Developer Proposes Two-Tier LLM Routing to Cut Costs Without Sacrificing Output Quality

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A software developer has outlined a two-tier LLM pipeline that routes tasks to a cheaper model by default and falls back to a more capable, costlier model only when the output fails a deterministic check. The key design principle is that no language model is allowed to judge its own or a peer's output — validation is handled exclusively by objective tools such as test suites, JSON schema validators, or regex checks. Every routing decision is logged to a JSONL audit file, making the system's behavior traceable and measurable over time. The pipeline is built to be provider-agnostic, compatible with any service that supports the OpenAI-compatible chat API. The author notes the article was prepared as part of outreach for MonkeyCode, a platform that provided free model access used during development.

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