LLM Routing
With the rapid proliferation of large language models (LLM) — each optimized for different strengths, style, or latency/cost profile — routing has become an essential technique to operationalize the use of different models.
Arch provides three distinct routing approaches to meet different use cases:
Model-based Routing: Direct routing to specific models using provider/model names
Alias-based Routing: Semantic routing using custom aliases that map to underlying models
Preference-aligned Routing: Intelligent routing using the Arch-Router model based on context and user-defined preferences
This enables optimal performance, cost efficiency, and response quality by matching requests with the most suitable model from your available LLM fleet.
Routing Methods
Model-based Routing
Direct routing allows you to specify exact provider and model combinations using the format provider/model-name
:
Use provider-specific names like
openai/gpt-4o
oranthropic/claude-3-5-sonnet-20241022
Provides full control and transparency over which model handles each request
Ideal for production workloads where you want predictable routing behavior
Alias-based Routing
Alias-based routing lets you create semantic model names that decouple your application from specific providers:
Use meaningful names like
fast-model
,reasoning-model
, orarch.summarize.v1
(see Model Aliases)Maps semantic names to underlying provider models for easier experimentation and provider switching
Ideal for applications that want abstraction from specific model names while maintaining control
Preference-aligned Routing (Arch-Router)
Traditional LLM routing approaches face significant limitations: they evaluate performance using benchmarks that often fail to capture human preferences, select from fixed model pools, and operate as “black boxes” without practical mechanisms for encoding user preferences.
Arch’s preference-aligned routing addresses these challenges by applying a fundamental engineering principle: decoupling. The framework separates route selection (matching queries to human-readable policies) from model assignment (mapping policies to specific LLMs). This separation allows you to define routing policies using descriptive labels like Domain: 'finance', Action: 'analyze_earnings_report'
rather than cryptic identifiers, while independently configuring which models handle each policy.
The Arch-Router model automatically selects the most appropriate LLM based on:
Domain Analysis: Identifies the subject matter (e.g., legal, healthcare, programming)
Action Classification: Determines the type of operation (e.g., summarization, code generation, translation)
User-Defined Preferences: Maps domains and actions to preferred models using transparent, configurable routing decisions
Human Preference Alignment: Uses domain-action mappings that capture subjective evaluation criteria, ensuring routing aligns with real-world user needs rather than just benchmark scores
This approach supports seamlessly adding new models without retraining and is ideal for dynamic, context-aware routing that adapts to request content and intent.
Model-based Routing Workflow
For direct model routing, the process is straightforward:
Client Request
The client specifies the exact model using provider/model format (
openai/gpt-4o
).Provider Validation
Arch validates that the specified provider and model are configured and available.
Direct Routing
The request is sent directly to the specified model without analysis or decision-making.
Response Handling
The response is returned to the client with optional metadata about the routing decision.
Alias-based Routing Workflow
For alias-based routing, the process includes name resolution:
Client Request
The client specifies a semantic alias name (
reasoning-model
).Alias Resolution
Arch resolves the alias to the actual provider/model name based on configuration.
Model Selection
If the alias maps to multiple models, Arch selects one based on availability and load balancing.
Request Forwarding
The request is forwarded to the resolved model.
Response Handling
The response is returned with optional metadata about the alias resolution.
Preference-aligned Routing Workflow (Arch-Router)
For preference-aligned dynamic routing, the process involves intelligent analysis:
Prompt Analysis
When a user submits a prompt without specifying a model, the Arch-Router analyzes it to determine the domain (subject matter) and action (type of operation requested).
Model Selection
Based on the analyzed intent and your configured routing preferences, the Router selects the most appropriate model from your available LLM fleet.
Request Forwarding
Once the optimal model is identified, our gateway forwards the original prompt to the selected LLM endpoint. The routing decision is transparent and can be logged for monitoring and optimization purposes.
Response Handling
After the selected model processes the request, the response is returned through the gateway. The gateway can optionally add routing metadata or performance metrics to help you understand and optimize your routing decisions.
Arch-Router
The Arch-Router is a state-of-the-art preference-based routing model specifically designed to address the limitations of traditional LLM routing. This compact 1.5B model delivers production-ready performance with low latency and high accuracy while solving key routing challenges.
