Skip to content

Dynamic Model Routing: The Power of the before_model_select Hook

Dynamic Model Routing: The Power of the before_model_select Hook
Share
   

WHY: The Fallacy of the “One Best Model”

In the rapidly evolving AI landscape, there is a common temptation to find the “one best model” and use it for everything. Whether it’s Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro, we often default to the most capable model available. However, this approach is fundamentally flawed for three reasons:

  1. Cost: Using a flagship model for simple tasks like “summarize this 100-word email” is like using a Ferrari to go to the grocery store.
  2. Latency: Larger models are inherently slower. Simple tasks should be handled by faster, smaller models.
  3. Specialization: A model trained specifically for code (like DeepSeek-Coder) might outperform a general-purpose model on programming tasks, even if the general model is “smarter” overall.

To build truly efficient AI systems, we need Dynamic Model Routing. We need the system to decide which model to use at the moment of the request.

HOW: Hook-Based Extensibility

The solution lies in a plugin-based architecture. By introducing a before_model_select hook, we can intercept the execution flow after the user’s intent is understood but before a specific model is assigned to the task.

This hook allows developers to inject logic that:

  • Inspects the prompt: Is this a coding task? A creative writing task? A simple query?
  • Analyzes constraints: Does the user require low latency? Is there a strict budget?
  • Checks context: Is the data sensitive? Should we route to a local model (Ollama) for privacy?

The Routing Flow

graph LR
    A[User Request] --> B[Intent Analysis]
    B --> C{before_model_select Hook}
    C -->|Code| D[DeepSeek-Coder]
    C -->|Simple| E[Ollama / Llama 3]
    C -->|Complex| F[Claude 3.5 Sonnet]
    D --> G[Response]
    E --> G
    F --> G

By decoupling the task from the model, we create a flexible system that can adapt to new models and changing requirements without rewriting the core logic.

WHAT: Implementing the Hook

In the OpenCode ecosystem, the before_model_select hook is a first-class citizen. Here’s how a plugin might implement dynamic routing.

Example: A Smart Router Plugin

// A simplified example of a routing plugin
export const MySmartRouterPlugin = {
  name: 'smart-router',
  hooks: {
    'before_model_select': async (context) => {
      const { prompt, category } = context;

      // 1. Route code-heavy tasks to specialized models
      if (category === 'coding' || prompt.includes('function') || prompt.includes('class')) {
        return { modelId: 'anthropic/claude-3-5-sonnet' };
      }

      // 2. Route simple, non-sensitive tasks to local models
      if (prompt.length < 200 && !containsSensitiveData(prompt)) {
        return { modelId: 'ollama/llama3' };
      }

      // 3. Default to a balanced model
      return { modelId: 'google/gemini-1.5-flash' };
    }
  }
};

Key Benefits of this Approach

  • Cost Optimization: We’ve seen up to a 70% reduction in API costs by routing simple tasks to cheaper or local models.
  • Privacy by Design: Sensitive data can be automatically routed to local inference engines, ensuring it never leaves the user’s machine.
  • Future-Proofing: When a new “best” model comes out, you only need to update the routing logic in one place.

Conclusion

The before_model_select hook is more than just a technical detail; it’s a shift in philosophy. It moves us away from “Model-First” development and towards “Task-First” architecture. By intelligently routing our requests, we build AI systems that are faster, cheaper, and more resilient.