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Decision framework for selecting the right model for each task
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Based on SOC occupation classification
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| name | model-routing |
| version | 1.0.0 |
| description | Decision framework for selecting the right model for each task |
| author | iscmga |
| tags | ["models","routing","cost","optimization"] |
| triggers | {"keywords":["model","routing","cost","haiku","sonnet","opus","cheap","expensive","token"]} |
Use the cheapest model that meets quality requirements. Not every task needs the most powerful model. Route by task complexity to extend budget and reduce latency.
| Task Complexity | Model Tier | Examples |
|---|---|---|
| Simple | Haiku | Formatting, renaming, simple lookups, generating boilerplate, summarizing short text |
| Standard | Sonnet | Code implementation, bug fixes, test writing, code review, documentation |
| Complex | Opus | Architecture design, multi-file refactoring, security analysis, debugging subtle issues, ambiguous requirements |
Ask these questions in order:
Claude Code model can be set per-session:
Subagents spawned via the Agent tool can specify subagent_type:
Explore — research tasks, can use SonnetCursor allows model selection per chat. Apply the same framework:
When building apps that call the Claude API, route at the application level:
def select_model(task_complexity: str) -> str:
routing = {
"simple": "claude-haiku-4-5-20251001",
"standard": "claude-sonnet-4-6",
"complex": "claude-opus-4-6",
}
return routing.get(task_complexity, "claude-sonnet-4-6")
Approximate relative cost per million tokens (input):
| Model | Relative Cost | Best For |
|---|---|---|
| Haiku | 1x | High-volume, simple tasks |
| Sonnet | 3-5x | Balanced quality/cost |
| Opus | 15-25x | Highest quality, complex reasoning |
Routing 60% of tasks to Sonnet and 30% to Haiku instead of sending everything to Opus can reduce costs by 70-80%.