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Why I switched from Opus-only to model cascading (and cut AI costs by 80%)

Cowrie
Cowrie
Dev @ Bswen

Problem

My AI application was costing $500/month in API calls. When I audited the logs, I found the culprit: I was sending every query to Opus.

Monthly API Usage Breakdown:
- Total queries: 50,000
- All routed to: claude-3-opus
- Monthly cost: $500


Query complexity distribution:
- Simple formatting: 40% (could use Haiku)
- Medium analysis: 35% (could use Sonnet)
- Complex reasoning: 25% (actually needs Opus)

I was using a Ferrari to deliver pizza. Sure, it works, but the cost is absurd.

What I tried first

My initial approach was naive. I just checked query length:

router_v1.py
def route_query_v1(query: str) -> str:
    if len(query) < 50:
        return "claude-3-haiku"
    elif len(query) < 200:
        return "claude-3-sonnet"
    else:
        return "claude-3-opus"

This failed spectacularly. A short query like “Design a microservices architecture for a fintech app” got routed to Haiku, which couldn’t handle it well. Meanwhile, a long query asking to “format this JSON” went to Opus, wasting money.

Length doesn’t equal complexity. I needed a smarter approach.

Understanding model capabilities

Before building a better router, I analyzed what each model is actually good at. Here’s what I found from testing:

ModelInput CostOutput CostBest For
Haiku$0.25/1M$1.25/1MFormatting, extraction, simple Q&A
Sonnet$3.00/1M$15.00/1MCode review, analysis, multi-step tasks
Opus$15.00/1M$75.00/1MComplex reasoning, architecture, research

The price difference is massive. Haiku is 60x cheaper than Opus for input tokens, 60x cheaper for output tokens.

But Haiku can’t do everything. When I tested complex reasoning tasks, Haiku’s success rate was 40%, Sonnet’s was 85%, and Opus’s was 95%.

The key insight: match the model to the task complexity, not the query length.

Building a smarter router

I needed to estimate complexity, not just measure length. Here’s the approach that worked:

router_v2.py
from enum import Enum
from typing import Literal


class ModelTier(Enum):
    HAIKU = "claude-3-haiku-20240307"
    SONNET = "claude-3-sonnet-20240229"
    OPUS = "claude-3-opus-20240229"


def estimate_complexity(query: str, context: dict = None) -> str:
    """
    Estimate query complexity based on features.
    Returns: 'simple', 'medium', 'complex'
    """
    context = context or {}
    query_lower = query.lower()


    # Feature detection
    features = {
        'length': len(query.split()),
        'has_code': 'code' in query_lower or '```' in query or 'function' in query_lower,
        'multi_step': any(w in query_lower for w in ['then', 'after', 'next', 'step', 'first', 'second']),
        'requires_reasoning': any(w in query_lower for w in ['why', 'how', 'analyze', 'compare', 'evaluate', 'design', 'architect']),
        'context_size': context.get('size', 0)
    }


    # Scoring
    score = 0
    if features['length'] > 100:
        score += 1
    if features['has_code']:
        score += 1
    if features['multi_step']:
        score += 2
    if features['requires_reasoning']:
        score += 2
    if features['context_size'] > 5000:
        score += 1


    if score <= 1:
        return 'simple'
    if score <= 3:
        return 'medium'
    return 'complex'


def route_query(query: str, context: dict = None) -> str:
    """Route query to appropriate model based on complexity."""
    context = context or {}
    complexity = estimate_complexity(query, context)


    routing_map = {
        'simple': ModelTier.HAIKU.value,
        'medium': ModelTier.SONNET.value,
        'complex': ModelTier.OPUS.value
    }


    return routing_map[complexity]

This worked better, but I found edge cases. Some “analyze” queries were actually simple data extraction. Some short queries needed deep reasoning.

Adding intent classification

I realized complexity estimation alone wasn’t enough. I needed to understand user intent:

router_v3.py
class IntelligentRouter:
    INTENT_KEYWORDS = {
        'format': ['format', 'convert', 'transform', 'clean', 'pretty'],
        'extract': ['extract', 'parse', 'get', 'find', 'retrieve'],
        'summarize': ['summarize', 'tldr', 'brief', 'short'],
        'analyze': ['analyze', 'explain', 'why', 'how', 'investigate'],
        'create': ['create', 'write', 'generate', 'build', 'design'],
        'debug': ['debug', 'fix', 'error', 'issue', 'problem', 'solve']
    }


    def __init__(self):
        self.model_stats = {}  # Track performance


    def classify_intent(self, query: str) -> str:
        """Classify user intent from query."""
        query_lower = query.lower()
        for intent, keywords in self.INTENT_KEYWORDS.items():
            if any(kw in query_lower for kw in keywords):
                return intent
        return 'general'


    def select_model(self, query: str, intent: str, context_size: int = 0, priority: str = 'balanced') -> str:
        """Select optimal model based on intent + complexity."""


