LLM Routing: Choosing the Right Model for Each Task
By Learnia Team
LLM Routing: Choosing the Right Model for Each Task
This article is written in English. Our training modules are available in French.
Should every question go to GPT-4? That's like using a sports car to fetch groceries—overkill for simple tasks and unnecessarily expensive. LLM routing matches questions to the right model, optimizing cost and speed without sacrificing quality.
What Is LLM Routing?
LLM routing is the practice of directing different queries to different AI models based on task requirements.
The Basic Concept
User query → Router → Appropriate model
"What's 2+2?" → Fast, cheap model (GPT-3.5)
"Analyze this legal contract" → Powerful model (GPT-4)
"Generate a poem" → Creative model (Claude)
Why Routing Matters
The Cost Reality
| Model | Input Cost (per 1M tokens) | Quality | |-------|---------------------------|---------| | GPT-3.5 Turbo | $0.50 | Good | | GPT-4 Turbo | $10.00 | Excellent | | GPT-4o | $2.50 | Very Good | | Claude 3 Haiku | $0.25 | Good | | Claude 3 Opus | $15.00 | Excellent |
20-60× price difference between models.
The Math
Without routing:
1000 queries/day × GPT-4 ($0.01/query) = $10/day = $300/month
With routing (70% simple, 30% complex):
700 queries × GPT-3.5 ($0.0005) = $0.35
300 queries × GPT-4 ($0.01) = $3.00
Total: $3.35/day = $100/month
Savings: 67% cost reduction
Routing Strategies
1. Task-Based Routing
Route based on what the user is asking:
Classification/extraction → Small model
Creative writing → Medium model
Complex reasoning → Large model
Code generation → Specialized model
2. Complexity-Based Routing
Estimate query difficulty:
Simple: "What's the weather?"
→ Fast model
Medium: "Summarize this article"
→ Balanced model
Complex: "Compare these three legal arguments"
→ Powerful model
3. Cascade Routing
Try smaller model first, escalate if needed:
Step 1: Send to GPT-3.5
Step 2: Check confidence/quality
Step 3: If uncertain → re-send to GPT-4
4. Intent-Based Routing
Classify intent, then route:
Intent: customer_support → Support-tuned model
Intent: code_help → Code-specialized model
Intent: creative → Creative model
Intent: analysis → Reasoning model
What Makes a Query "Complex"?
Signals of Complexity
✓ Multi-step reasoning required
✓ Domain expertise needed
✓ Long context to process
✓ Nuanced judgment required
✓ High-stakes outcome
Signals of Simplicity
✓ Single fact lookup
✓ Simple format conversion
✓ Short, clear instruction
✓ Low-stakes outcome
✓ Well-defined output
Real-World Routing Examples
Customer Support Bot
"What are your hours?"
→ Route to FAQ lookup + small model
Cost: $0.0001 | Latency: 200ms
"I'm having a complex billing dispute about..."
→ Route to support-specialized model + human escalation flag
Cost: $0.005 | Latency: 1s
Code Assistant
"Add a comment to this line"
→ Small, fast model
Cost: $0.0002
"Refactor this 500-line function for performance"
→ Large model with long context
Cost: $0.02
Research Assistant
"When was the Eiffel Tower built?"
→ Small model (factual recall)
"Compare the economic impacts of three trade policies"
→ Large model (analysis + reasoning)
Cascade Pattern Deep Dive
The cascade approach is particularly powerful:
┌─────────────────┐
│ User Query │
└────────┬────────┘
▼
┌─────────────────┐
│ Tier 1: Small │──── Confident? ──▶ Return answer
│ (GPT-3.5/Haiku) │ │
└────────┬────────┘ │ No
▼ ▼
┌─────────────────┐
│ Tier 2: Medium │──── Confident? ──▶ Return answer
│ (GPT-4o/Sonnet) │ │
└────────┬────────┘ │ No
▼ ▼
┌─────────────────┐
│ Tier 3: Large │────────────────▶ Return answer
│ (GPT-4/Opus) │
└─────────────────┘
Advantages of Cascading
✓ Most queries resolved by cheap model
✓ Complex queries still get best model
✓ Natural quality/cost optimization
✓ Built-in escalation path
Implementing a Simple Router
Conceptual Approach
1. Analyze incoming query
- Length
- Keywords (e.g., "analyze", "compare", "simple")
- Domain detection
2. Assign complexity score (0-10)
- 0-3: Simple → Small model
- 4-6: Medium → Medium model
- 7-10: Complex → Large model
3. Route to selected model
4. (Optional) Evaluate response quality
- If low quality, retry with larger model
Routing Signals
Simple query indicators:
- Short (< 20 words)
- Contains "what is", "define", "when"
- Single question
Complex query indicators:
- Long (> 100 words)
- Contains "analyze", "compare", "evaluate"
- Multiple sub-questions
- Technical jargon
- Attached documents
Common Routing Mistakes
1. Over-Routing to Expensive Models
❌ Send everything to GPT-4 "just in case"
✅ Trust smaller models for simple tasks
2. Under-Routing Complex Tasks
❌ Always use the cheapest model
✅ Accept higher cost for quality-critical tasks
3. Ignoring Latency
❌ Route based only on cost
✅ Consider: Simple queries need fast responses
4. No Fallback
❌ Single model, no backup
✅ Have escalation path when confidence is low
Key Takeaways
- →LLM routing matches queries to appropriate models
- →Can reduce costs by 50-70% without quality loss
- →Route by task type, complexity, or cascade
- →Simple queries → cheap/fast models; complex → powerful models
- →Monitor and iterate—routing rules need tuning
Ready to Build Smart AI Workflows?
This article covered the what and why of LLM routing. But production routing systems require implementation patterns, monitoring, and optimization.
In our Module 4 — Chaining & Routing, you'll learn:
- →Designing multi-model architectures
- →Implementing routing logic
- →Cascade patterns and fallbacks
- →Cost optimization strategies
- →Monitoring and improving routing accuracy
Module 4 — Chaining & Routing
Build multi-step prompt workflows with conditional logic.