Intelligent LLM Routing: How to Pick the Right Model Per Query

The model selection problem

Not every query deserves the same model. Asking GPT-5.2 to answer "what's 2+2" is like hiring a litigation partner to proofread a grocery list. On the other end, routing a complex legal analysis through Gemini Flash because it is fast and cheap will give you a fast, cheap, and wrong answer.

This is the core tension in LLM routing: every model has a different cost, latency profile, and set of strengths. GPT-5.2 excels at code generation and structured reasoning. Claude Sonnet 4.5 handles creative writing and mathematical proofs with more nuance. Gemini 3 Flash is unbeatable on translation and quick factual lookups when you need sub-second responses. When your application sends every request to a single model, you are either overpaying for simple queries or underserving complex ones.

Intelligent routing solves this by matching each query to the model best suited for it. The result is lower cost, faster responses, and higher quality -- all at the same time.

Static routing: the baseline

The simplest approach is static routing. You hardcode a model per endpoint or per feature. Your chatbot uses Claude. Your code assistant uses GPT. Your translation pipeline uses Gemini.

# Static routing — simple but inflexible
if feature == "chat":
    model = "claude-sonnet-4.5"
elif feature == "code":
    model = "gpt-5.2"
elif feature == "translate":
    model = "gemini-3-flash"

This works when your application has clearly separated use cases. But it falls apart the moment users send mixed queries to the same endpoint. A chat interface receives code questions, translation requests, creative writing prompts, and factual lookups in the same conversation. Static routing cannot adapt.

It also means you are locked into a model even when it is the wrong choice for a particular query. Every "write me a haiku" that hits your GPT-5.2 endpoint costs you more than it needs to and returns slower than it should.

Heuristic routing: fast classification

A step up from static routing is heuristic classification. Instead of routing by endpoint, you inspect the query itself and classify it into a task category using pattern matching.

The key insight is that most queries contain strong lexical signals. A message containing "debug", "function", "implement", or "refactor" is almost certainly a code task. One mentioning "translate" or "in Spanish" is a translation task. Keywords like "poem", "story", or "creative writing" signal creative work.

Regex-based classification runs in microseconds. There is zero latency overhead -- no extra LLM call, no embedding lookup, no network round trip. You parse the query string, match against a set of patterns, and get a task category back immediately.

The tradeoff is precision. Heuristics rely on surface-level keyword matching. A query like "write a function that translates poetry" contains signals for code, translation, and creative writing simultaneously. You need sensible priority ordering and fallback rules to handle ambiguity. But for the majority of real-world traffic, simple pattern matching gets the classification right.

For a broader overview of these concepts, see our LLM routing guide.

Data-driven routing: learning from history

Heuristics give you a good starting point. Data-driven routing takes it further by analyzing what actually happened with past requests.

The idea is straightforward: look at your request logs, find which models performed best for each query pattern, and use that historical evidence to inform future routing decisions. If Claude Sonnet consistently produces better results for legal analysis queries while GPT-5.2 handles code generation faster and cheaper, the router should learn that mapping automatically.

LLMWise provides optimization policies that do exactly this. You define a goal -- balanced quality, lowest latency, or minimum cost -- and the system analyzes your request history to recommend a primary model and fallback chain for each query category. The optimization engine runs regression analysis on your actual usage data, not generic benchmarks.

This matters because benchmark performance does not always predict real-world performance for your specific workload. A model that scores well on HumanEval might underperform on the particular style of code your users write. Historical data captures those patterns.

To compare how different models perform on your actual workloads, see our guide on how to compare LLM models.

Cost-constrained routing

Raw performance is only half the equation. Cost-constrained routing adds budget guardrails: route to the cheapest model that meets a quality threshold for each task type.

This is where intelligent routing delivers the biggest ROI. Consider a typical API workload:

• 60% of queries are simple factual lookups or short-form answers. Gemini Flash handles these at a fraction of the cost.
• 25% are moderate complexity tasks (summarization, rewriting, structured extraction). Mid-tier models perform well.
• 15% are genuinely complex reasoning, code generation, or creative tasks that benefit from frontier models.