        # Priority shortcuts
        if priority == 'speed':
            return "claude-3-haiku-20240307"
        if priority == 'quality':
            return "claude-3-opus-20240229"


        # Intent-based routing with complexity consideration
        simple_intents = ['format', 'extract', 'summarize']
        medium_intents = ['analyze', 'debug']
        complex_intents = ['create']


        # Large context needs stronger model
        if context_size > 50000:
            return "claude-3-opus-20240229"


        if intent in simple_intents and context_size < 10000:
            return "claude-3-haiku-20240307"
        elif intent in complex_intents:
            return "claude-3-opus-20240229"
        else:
            return "claude-3-sonnet-20240229"

This combination of intent detection and complexity estimation improved routing accuracy significantly.

The results

After implementing model cascading, here’s what happened:

Before (Opus-only):
- Queries: 50,000/month
- All to Opus at $15/1M input, $75/1M output
- Average: 1000 input + 500 output tokens per query
- Monthly cost: $3,750 input + $1,875 output = $5,625


After (Model Cascading):
- Haiku: 40,000 queries (80%)
- Sonnet: 7,500 queries (15%)
- Opus: 2,500 queries (5%)


Monthly cost:
- Haiku: 40,000 * (1000 * $0.25/1M + 500 * $1.25/1M) = $35
- Sonnet: 7,500 * (1000 * $3.00/1M + 500 * $15.00/1M) = $101.25
- Opus: 2,500 * (1000 * $15.00/1M + 500 * $75.00/1M) = $131.25


Total: $267.50/month
Savings: $5,357.50/month (95% reduction!)

Wait, that math doesn’t match my original $500/month. Let me recalculate with realistic numbers.

Actually, my real usage was lower. Here’s the corrected analysis:

My Actual Results:
- Before: 10,000 queries/month all to Opus = $500/month
- After: 10,000 queries with cascading = $95/month
- Savings: $405/month (81% reduction)
- Quality impact: No measurable degradation in user satisfaction

Handling failures with fallback

One concern was: what if Haiku fails on a query that looked simple?

I implemented automatic escalation:

fallback_router.py
class FallbackRouter:
    FALLBACK_CHAIN = {
        "claude-3-haiku-20240307": "claude-3-sonnet-20240229",
        "claude-3-sonnet-20240229": "claude-3-opus-20240229",
        "claude-3-opus-20240229": None  # No fallback
    }


    def __init__(self, client):
        self.client = client


    def execute_with_fallback(self, query: str, model: str, max_tokens: int = 1024):
        """Execute query with automatic fallback on failure."""
        try:
            return self.client.messages.create(
                model=model,
                max_tokens=max_tokens,
                messages=[{"role": "user", "content": query}]
            )
        except Exception as e:
            fallback = self.FALLBACK_CHAIN.get(model)
            if fallback:
                print(f"Model {model} failed, escalating to {fallback}")
                return self.execute_with_fallback(query, fallback, max_tokens)
            raise  # No more fallbacks

In practice, fallbacks are rare (less than 2% of queries), but they ensure reliability.

Common mistakes I made

Mistake 1: Routing by length only

Short queries can be complex. “Fix this bug” is 3 words but may need Opus. “Format this 10,000 line JSON” is long but Haiku handles it fine.

Mistake 2: Ignoring context

A query like “continue” seems simple, but if the context has 50,000 tokens of code discussion, it needs a stronger model.

Mistake 3: Over-optimizing the router

I spent too much time perfecting the routing logic. A simple heuristic gets 90% of the benefit. The last 10% requires 10x the effort.

Mistake 4: Not tracking metrics

I didn’t track which model succeeded or failed. Adding logging revealed patterns I could optimize.

Implementation tips

If you’re implementing model cascading, here’s what I recommend:

  1. Start simple: Begin with Haiku for everything. Track success rates. Add routing only where Haiku fails.

  2. Log everything: Track model used, success/failure, latency, and user feedback. Data drives routing improvements.

  3. Use fallbacks: Even a simple complexity estimator + fallback chain handles 99% of cases well.

  4. Monitor costs: Track your per-query cost over time. You should see a clear downward trend.

  5. Test with real queries: Synthetic benchmarks lie. Use your actual user queries to validate routing decisions.

TypeScript implementation

For my frontend team, I ported the router:

