Multi-Tier Model & Prefix Cache Router
Benchmark inference cascades and prompt caching economics across flagship reasoning (Astra), balanced execution (Sol), and high-throughput routing (Luna).
Cascade Traffic Allocation & Pricing
OpenAI GPT-6 Universe Specifications| Req ID | Task Intent | Prefix Cache | First Pass | Escalation Path | Latency | Cost |
|---|
Production TypeScript Router (Drop-In SDK Cascade)
Architectural Advantage
The GPT-6 family introduces a tri-tier operational matrix: GPT-6 Astra provides frontier reasoning, while GPT-6 Sol and GPT-6 Luna bring that foundational architecture into fast, affordable production tiers.
By coupling fast first-pass validation with shared prompt prefix caching, teams avoid routing 100% of volume through flagship models while preserving near-identical accuracy.
Optimization FAQ
How does Prompt Prefix Caching work in GPT-6?
Prefix caching reuses precomputed attention states for static parts of the prompt (system guidelines, schemas, few-shot examples, large codebases). GPT-6 yields up to 75-80% discount on cached input tokens with near-zero time-to-first-token (TTFT).
What criteria triggers escalation from Luna to Sol or Astra?
Production routers use three signals: token confidence scores, structured JSON validation errors, and regex assertion tests. If Luna produces an invalid schema or indicates low confidence, the request seamlessly escalates to Sol or Astra with zero customer impact.
Can this configuration be exported to LangChain or LiteLLM?
Yes. The downloadable configuration file includes exact fallback rules, model IDs, token limits, and cache keys ready for LiteLLM router proxy, custom edge workers, or Python SDK pipelines.