LLM Routing: How to Choose the Right AI Model for Each Task
How LLM routing works: matching tasks to models by complexity, quality, latency and cost, static rules, classifier routers and cascades, fallbacks, and evaluation-based routing decisions.
Quick answer
LLM routing sends each request or step to the model that meets its quality requirement at the lowest acceptable cost and latency. Start with static routing by task type (a small model for classification and extraction, a stronger model for complex reasoning), backed by evaluations on your own data. Add a classifier router or a cascade (cheap model first, escalate when validation fails) only when traffic is varied enough to justify it. No model is best at everything, so re-evaluate routes when models change.
Where This Fits
Routing usually lives in an LLM gateway or AI service. It is a major lever for cost optimization, depends on evaluation for its decisions, and differs from AI orchestration, which coordinates whole flows.
What Should Drive Model Choice?
| Factor | Questions to ask |
|---|---|
| Quality | Does this model pass the evaluation set for this task? |
| Latency | Is the response fast enough for the user experience? |
| Cost | What is the cost per completed task, including retries? |
| Context length | Does the input fit comfortably? |
| Capabilities | Tool use, structured outputs, vision, audio, languages |
| Data handling | Where is data processed and retained? Is this data class allowed? |
Routing Strategies
Static by task: each task type has a configured model. Simple, predictable and usually the right start. Classifier router: a small model or rules estimate the difficulty or type of each request and pick a tier. Useful when one endpoint receives very varied requests. Cascade: try a cheaper model, validate the result, escalate to a stronger model on failure. Saves cost when most requests are easy, at the price of extra latency on hard ones.
Evaluation-Based Routing
Routing decisions should come from data. For each task, run your evaluation set through candidate models and record success rate, latency and cost. Choose the cheapest model that meets the quality bar, document the decision and re-test when providers release or update models. Monitor production signals (validation failures, escalations, user feedback) by route.
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Fallbacks and Availability
Routing also handles failure: when a provider returns errors or times out, fall back to another model or provider that has been evaluated for that task. Avoid silent fallbacks for tasks where consistency matters, such as extraction feeding financial systems, and log every fallback.
Implementation Options
- Configuration in an LLM gateway mapping tasks to models and fallbacks
- A routing function in your AI service, versioned with prompts
- Classifier routers trained or prompted on labelled examples
- Cascades with deterministic validation between tiers
- Feature flags to roll out routing changes gradually
Advantages and Limitations
Routing reduces cost and latency and improves resilience. It adds evaluation and maintenance work, routers can misclassify, cascades add latency for hard requests, and different models can produce subtly different behaviour that users notice. Keep the number of routes small and well tested.
How to Set Up Routing Step by Step
- 1. List AI tasks and their quality, latency and data requirements
- 2. Build evaluation sets per task
- 3. Test candidate models and record quality, latency and cost
- 4. Configure static routes with fallbacks
- 5. Monitor production metrics by route
- 6. Add classifier or cascade routing only where traffic varies widely
- 7. Re-evaluate when models change
Example Routing Configuration
Keep routing rules in configuration, versioned and tied to evaluation results, so changes are reviewed like code.
routes:
ticket_classification:
model: small-fast-model
fallback: [small-model-provider-b]
eval: classification_v4 # 96% accuracy at last run
reply_drafting:
model: large-model
fallback: [large-model-provider-b]
eval: drafting_v2
document_extraction:
cascade:
- model: small-vision-model
accept_if: schema_valid and totals_match
- model: large-vision-model
eval: extraction_v3
contract_review:
model: large-model
fallback: [] # no silent fallback for this taskRouting Inside Agents
Agents make many calls of different difficulty. Planning and final answers may need a strong model; tool argument formatting, summarizing tool results and classifying intermediate states often do not. Route agent steps by type, and watch for errors introduced at handoffs between models. Include agent-level success and cost in routing evaluations, not just per-step accuracy; see AI agent development.
Routing vs Orchestration
Model routing and orchestration are often confused. Routing answers one question per request or step: which model should handle this, given task type, difficulty, cost, latency and provider availability? Orchestration coordinates a whole workflow: the sequence of retrieval, model calls, tools, validation, retries and human approvals. A router is usually one component inside an orchestrated system, often implemented in the gateway, while orchestration lives in application or agent logic. See AI orchestration and AI agent orchestration for the broader picture.
Cost Governance for Routing
Routing is one of the strongest cost levers, so govern it like spending policy. Record which model handled each request and why, report cost and quality by route, and review routes when providers change prices or release models. Set per-feature budgets and let the router prefer cheaper models as budgets tighten, only where evaluation shows quality holds. Avoid routing changes that silently trade quality for cost: every change to routing rules should pass the same evaluation gates as prompt and model changes. Related practices are in AI inference optimization, LLM regression testing and AI platform engineering.
Worked Example
An illustrative scenario, not a client case: a support platform uses one large model for ticket classification, reply drafting and summarization. Evaluation shows a small model matches the large one on classification and summarization. Routing those tasks to the small model and keeping drafting on the larger one lowers cost substantially with no measurable quality change on the evaluation set.
Common Mistakes
- Choosing models from public leaderboards rather than your own tasks
- Routing on price without quality checks
- Too many routes to maintain
- Silent fallbacks for consistency-critical tasks
- Not re-testing after provider model updates
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Conclusion
Routing matches models to tasks using evidence. Start static, evaluate per task, add smarter routing only where it pays and re-test as models evolve. Related: LLM gateway and LLM cost optimization.
Common questions
Choosing which language model handles each request or step, based on the task's requirements for quality, latency, cost, context length, language or data handling.