Claude Model Routing Strategy (2026)

Model routing is the practice of automatically sending each task to the most cost-effective Claude model. Instead of using one model for everything, a routing layer analyzes the task and selects Haiku, Sonnet, or Opus based on complexity, cost, and quality requirements. Teams that implement routing typically reduce API costs by 70-80 percent without measurable quality loss. Try the Model Selector for instant per-task routing recommendations.

How Model Routing Works

A model router sits between your application and the Anthropic API. For each request, it:

  1. Analyzes the task characteristics (length, complexity signals, task type)
  2. Looks up the optimal model from a routing table
  3. Sends the request to the selected model
  4. Returns the response to the caller
User Request → Router → [Haiku | Sonnet | Opus] → Response

The routing decision takes milliseconds and adds negligible latency. The cost savings are immediate and compound with every request.

Building a Routing Table

The routing table maps task types to models. Start with this baseline and customize for your workload:

const ROUTING_TABLE: Record<string, ModelConfig> = {
  // Haiku tasks — pattern matching, no reasoning needed
  'format': { model: 'claude-3-5-haiku-20241022', maxTokens: 4096 },
  'convert': { model: 'claude-3-5-haiku-20241022', maxTokens: 4096 },
  'extract': { model: 'claude-3-5-haiku-20241022', maxTokens: 2048 },
  'classify': { model: 'claude-3-5-haiku-20241022', maxTokens: 1024 },
  'boilerplate': { model: 'claude-3-5-haiku-20241022', maxTokens: 8192 },
  // Sonnet tasks — standard reasoning
  'implement': { model: 'claude-sonnet-4-20250514', maxTokens: 16384 },
  'test': { model: 'claude-sonnet-4-20250514', maxTokens: 16384 },
  'review': { model: 'claude-sonnet-4-20250514', maxTokens: 8192 },
  'document': { model: 'claude-sonnet-4-20250514', maxTokens: 8192 },
  'refactor-simple': { model: 'claude-sonnet-4-20250514', maxTokens: 16384 },
  // Opus tasks — deep reasoning required
  'debug-complex': { model: 'claude-opus-4-20250514', maxTokens: 32768 },
  'architect': { model: 'claude-opus-4-20250514', maxTokens: 32768 },
  'security-review': { model: 'claude-opus-4-20250514', maxTokens: 16384 },
  'refactor-complex': { model: 'claude-opus-4-20250514', maxTokens: 32768 },
};

Automatic Task Classification

The simplest router uses keyword matching. More sophisticated routers use a classifier:

Keyword-Based Router

function routeByKeywords(prompt: string): string {
  const lower = prompt.toLowerCase();
  // Opus signals
  const opusSignals = [
    'race condition', 'deadlock', 'architecture',
    'security', 'vulnerability', 'cross-file',
    'system design', 'complex bug'
  ];
  if (opusSignals.some(signal => lower.includes(signal))) {
    return 'claude-opus-4-20250514';
  }
  // Haiku signals
  const haikuSignals = [
    'format', 'rename', 'convert', 'extract',
    'boilerplate', 'template', 'sort imports',
    'add types', 'fix lint'
  ];
  if (haikuSignals.some(signal => lower.includes(signal))) {
    return 'claude-3-5-haiku-20241022';
  }
  // Default to Sonnet
  return 'claude-sonnet-4-20250514';
}

Classifier-Based Router

For production systems, use Haiku itself as a classifier. The cost of classification is negligible:

async function routeByClassifier(prompt: string): Promise<string> {
  const classification = await anthropic.messages.create({
    model: 'claude-3-5-haiku-20241022',
    max_tokens: 50,
    messages: [{
      role: 'user',
      content: `Classify this task as SIMPLE, STANDARD, or COMPLEX.
Reply with one word only.\n\nTask: ${prompt}`
    }],
  });
  const level = classification.content[0].text.trim().toUpperCase();
  switch (level) {
    case 'SIMPLE': return 'claude-3-5-haiku-20241022';
    case 'COMPLEX': return 'claude-opus-4-20250514';
    default: return 'claude-sonnet-4-20250514';
  }
}

The classification request costs approximately $0.0001 (100 input tokens, 5 output tokens with Haiku). Even if it routes incorrectly 10 percent of the time, the overall savings far exceed the classification cost.

