LLM Router

LLM Router

Selects the optimal LLM model for each task. The single biggest cost lever in multi-agent systems — intelligent routing saves 45-85% while maintaining 95%+ of top-model quality.

When to Use

✅ Use for:

• Deciding which model to call for a specific task
• Assigning models to DAG nodes in agent workflows
• Optimizing LLM API costs across a system
• Building cascading try-cheap-first patterns

❌ NOT for:

• Prompt engineering (use prompt-engineer)
• Model fine-tuning or training
• Comparing model architectures (academic research)

Routing Decision Tree

flowchart TD
  A{Task type?} -->|Classify / validate / format / extract| T1["Tier 1: Haiku, GPT-4o-mini (~$0.001)"]
  A -->|Write / implement / review / synthesize| T2["Tier 2: Sonnet, GPT-4o (~$0.01)"]
  A -->|Reason / architect / judge / decompose| T3["Tier 3: Opus, o1 (~$0.10)"]
  
  T1 --> Q1{Quality sufficient?}
  Q1 -->|Yes| Done1[Use cheap model]
  Q1 -->|No| T2
  
  T2 --> Q2{Quality sufficient?}
  Q2 -->|Yes| Done2[Use balanced model]
  Q2 -->|No| T3

Tier Assignment Table

| Task Type | Tier | Models | Cost/Call | Why This Tier |

|-----------|------|--------|-----------|---------------|

| Classify input type | 1 | Haiku, GPT-4o-mini | ~$0.001 | Deterministic categorization |

| Validate schema/format | 1 | Haiku, GPT-4o-mini | ~$0.001 | Mechanical checking |

| Format output / template | 1 | Haiku, GPT-4o-mini | ~$0.001 | Structured transformation |

| Extract structured data | 1 | Haiku, GPT-4o-mini | ~$0.001 | Pattern matching |

| Summarize text | 1-2 | Haiku → Sonnet | ~$0.001-0.01 | Short summaries: Haiku; nuanced: Sonnet |

| Write content/docs | 2 | Sonnet, GPT-4o | ~$0.01 | Creative quality matters |

| Implement code | 2 | Sonnet, GPT-4o | ~$0.01 | Correctness + style |

| Review code/diffs | 2 | Sonnet, GPT-4o | ~$0.01 | Needs judgment, not just pattern matching |

| Research synthesis | 2 | Sonnet, GPT-4o | ~$0.01 | Multi-source reasoning |

| Decompose ambiguous problem | 3 | Opus, o1 | ~$0.10 | Requires deep understanding |

| Design architecture | 3 | Opus, o1 | ~$0.10 | Complex system reasoning |

| Judge output quality | 3 | Opus, o1 | ~$0.10 | Meta-reasoning about quality |

| Plan multi-step strategy | 3 | Opus, o1 | ~$0.10 | Long-horizon planning |

Three Routing Strategies

Strategy 1: Static Tier Assignment (Start Here)

Assign model by task type at DAG design time. No runtime logic. Gets 60-70% of possible savings.

nodes:
  - id: classify
    model: claude-haiku-4-5     # Tier 1: $0.001
  - id: implement
    model: claude-sonnet-4-5    # Tier 2: $0.01  
  - id: evaluate
    model: claude-opus-4-5      # Tier 3: $0.10

Strategy 2: Cascading (Try Cheap First)

Try the cheap model; if quality is below threshold, escalate. Adds ~1s latency but saves 50-80% on nodes where cheap succeeds.

1. Execute with Tier 1 model
2. Quick quality check (also Tier 1 — costs ~$0.001)
3. If quality ≥ threshold → done
4. If quality < threshold → re-execute with Tier 2

Best for nodes where you're genuinely unsure which tier is needed.

Strategy 3: Adaptive (Learn from History)

Record success/failure per task type per model. Over time, the router learns:

• "Classification nodes always succeed on Haiku" → stay cheap
• "Code review nodes fail on Haiku 40% of the time" → upgrade to Sonnet
• "Architecture nodes succeed on Sonnet 90% of the time" → don't need Opus

Gets 75-85% savings after ~100 executions of training data.

Provider Selection

Once model tier is chosen, select the provider:

| Model Class | Provider Options | Selection Criteria |

|------------|-----------------|-------------------|

| Haiku-class | Anthropic, AWS Bedrock | Latency, regional availability |

| Sonnet-class | Anthropic, AWS Bedrock, GCP Vertex | Cost, rate limits |

| Opus-class | Anthropic | Only provider |

| GPT-4o-class | OpenAI, Azure OpenAI | Rate limits, compliance |

| Open-source | Ollama (local), Together.ai, Fireworks | Cost ($0), latency, GPU availability |

Cost Impact Example

10-node DAG, "refactor a codebase":

| Strategy | Mix | Cost | Savings |

|----------|-----|------|---------|

| All Opus | 10× $0.10 | $1.00 | — |

| All Sonnet | 10× $0.01 | $0.10 | 90% |

| Static tiers | 4× Haiku + 4× Sonnet + 2× Opus | $0.24 | 76% |

| Cascading | 6× Haiku + 3× Sonnet + 1× Opus | $0.14 | 86% |

| Adaptive (trained) | Dynamic | ~$0.08 | 92% |

Anti-Patterns

Always Use the Best Model

Wrong: Route everything to Opus/o1 "for quality."

Reality: 60%+ of typical DAG nodes are classification, validation, or formatting — tasks where Haiku performs identically to Opus. You're burning money.

Always Use the Cheapest Model

Wrong: Route everything to Haiku "for cost."

Reality: Complex reasoning, architecture design, and quality judgment genuinely need stronger models. Haiku will produce plausible-looking but subtly wrong output on hard tasks.

Ignoring Latency

Wrong: Only optimizing for cost, ignoring that Opus takes 5-10x longer than Haiku.

Reality: In a 10-node DAG, model choice affects total execution time as much as cost. Route time-critical paths to faster models.

No Feedback Loop

Wrong: Setting model tiers once and never adjusting.

Reality: As models improve (Haiku gets smarter every generation), tasks that needed Sonnet last month may work on Haiku today. Record outcomes and adapt.