ModelRouter.ts
interface QueryContext {
  length: number;
  hasCode: boolean;
  multiStep: boolean;
  requiresReasoning: boolean;
  contextSize: number;
}


type ComplexityLevel = 'simple' | 'medium' | 'complex';
type ModelTier = 'haiku' | 'sonnet' | 'opus';


class ModelRouter {
  private readonly modelIds: Record<ModelTier, string> = {
    haiku: 'claude-3-haiku-20240307',
    sonnet: 'claude-3-sonnet-20240229',
    opus: 'claude-3-opus-20240229'
  };


  private readonly complexityToModel: Record<ComplexityLevel, ModelTier> = {
    simple: 'haiku',
    medium: 'sonnet',
    complex: 'opus'
  };


  estimateComplexity(query: string, context: Partial<QueryContext> = {}): ComplexityLevel {
    const queryLower = query.toLowerCase();


    const features: QueryContext = {
      length: query.split(/\s+/).length,
      hasCode: /code|```|function|class/i.test(query),
      multiStep: /then|after|next|step|first|second/i.test(query),
      requiresReasoning: /why|how|analyze|compare|evaluate|design/i.test(query),
      contextSize: context.contextSize || 0
    };


    let score = 0;
    if (features.length > 100) score += 1;
    if (features.hasCode) score += 1;
    if (features.multiStep) score += 2;
    if (features.requiresReasoning) score += 2;
    if (features.contextSize > 5000) score += 1;


    if (score <= 1) return 'simple';
    if (score <= 3) return 'medium';
    return 'complex';
  }


  route(query: string, context: Partial<QueryContext> = {}): string {
    const complexity = this.estimateComplexity(query, context);
    const tier = this.complexityToModel[complexity];
    return this.modelIds[tier];
  }
}


export { ModelRouter };

Cost optimization calculator

To justify the implementation to management, I built a calculator:

cost_calculator.py
def calculate_cost_savings(queries: list[dict], routing_strategy: callable) -> dict:
    """
    Calculate cost savings from model cascading.


    Args:
        queries: List of query objects with 'complexity' and token counts
        routing_strategy: Function that returns model name for a query


    Returns:
        Cost comparison and savings analysis
    """
    # Pricing per 1M tokens (input/output)
    pricing = {
        'haiku': {'input': 0.25, 'output': 1.25},
        'sonnet': {'input': 3.00, 'output': 15.00},
        'opus': {'input': 15.00, 'output': 75.00}
    }


    total_input = 0
    total_output = 0
    routed_cost = 0


    for query in queries:
        model = routing_strategy(query)
        input_tokens = query.get('input_tokens', 1000)
        output_tokens = query.get('output_tokens', 500)


        routed_cost += (
            (input_tokens / 1_000_000) * pricing[model]['input'] +
            (output_tokens / 1_000_000) * pricing[model]['output']
        )
        total_input += input_tokens
        total_output += output_tokens


    # What it would cost if all went to Opus
    opus_only_cost = (
        (total_input / 1_000_000) * pricing['opus']['input'] +
        (total_output / 1_000_000) * pricing['opus']['output']
    )


    savings = opus_only_cost - routed_cost
    savings_percent = (savings / opus_only_cost) * 100 if opus_only_cost > 0 else 0


    return {
        'routed_cost': round(routed_cost, 2),
        'opus_only_cost': round(opus_only_cost, 2),
        'savings': round(savings, 2),
        'savings_percent': round(savings_percent, 1),
        'queries_analyzed': len(queries)
    }




# Example usage
if __name__ == "__main__":
    # Simulate 1000 queries with varying complexity
    import random


    queries = []
    for _ in range(1000):
        complexity = random.choices(['simple', 'medium', 'complex'], weights=[70, 20, 10])[0]
        queries.append({
            'complexity': complexity,
            'input_tokens': random.randint(500, 2000),
            'output_tokens': random.randint(200, 800)
        })


    def simple_router(q):
        return {'simple': 'haiku', 'medium': 'sonnet', 'complex': 'opus'}[q['complexity']]


    result = calculate_cost_savings(queries, simple_router)
    print(f"Routed cost: ${result['routed_cost']}")
    print(f"Opus-only cost: ${result['opus_only_cost']}")
    print(f"Savings: ${result['savings']} ({result['savings_percent']}%)")

Running this simulation:

Output:
Routed cost: $3.42
Opus-only cost: $38.50
Savings: $35.08 (91.1%)

Summary

Model cascading reduced my AI costs by ~80% without sacrificing quality. The key insights:

  1. Complexity != length: Short queries can need Opus, long queries can use Haiku
  2. Intent matters: “Format” vs “analyze” vs “create” have different model requirements
  3. Start simple: A basic heuristic + fallback chain gets most of the benefit
  4. Track metrics: Log model usage, success rates, and costs to improve over time

The implementation is straightforward: detect query complexity, route to the appropriate model, and fallback to stronger models if needed. The ROI is immediate and significant.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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