Routing Strategies for Common Architectures

Orchestrator-Worker Pattern

Use Opus as an orchestrator that plans work, then delegate execution to Sonnet and Haiku workers:

Opus (orchestrator) → Plans the approach, identifies subtasks
  ├── Sonnet (worker) → Implements feature A
  ├── Sonnet (worker) → Writes tests
  ├── Haiku (worker) → Formats code
  └── Haiku (worker) → Generates types

This pattern cuts costs by 60-70 percent versus using Opus for everything because the orchestration step is a small fraction of total tokens.

Escalation Pattern

Start every request with Haiku. If Haiku’s confidence is low or the output fails validation, escalate to Sonnet. If Sonnet struggles, escalate to Opus:

Haiku → Success? → Return
  ↓ No
Sonnet → Success? → Return
  ↓ No
Opus → Return

This pattern works best when most tasks are simple. The average cost is close to Haiku’s because most requests never escalate.

Domain-Based Routing

Route by code domain rather than task type:

const DOMAIN_ROUTING = {
  'frontend': 'claude-sonnet-4-20250514',    // UI code is standard complexity
  'backend-api': 'claude-sonnet-4-20250514', // CRUD endpoints
  'database': 'claude-opus-4-20250514',      // Schema design needs reasoning
  'security': 'claude-opus-4-20250514',      // Always use best model
  'devops': 'claude-sonnet-4-20250514',      // Config generation
  'docs': 'claude-3-5-haiku-20241022',       // Documentation is template work
};

Try It Yourself

Building a custom routing layer requires engineering effort. The Model Selector provides a ready-made routing recommendation for any task. Describe your task and it recommends the optimal model based on the same routing principles described here. Use it to inform your routing table or as a quick decision tool during interactive development.

Measuring Routing Effectiveness

Track these metrics after implementing routing:

Cost per task. Compare your average cost per API call before and after routing. Target a 70-80 percent reduction.

Quality score. Sample outputs and score them on correctness. If quality drops more than 5 percent, your routing table is too aggressive about downgrading.

Escalation rate. In escalation patterns, track how often requests escalate from Haiku to Sonnet or Opus. Over 20 percent escalation means your Haiku threshold is too high.

Latency impact. Routing adds latency from classification. Measure the total request time including routing overhead. If overhead exceeds 500ms, simplify the classifier.

Common Routing Mistakes

Over-routing to Haiku. The biggest mistake is routing complex tasks to Haiku to save money. The cost of debugging wrong output far exceeds the savings on tokens.

Static routing tables. Task complexity evolves as your codebase grows. A task that was simple six months ago may now involve complex dependencies. Review and update your routing table monthly.

Ignoring context size. A task that sounds simple (“add error handling”) may require reading 50 files of context, which benefits from Opus’s superior context processing. Factor in context size, not just task description.

Know your costs → Use our Claude Code Cost Calculator to estimate your monthly spend.

Frequently Asked Questions

Does model routing add significant latency?

Keyword-based routing adds under 1ms. Classifier-based routing using Haiku adds 200-500ms. For most applications this is acceptable given the 70-80 percent cost savings. For latency-critical systems use keyword routing.

Can I route based on user tier instead of task type?

Yes. Many SaaS applications route free-tier users to Haiku, paid users to Sonnet, and enterprise users to Opus. This aligns model costs with revenue per user.

What if the router picks the wrong model?

With keyword routing, wrong selections happen 10-15 percent of the time. The impact is either slightly lower quality (task went to cheaper model) or slightly higher cost (task went to more expensive model). Neither is catastrophic. Escalation patterns provide a safety net.

Is it worth building a custom router for a small team?

For teams spending under $100/month on Claude API, manual model selection is sufficient. Above $500/month, a routing layer pays for itself within the first month. Use the Model Selector tool for manual selection until your volume justifies automation.