Model Selection Strategies: Right-Sizing Your AI Workloads

While the AI community anticipates Claude 3’s tiered model approach, it’s worth examining how to right-size your AI workloads today. Understanding when to use different capability levels can dramatically reduce costs while maintaining quality.

The Current Model Landscape

Today, we have several tiers to choose from:

Provider	Model	Use Case	Speed	Cost
OpenAI	GPT-4 Turbo	Complex reasoning	Slower	$$$
OpenAI	GPT-3.5 Turbo	General tasks	Fast	$
Anthropic	Claude 2.1	Long context	Medium	$$

When to Use Each Tier

High-Capability Models (GPT-4 Turbo, Claude 2.1)

Use for tasks that require:

high_capability_tasks = [
    "Complex multi-step reasoning",
    "Nuanced analysis and synthesis",
    "Long document understanding (Claude)",
    "Code generation with architectural decisions",
    "Tasks requiring world knowledge"
]

Fast/Efficient Models (GPT-3.5 Turbo)

Ideal for high-volume, simpler tasks:

from openai import OpenAI

client = OpenAI()

def classify_support_ticket(ticket: str) -> dict:
    """Use GPT-3.5 for classification tasks"""
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {
                "role": "user",
                "content": f"""Classify this support ticket:

Ticket: {ticket}

Return JSON with:
- category: (billing, technical, general)
- priority: (low, medium, high)
- sentiment: (positive, neutral, negative)"""
            }
        ]
    )
    return response.choices[0].message.content

Building a Model Router

Automatically route requests to the appropriate model:

from openai import OpenAI
from anthropic import Anthropic
from enum import Enum

class TaskComplexity(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"
    LONG_CONTEXT = "long_context"

class ModelRouter:
    MODEL_MAP = {
        TaskComplexity.SIMPLE: ("openai", "gpt-3.5-turbo"),
        TaskComplexity.MODERATE: ("openai", "gpt-4-turbo"),
        TaskComplexity.COMPLEX: ("openai", "gpt-4-turbo"),
        TaskComplexity.LONG_CONTEXT: ("anthropic", "claude-2.1")
    }

    def __init__(self):
        self.openai = OpenAI()
        self.anthropic = Anthropic()

    def estimate_complexity(self, prompt: str, context_length: int) -> TaskComplexity:
        """Estimate task complexity based on prompt characteristics"""

        # Long context goes to Claude
        if context_length > 50000:
            return TaskComplexity.LONG_CONTEXT

        word_count = len(prompt.split())

        # Simple heuristics - customize for your use case
        if word_count < 50 and "simple" in prompt.lower():
            return TaskComplexity.SIMPLE
        elif word_count > 500 or "analyze" in prompt.lower():
            return TaskComplexity.COMPLEX
        return TaskComplexity.MODERATE

    def route(self, prompt: str, context_length: int = 0) -> str:
        """Route to appropriate model based on complexity"""
        complexity = self.estimate_complexity(prompt, context_length)
        provider, model = self.MODEL_MAP[complexity]

        print(f"Using {complexity.value} tier ({provider}/{model})")

        if provider == "openai":
            response = self.openai.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
        else:
            response = self.anthropic.messages.create(
                model=model,
                max_tokens=4096,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.content[0].text

Cost Comparison

# Pricing per 1M tokens (as of March 2024)
PRICING = {
    "gpt-4-turbo": {"input": 10.00, "output": 30.00},
    "gpt-3.5-turbo": {"input": 0.50, "output": 1.50},
    "claude-2.1": {"input": 8.00, "output": 24.00}
}

def calculate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    """Calculate cost for a given workload"""
    pricing = PRICING[model]
    input_cost = (input_tokens / 1_000_000) * pricing["input"]
    output_cost = (output_tokens / 1_000_000) * pricing["output"]
    return input_cost + output_cost

# Example: 1M input tokens, 100K output tokens
models = ["gpt-4-turbo", "gpt-3.5-turbo", "claude-2.1"]
for model in models:
    cost = calculate_cost(model, 1_000_000, 100_000)
    print(f"{model}: ${cost:.2f}")

Latency Comparison

import time
from openai import OpenAI

client = OpenAI()
MODELS = [
    "gpt-3.5-turbo",
    "gpt-4-turbo"
]

prompt = "What is 2 + 2?"

for model in MODELS:
    start = time.time()
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=64
    )
    elapsed = time.time() - start
    print(f"{model}: {elapsed:.2f}s")

Best Practices

1. Start with the fastest model: Test if it meets quality requirements
2. Use routing: Match your model to task complexity
3. Monitor quality: Track metrics to ensure model fits task
4. Prepare for new models: Claude 3 will likely introduce new tiers

What Claude 3 Might Bring

When Claude 3 releases (rumored soon), we expect:

• Multiple tiers (possibly Opus, Sonnet, Haiku based on industry patterns)
• Better price/performance at each tier
• Potential vision capabilities
• This will expand routing options significantly

Conclusion

Don’t default to the most powerful model. Match your model to your task complexity to optimize both cost and latency while maintaining quality. Build flexible routing infrastructure now to easily incorporate new models like Claude 3 when they arrive.