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Multi-LLM Orchestration

Why Multi-LLM Architecture?

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The Multi-LLM Reality

No Single Model Rules Them All

In production, you'll rarely use just one LLM. Here's why companies like Alhena AI use multiple models:

Model Strengths & Weaknesses

ModelStrengthsWeaknessesBest For
GPT-4Reasoning, coding, general tasksExpensive, slowerComplex decisions
GPT-3.5Fast, cheapLess accurateSimple classification
ClaudeLong context, safetySlower, pricierDocument analysis
GeminiMultimodal, fastNewer, less testedImage + text tasks
MistralOpen source, fastLess capableHigh-volume, simple

Cost Comparison (per 1M tokens, approx.)

The Math: If you route 80% of simple queries to GPT-3.5 instead of GPT-4, you save ~95% on those calls while maintaining quality for complex cases.

Real Production Patterns

  1. Tiered Routing: Simple → cheap model, Complex → expensive model
  2. Fallback Chains: Primary fails → try secondary → try tertiary
  3. Ensemble: Multiple models vote on the answer
  4. Specialization: Different models for different task types

Alhena AI Example

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