Ranked comparison

LLM Router: Intelligent Model Selection for Every Request

An LLM router inspects each request and picks the best model based on task type, cost constraints, and latency requirements. No more hard-coding a single model for everything.

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Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.

Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

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Why teams start here first
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Starter
Auto lane only
Curated cheap model pool with no manual premium-model selection.
Teams
Premium when you need it
Manual GPT, Claude, and Gemini Pro access starts here.
Billing
Plan tokens first
Add-on credits only extend usage after included plan tokens are exhausted.
Evaluation criteria
Routing intelligenceLatency overheadFallback chainsCost awarenessEase of integration
1
LLMWise Auto RouterLLMWise

Heuristic routing that classifies queries via regex patterns and maps them to the optimal model in microseconds. No ML inference step means no added latency. If the chosen model is down, the mesh layer reroutes to a fallback automatically.

Zero routing latency - classification happens in microseconds, not millisecondsMesh mode provides automatic fallback routing when providers degradeOptimization engine analyzes historical data and recommends route changes
2
OpenRouter AutoOpenRouter

OpenRouter's auto mode selects from their full model catalog based on the prompt. Broad coverage but less transparent about routing logic, and you cannot customize the decision criteria.

Access to 300+ models for the router to choose fromSimple - just set model to 'auto' in the API callNo infrastructure to manage
3
Unify AIUnify

Benchmark-driven routing that uses public LLM benchmark scores to match queries to models. Good for teams that want transparent, data-backed routing decisions.

Routes based on published benchmark performanceTransparent routing logic you can inspectSupports custom routing policies
4
Portkey RoutingPortkey

Conditional routing with weight-based traffic splitting. Best for teams that want manual control over which requests go where, with A/B testing built in.

Weight-based traffic splitting for A/B testingConditional routing based on request metadataRetry and fallback chains configurable per route
Evidence snapshot

LLM Router: Intelligent Model Selection for Every Request scoring method

Ranking evidence from practical criteria teams use for real production traffic.

Criteria
5
evaluation dimensions used
Models ranked
4
candidates evaluated
Top pick
LLMWise Auto Router
current #1 recommendation
FAQ coverage
4
selection objections addressed
Our recommendation

For most teams, LLMWise's auto-router is the best starting point - it adds zero latency and handles 90%+ of routing decisions correctly out of the box. If you need to customize routing based on benchmarks, Unify AI gives you more transparency. For manual traffic splitting and A/B testing, Portkey offers the most control.

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Common questions

What is LLM routing?
LLM routing is the practice of directing each API request to the best-fit model based on task type, cost constraints, and latency requirements. Instead of hard-coding GPT-5 for everything, a router classifies each query and sends it to the model that best matches - sending simple tasks to cheap models and complex tasks to expensive ones.
Does LLM routing add latency?
It depends on the implementation. ML-based routers add 50-200ms for classification inference. LLMWise uses regex-based heuristic classification that runs in microseconds - effectively zero added latency. The time saved by picking the right model often outweighs any routing overhead.
How is LLM routing different from an LLM gateway?
A gateway provides the infrastructure layer - unified API, authentication, failover. A router adds the intelligence layer - deciding which model handles each request. LLMWise combines both: the gateway handles connectivity and the auto-router handles model selection.
Can I route based on cost?
Yes. Cost-based routing sends requests to the cheapest model that meets a minimum quality threshold. LLMWise's auto-router does this by default - simple queries like classification or extraction go to cheaper models while complex reasoning tasks go to frontier models.

Start on Auto, move up only when you need it

Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.

Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

Starter Auto laneTeams premium manual accessPlan tokens + add-ons
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