Model Selection Strategy
A practical framework for choosing the right model for every task.
Most people pick one model and use it for everything. That's like using a hammer for every home repair. Different tasks have different requirements for quality, speed, cost, and capability — and different models optimize for different combinations of these factors.
- Quality required — Is this a critical business document or a quick brainstorm? High-stakes tasks justify premium models.
- Speed required — Do you need real-time responses (chatbot) or is batch processing acceptable? Smaller models respond faster.
- Cost sensitivity — Are you making 10 queries a day or 10,000? At scale, model choice dramatically affects your bill.
- Special capabilities — Do you need vision, long context, tool use, or real-time information? Not all models support all features.
Here is a practical decision framework for common task categories:
- Quick Q&A or simple tasks → Use a fast, low-cost model tier first
- Complex reasoning or math → Use a reasoning-oriented tier where accuracy matters more than speed
- Long document analysis → Use the model family with the strongest long-context behavior for your stack
- Creative writing → Test at least two strong general-purpose model families; preferences here are often subjective
- Code generation → Start with a strong coding-capable family, then benchmark on your actual repository and task type
- Multimodal (image/video input) → Prefer a family with strong native vision, audio, or video support
- Production APIs at scale → Start with the cheapest model that meets quality bar, upgrade only where needed
In production systems, a powerful pattern is to cascade: start with a fast, cheap model, and only escalate to a more capable one when needed. For example, use a low-cost model tier to classify incoming requests, then route only the hardest cases to a reasoning or flagship tier. This can dramatically reduce costs while preserving quality where it matters.
Task Complexity Classifier
Use a small model to route tasks to the appropriate model tier.
Classify this user request as SIMPLE, MODERATE, or COMPLEX based on these criteria: - SIMPLE: Factual lookup, simple formatting, basic Q&A - MODERATE: Requires some analysis, multiple steps, or domain knowledge - COMPLEX: Requires deep reasoning, multi-step logic, or creative expertise Request: "[USER REQUEST]" Classification:
Prompt Templates
Model Evaluation Template
Standardized template for fair cross-model comparison.
I'm evaluating AI models for [USE CASE]. Please complete this task so I can compare your output with other models: Task: [SPECIFIC TASK] Quality criteria: [WHAT I'M EVALUATING] Format: [EXACT OUTPUT FORMAT] Please respond exactly in the specified format with no additional commentary.
Cost-Quality Analysis
Framework for balancing cost and quality at scale.
I need to process [VOLUME] of [TASK TYPE] per [TIME PERIOD]. Help me think through model selection: 1. What's the minimum model quality needed for this task? 2. What's the cost at this volume for different model tiers? 3. Where could I use a cheaper model without quality loss? 4. What percentage of requests likely need a premium model? Assume standard API pricing.
Test Your Knowledge
Knowledge Check
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What is the "cascade pattern" in model selection?
Key Takeaways
- ✓Model selection is a core prompt engineering skill — the right model matters as much as the right prompt
- ✓Evaluate models across four factors: quality, speed, cost, and special capabilities
- ✓Use the cascade pattern in production to optimize cost without sacrificing quality
- ✓Always test your specific prompts across multiple models before committing
- ✓The cheapest model that meets your quality bar is the correct choice for production
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