How do you implement multi-model LLM architectures with model routing?

Multi-model architectures route requests to different LLMs based on task characteristics, optimizing cost and quality. Architecture: request classifier (analyze incoming request to determine complexity, type, and requirements), model router (select appropriate model based on classification), execution (send to selected model), and quality verification (optionally verify output quality). Routing strategies: (1) Complexity-based: simple factual queries go to fast/cheap models (GPT-4o-mini, Haiku), complex reasoning tasks go to powerful models (GPT-4o, Opus, Sonnet). (2) Task-based: code generation routes to code-specialized models, creative writing routes to models strong in generation, analysis routes to models strong in reasoning. (3) Cascading: start with the cheapest model, evaluate output quality, escalate to a more powerful model if quality is insufficient. Implementation: train a lightweight classifier on labeled examples of request complexity, implement model registry with capability profiles (latency, cost, quality per task type), build routing logic that considers constraints (latency SLA, cost budget, quality minimum), and implement fallback chains (if primary model is unavailable, route to backup). Quality gates: implement post-generation quality checks (confidence scoring, format validation, factuality checks), automatically re-route to a stronger model if quality gate fails, and track re-routing frequency per model pair for optimization. Cost impact: properly implemented routing can reduce LLM costs by 50-70% while maintaining quality by directing easy queries (which are typically the majority) to cheaper models. Evaluation: compare routed system against single-model baselines on quality metrics, measure cost savings, track routing accuracy (did requests go to appropriate models), and monitor latency distribution across model tiers.