llm-router

name	llm-router
description	Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to Sonnet/Opus/o1. Use when deciding which model to call, optimizing LLM costs, or building multi-model agent systems. Activate on "which model", "model selection", "route to model", "LLM cost", "model routing", "cheap vs expensive model". NOT for prompt engineering (use prompt-engineer), model fine-tuning, or training custom models.
allowed-tools	Read
argument-hint	[task-description] [budget: low|medium|high]
metadata	{"category":"AI & Machine Learning","tags":["llm","router","which-model","model-selection","route-to-model"],"pairs-with":[{"skill":"cost-optimizer","reason":"Model routing is the primary mechanism for implementing cost optimization decisions"},{"skill":"cost-accrual-tracker","reason":"Real-time cost data informs routing decisions to stay within budget constraints"},{"skill":"prompt-engineer","reason":"Prompt complexity analysis determines which model tier the router should select"}]}

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.

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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)

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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

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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

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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.

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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

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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%

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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.