, repeatable code patterns.
• Implementation:

  pythonCopy code
  def route_to_coding(task_ctx):
      return {
          "model": "claude-3-haiku",
          "temperature": 0.0,
          "max_tokens": 4096
      }
  

Example

typescriptCopy code
// Routing pipeline orchestrator
import { getTaskComplexity } from "./complexity-analyzer";

export async function executeTaskWithMoE(task: string) {
  const complexity = getTaskComplexity(task);
  
  if (complexity.score > 0.8) {
    const plan = await callModel("claude-3-opus", `Plan this task: ${task}`);
    const code = await callModel("claude-3-5-sonnet", `Write code for plan: ${plan}`);
    return { plan, code };
  } else {
    return callModel("claude-3-haiku", `Write snippet: ${task}`);
  }
}