Without cost-constrained routing, you pay frontier model prices for 100% of your traffic. With it, you pay frontier prices only for the 15% that needs it. The remaining 85% routes to cheaper, faster models with no meaningful quality degradation.

For a deeper look at cutting LLM costs in production, read how to reduce LLM API costs.

How LLMWise Auto mode works

LLMWise Auto mode implements heuristic routing with zero configuration required. When you set model: "auto" in your API request, the system runs a classification pipeline before dispatching to any provider.

Here is how the pipeline works:

1. Extract the query text from the last user message in the conversation.
2. Run regex patterns against the text to detect task categories: code, math, creative writing, and translation each have their own pattern set.
3. Apply length-based fallback -- short queries (under 60 characters) route to a fast model for quick factual lookups. Longer unclassified queries route to a strong general-purpose model for deeper analysis.
4. Map the category to a model using a routing table: code and analysis go to GPT-5.2, math and creative go to Claude Sonnet 4.5, translation and quick facts go to Gemini 3 Flash.
5. Handle multimodal inputs -- if the request includes images, it routes to a vision-capable model regardless of text classification.

To use Auto mode, set the model field to "auto" in your request:

curl -X POST https://llmwise.ai/api/v1/chat \
  -H "Authorization: Bearer mm_sk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [
      {"role": "user", "content": "Refactor this Python function to use async/await"}
    ],
    "stream": true
  }'

The response stream includes the actual model that was selected, so you always know which model handled your request:

data: {"model": "gpt-5.2", "delta": "Here's the refactored", "done": false}
data: {"model": "gpt-5.2", "delta": " async version...", "done": false}
data: [DONE]

In this example, the query contains "refactor", "Python", "function", and "async" -- all strong code signals -- so Auto mode routes to GPT-5.2.

Auto mode pairs well with failover routing. If the selected model is unavailable or rate-limited, LLMWise Mesh mode can fall back to alternative models in the same quality tier.

When to use manual vs auto routing

Auto routing is the right default for general-purpose traffic. It handles the 80% case well: most queries have clear task signals, and the heuristic classification matches them to appropriate models without any configuration.

Manual model selection makes sense when:

• You have tested a specific model for a particular workflow and confirmed it outperforms alternatives. For example, if you have benchmarked GPT-5.2 against Claude Sonnet on your exact prompt templates and one wins decisively.
• You need deterministic routing for compliance or audit reasons. Auto mode's classification is deterministic given the same input, but the routing table may evolve.
• Your queries lack lexical signals. If your prompts are heavily templated with minimal user-supplied text, the heuristic classifier has less to work with.
• You are using specialized models like fine-tuned variants or domain-specific endpoints that are not in the Auto routing table.

A practical hybrid approach: use Auto for your general chat interface and manual selection for specialized pipelines where you have validated specific model performance.

Getting started with routing

Here are practical steps to implement intelligent LLM routing in your application:

1. Start with Auto mode. Set model: "auto" and monitor the model field in responses to see which models are being selected for your traffic.
2. Review your traffic distribution. Use the LLMWise dashboard to see how queries break down by category and which models are handling them.
3. Identify outliers. Look for query categories where Auto mode's selection does not match your quality expectations. These are candidates for manual overrides.
4. Set up optimization policies. Define your goal (balanced, latency, or cost) and let the data-driven optimizer refine routing based on your actual request history.
5. Add failover chains. Combine Auto mode with Mesh routing so that model failures do not become user-facing errors.
6. Monitor cost per query. Track whether routing changes actually reduce your per-query cost without degrading response quality.

Intelligent routing is not a one-time configuration. As models improve, pricing changes, and your traffic patterns evolve, the optimal routing strategy shifts with them. The goal is to build a system that adapts -- starting with heuristics, refining with data, and always keeping cost and quality in balance.