a0p-model-selector

LLM orchestration for cost-efficiency is optimized by applying tiered model ladders and swarm patterns to route tasks from nano to opus models, maximizing throughput and minimizing expenses for distributed deployments.

Model selection for tiered LLM routing uses a decision matrix to guide escalation gates, batching, and deterministic retries from lower tiers to higher reasoning models based on specific task requirements.

A swarm of cheap LLMs can outperform a single flagship model by leveraging batching, deterministic T0 schema outputs, and escalation gates to efficiently distribute and process reasoning tasks.

Energy registry configurations control per-role model defaults by mapping specific roles to designated model tiers, providing safe, cost-predictable assignments for routing and distillation tasks.

LLM model selection handles caching-aware provider routing to maximize cost-efficiency, applying architectural best practices for evaluation and distributed orchestration across different model providers.

Tasks should be escalated to higher tiers in a model ladder when lower-tier models fail to meet deterministic output schemas or when complex reasoning tasks exceed the capabilities of nano models.