How to Route AI Requests to the Best LLM Automatically

Why One Model for Everything Is a Bad Idea

Sending every AI request to your best, most capable model feels safe. You never have to worry about a task being underpowered. But it is also massively wasteful — and the waste compounds at scale.

Consider a typical product that uses AI: it might run sentiment analysis on user feedback, summarize support tickets, generate product descriptions, answer customer questions, and occasionally do complex multi-step reasoning for internal reports. Those tasks have wildly different requirements. Sentiment analysis on a short text is a trivial job for a small, cheap model. Complex report generation might genuinely need a frontier model with a long context window.

Using GPT-4o or Claude Opus for sentiment analysis is like hiring a senior architect to paint your fence. Technically capable, but extremely expensive and completely unnecessary.

Automatic LLM routing fixes this by matching each task to the right model based on what that task actually needs.

The Core Concepts Behind Routing

Good routing decisions are based on a few key signals:

Task complexity — Simple classification, short summarization, and keyword extraction are low-complexity. Multi-step reasoning, code generation, and long document analysis are high-complexity.

Input length — Some models handle long contexts better than others. A 100,000-token document needs a model with a large enough context window to actually process it.

Required capabilities — Does the task need tool use? Vision? Structured JSON output? Code execution? These requirements eliminate models that lack those features.

Cost ceiling — Some tasks have a hard maximum you are willing to spend per call. Routing should respect that and find the best model within that constraint.

Latency sensitivity — A real-time chat interface needs fast responses. A background processing job can tolerate higher latency from a more thorough model.

How to Set Up Routing Rules

The most straightforward routing setup uses a tiered model approach. Define three tiers, assign tasks to each, and let the gateway handle the rest.

// Routing config example using RBAOS routing rules
const routingConfig = {
  rules: [
    {
      name: 'simple-tasks',
      conditions: {
        maxInputTokens: 2000,
        taskTypes: ['classification', 'sentiment', 'extraction', 'short-summary']
      },
      targetModel: 'gemini-flash-2.0',  // fast and cheap
      fallback: 'claude-haiku-3'
    },
    {
      name: 'standard-tasks',
      conditions: {
        maxInputTokens: 20000,
        taskTypes: ['summarization', 'qa', 'drafting', 'translation']
      },
      targetModel: 'claude-sonnet-4',
      fallback: 'gpt-4o-mini'
    },
    {
      name: 'complex-tasks',
      conditions: {
        taskTypes: ['reasoning', 'code-generation', 'analysis', 'long-context']
      },
      targetModel: 'claude-opus-4',
      fallback: 'gpt-4o'
    }
  ]
};

This setup alone can cut AI API costs significantly without touching response quality. Simple tasks stop consuming expensive model budget. Complex tasks still get the horsepower they need.

Building Better Task Signals

The routing rules above work well when you know the task type in advance. But what if requests come in from users and you need to classify them first?

The answer is a lightweight pre-classifier — a fast, cheap model call that categorizes the incoming request, then routes the actual request appropriately.

async function routeRequest(userMessage) {
  // Step 1: classify the task using a cheap fast model
  const classifyResponse = await fetch('https://api.rbaos.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${process.env.RBAOS_API_KEY}` },
    body: JSON.stringify({
      model: 'gemini-flash-2.0',
      messages: [
        {
          role: 'system',
          content: 'Classify this request as: simple, standard, or complex. Reply with one word only.'
        },
        { role: 'user', content: userMessage }
      ],
      max_tokens: 5
    })
  });

  const taskType = await classifyResponse.json();
  const tier = taskType.content[0].text.trim().toLowerCase();

  // Step 2: route to the right model based on classification
  const modelMap = {
    simple: 'gemini-flash-2.0',
    standard: 'claude-sonnet-4',
    complex: 'claude-opus-4'
  };

  return modelMap[tier] || 'claude-sonnet-4';
}

The pre-classifier call itself costs almost nothing. The savings on the main call more than pay for it.

Routing Based on Output Format Requirements

Task type is not the only signal. Output format requirements are often even more reliable routing signals.

If a task requires valid JSON output, you need a model that handles structured output reliably. If it needs citation with sources, you need a model with web access or RAG support. If it needs code that will actually run, you want a model with strong code benchmarks.

Routing based on output requirements tends to be more stable than routing on task complexity, because output requirements do not change based on context.

Monitoring Your Routing Performance

Routing is not a set-and-forget configuration. You need to watch what is happening:

• What percentage of requests are hitting each tier
• Are fallback routes being triggered often (which suggests a provider reliability issue)
• What is the cost per request across different task types
• Are any task types consistently underperforming in quality (which suggests they need a higher-tier model)

The RBAOS platform gives you this visibility in a single dashboard across all providers. You can see per-model usage, latency distribution, and cost breakdown without piecing it together from multiple provider portals.

For a full explanation of how the gateway handles routing under the hood, smart LLM routing explained covers the technical detail. For information on how much this kind of optimization typically saves in practice, cutting AI API costs by 60 percent has real numbers.

The Compounding Effect at Scale

At low volume, routing optimization feels like a nice-to-have. At scale, it becomes critical. A team processing 500,000 AI calls per month at an average cost of $0.01 per call is spending $5,000 per month. Routing 70% of those calls to cheaper models that handle them just as well for those tasks drops that figure dramatically.

The pricing page covers what routing capabilities are available at each RBAOS tier. Start with a basic three-tier setup, monitor for a week, then refine based on what you actually see in the data.