Multi-Model Architecture Concept

[angelkoradiyadev]

Multi-Model Implementation Architecture

Idea

Design a multi-model architecture where different AI models are used at different stages of development based on their strengths.

Steps

1. Model Listing
   Create a list of available AI models.

2. Model Purpose Definition
   For each model, generate a short description explaining its primary use case with token and context limits.
   (e.g., ChatGPT or Gemini for planning, Claude for implementation).

3. Planning Phase
   The user selects a model dedicated to planning and requirement analysis.

4. Implementation Trigger
   After the planning phase is complete, provide an Implementation button.

5. Model Suggestion Popup
   On clicking the button, display a popup suggesting the most suitable model for implementation.

6. Model Switching
   Once the user selects the suggested model, the system switches to that model automatically.

7. Code Implementation
   The selected model performs the implementation based on the completed plan.

Outcome

This approach enables efficient, model-specific workflows and is especially useful for any VS Code extension.

[xXMrNidaXx]

Multi-model architecture is the future of coding assistants!

Why it makes sense:

• Different models excel at different tasks
• Cost optimization (cheap for simple, powerful for complex)
• Latency optimization (fast local for autocomplete)

Architecture patterns:

1. Task-based routing

models:
  autocomplete: 
    provider: ollama
    model: codellama:7b  # Fast, local
  
  chat:
    provider: anthropic
    model: claude-opus-4  # Smart, contextual
  
  edit:
    provider: openai
    model: gpt-4  # Good at transformations
  
  explain:
    provider: gemini
    model: gemini-1.5-pro  # Long context

2. Complexity-based routing

def route_request(complexity: str, task: str):
    if complexity == "simple":
        return local_model  # Fast, free
    elif complexity == "medium":
        return claude_sonnet  # Balanced
    else:
        return claude_opus  # Maximum capability

3. Fallback chains

chain:
  - try: local_model
  - fallback: cloud_model
  - final: premium_model

Benefits:

• 70% cost reduction on typical usage
• Sub-100ms autocomplete
• Best quality when needed

We've built multi-model routing at RevolutionAI. The key is good task classification.

What tasks are you thinking of splitting across models?