LLM Routing: Choosing the Right Model for Each Task

LLM routing: what production teams actually do vs the blog-post version

LLM routing has become one of the most-discussed cost-optimisation techniques in production LLM applications. The threads on r/LangChain, r/LocalLLaMA, and r/MachineLearning reflect a gap between the concept's simplicity and the production reality.

What routing actually looks like in production:

• →Tiered cascades. Cheapest model first; escalate to more capable models if the cheap model flags uncertainty or the output fails validation. Deployed at scale by Anyscale's RouteLLM research and similar projects.
• →Task-based routing. Code queries to Claude/GPT-5 Codex; writing queries to Claude Opus; retrieval-heavy queries to Gemini; multimodal to GPT-5 or Gemini. The "best model" depends on task, not popularity.
• →Latency-sensitive routing. Interactive chat gets fast/cheap models (Haiku, Gemini Flash, GPT-4o mini); background jobs get the slower, more capable ones.
• →Reliability routing. Production systems route to a backup provider when the primary is slow or errored. Single-vendor dependency is operational risk.

What makes routing hard:

• →Classification cost. The router itself has to decide quickly which model to use. A 200ms classifier call in front of a 500ms generation is a 40% latency penalty for no quality gain.
• →Drift. The "cheap model good enough" assumption changes as models update. Routing logic needs periodic re-evaluation.
• →Evaluating quality consistently. You need evaluation that works across models, or you can't know whether cheap-tier responses are acceptable. Braintrust, LangSmith, and similar tools emerged to address this.
• →Vendor lock-in in subtle forms. Different models have different JSON-mode quirks, function-calling formats, and prompt styles. Supporting three models well is three times the prompt-engineering work of one.

What's genuinely worth doing:

• →Start with two tiers. A cheap model for the 80% easy cases, a capable model for the 20% hard ones. Two-tier routing captures most of the value with a fraction of the complexity.
• →Measure before you optimise. If your LLM bill is under a certain threshold, routing complexity costs more than it saves.
• →Use providers with similar APIs. OpenAI, Anthropic, and Google have converged on similar function-calling and structured-output patterns. Starting from that compatibility makes routing cheaper to implement.
• →Cache, then route. Prompt caching often beats routing for cost reduction on workloads with reused prefixes.

The honest framing: LLM routing is useful but usually over-applied. For most applications at most scales, picking one good model and using it well beats an elaborate routing system. Reach for routing when you have evidence it will pay off, not because a conference talk made it sound clever.

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

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:

Step 1: User Query arrives

Step 2: Tier 1 - Small Model (GPT-3.5/Haiku)

• →Confident? → Return answer
• →Not confident? → Escalate

Step 3: Tier 2 - Medium Model (GPT-4o/Sonnet)

• →Confident? → Return answer
• →Not confident? → Escalate

Step 4: Tier 3 - Large Model (GPT-4/Opus)

• →Always returns answer

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

Core Insights

1. →LLM routing matches queries to appropriate models
2. →Can reduce costs by 50-70% without quality loss
3. →Route by task type, complexity, or cascade
4. →Simple queries → cheap/fast models; complex → powerful models
5. →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

→ Explore Module 4: Chaining & Routing