The Multi-Model Paradigm
No single model is best at everything. Production AI systems increasingly use multiple models, each optimized for specific tasks, working together.
π‘ The Reality: OpenAI, Anthropic, Google, and open-source models each have strengths. Smart architectures leverage all of them.
Multi-Model Patterns
Pattern 1: Router Architecture
A classifier model routes requests to specialized models:
# ROUTER PROMPT
Classify this user request into one of these categories:
- code_generation: Writing or explaining code
- creative_writing: Stories, poems, creative content
- analysis: Data analysis, summarization
- conversation: General chat, Q&A
- specialized: Domain-specific queries
Return only the category name.
---
Based on category, route to:
- code_generation β Claude 3.5 Sonnet (best at code)
- creative_writing β GPT-4 (strong creative)
- analysis β GPT-4 Turbo (fast, good at analysis)
- conversation β GPT-3.5 (cost-effective)
- specialized β Fine-tuned domain model
Pattern 2: Chain Architecture
Models work sequentially, each building on the previous:
Step 1: Research
GPT-4 + Web Search
Gather information
β
Step 2: Analyze
Claude
Deep analysis
β
Step 3: Generate
GPT-4
Create output
β
Step 4: Review
Claude
Quality check
Pattern 3: Ensemble Architecture
Multiple models answer the same question, results are combined:
# ENSEMBLE PROMPT
You will receive answers from 3 different AI models to the same question.
Synthesize these into a single, best answer:
Model A (GPT-4):
{{gpt4_response}}
Model B (Claude):
{{claude_response}}
Model C (Gemini):
{{gemini_response}}
Instructions:
1. Identify points of agreement (high confidence)
2. Identify disagreements (investigate further)
3. Combine the strongest elements from each
4. Resolve conflicts using logical reasoning
5. Produce a unified, high-quality response
Pattern 4: Critic Architecture
One model generates, another critiques:
# GENERATOR (Model A)
Write a marketing email for our new product launch.
[Product details...]
---
# CRITIC (Model B)
Review this marketing email and provide feedback on:
1. Clarity and persuasiveness
2. Call-to-action effectiveness
3. Tone appropriateness
4. Potential improvements
Rate each category 1-10 and explain.
---
# GENERATOR (Model A) - Revision
Revise the email based on this feedback:
{{critic_feedback}}
Model Selection Matrix
| Task Type | Primary Choice | Alternative | Why |
|---|---|---|---|
| Code Generation | Claude 3.5 Sonnet | GPT-4 | Best code quality |
| Long Documents | Claude | Gemini | 200k+ context |
| Speed/Cost | GPT-3.5 | Claude Haiku | Fast and cheap |
| Reasoning | GPT-4o / Claude | o1 | Deep analysis |
| Multimodal | GPT-4V | Gemini Pro Vision | Image understanding |
Cost Optimization Strategy
Tier 1: Fast & Cheap
GPT-3.5 / Claude Haiku
Simple classification, basic Q&A
Tier 2: Balanced
GPT-4 Turbo / Claude Sonnet
Most production tasks
Tier 3: Premium
GPT-4 / Claude Opus / o1
Complex reasoning, critical tasks
π Key Takeaway: Don't marry one model. Build architectures that route to the right model for each task, optimizing for quality, speed, and cost.