Addressing Traditional Routing Limitations:
Human Preference Alignment Unlike benchmark-driven approaches, Arch-Router learns to match queries with human preferences by using domain-action mappings that capture subjective evaluation criteria, ensuring routing decisions align with real-world user needs.
Flexible Model Integration The system supports seamlessly adding new models for routing without requiring retraining or architectural modifications, enabling dynamic adaptation to evolving model landscapes.
Preference-Encoded Routing Provides a practical mechanism to encode user preferences through domain-action mappings, offering transparent and controllable routing decisions that can be customized for specific use cases.
To support effective routing, Arch-Router introduces two key concepts:
Domain – the high-level thematic category or subject matter of a request (e.g., legal, healthcare, programming).
Action – the specific type of operation the user wants performed (e.g., summarization, code generation, booking appointment, translation).
Both domain and action configs are associated with preferred models or model variants. At inference time, Arch-Router analyzes the incoming prompt to infer its domain and action using semantic similarity, task indicators, and contextual cues. It then applies the user-defined routing preferences to select the model best suited to handle the request.
In summary, Arch-Router demonstrates:
Structured Preference Routing: Aligns prompt request with model strengths using explicit domain–action mappings.
Transparent and Controllable: Makes routing decisions transparent and configurable, empowering users to customize system behavior.
Flexible and Adaptive: Supports evolving user needs, model updates, and new domains/actions without retraining the router.
Production-Ready Performance: Optimized for low-latency, high-throughput applications in multi-model environments.
Implementing Routing
Model-based Routing
For direct model routing, configure your LLM providers with specific provider/model names:
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
- model: openai/gpt-4o
access_key: $OPENAI_API_KEY
- model: anthropic/claude-3-5-sonnet-20241022
access_key: $ANTHROPIC_API_KEY
Clients specify exact models:
# Direct provider/model specification
response = client.chat.completions.create(
model="openai/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello!"}]
)
response = client.chat.completions.create(
model="anthropic/claude-3-5-sonnet-20241022",
messages=[{"role": "user", "content": "Write a story"}]
)
Alias-based Routing
Configure semantic aliases that map to underlying models:
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
- model: openai/gpt-4o
access_key: $OPENAI_API_KEY
- model: anthropic/claude-3-5-sonnet-20241022
access_key: $ANTHROPIC_API_KEY
model_aliases:
# Model aliases - friendly names that map to actual provider names
fast-model:
target: gpt-4o-mini
reasoning-model:
target: gpt-4o
creative-model:
target: claude-3-5-sonnet-20241022
Clients use semantic names:
# Using semantic aliases
response = client.chat.completions.create(
model="fast-model", # Routes to best available fast model
messages=[{"role": "user", "content": "Quick summary please"}]
)
response = client.chat.completions.create(
model="reasoning-model", # Routes to best reasoning model
messages=[{"role": "user", "content": "Solve this complex problem"}]
)
Preference-aligned Routing (Arch-Router)
To configure preference-aligned dynamic routing, you need to define routing preferences that map domains and actions to specific models:
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
- model: openai/gpt-4o
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code understanding
description: understand and explain existing code snippets, functions, or libraries
- name: complex reasoning
description: deep analysis, mathematical problem solving, and logical reasoning
- model: anthropic/claude-3-5-sonnet-20241022
access_key: $ANTHROPIC_API_KEY
routing_preferences:
- name: creative writing
description: creative content generation, storytelling, and writing assistance
- name: code generation
description: generating new code snippets, functions, or boilerplate based on user prompts
Clients can let the router decide or use aliases:
# Let Arch-Router choose based on content
response = client.chat.completions.create(
messages=[{"role": "user", "content": "Write a creative story about space exploration"}]
# No model specified - router will analyze and choose claude-3-5-sonnet-20241022
)
Combining Routing Methods
You can combine static model selection with dynamic routing preferences for maximum flexibility:
llm_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
- model: openai/gpt-4o
access_key: $OPENAI_API_KEY
routing_preferences:
- name: complex_reasoning
description: deep analysis and complex problem solving
- model: anthropic/claude-3-5-sonnet-20241022
access_key: $ANTHROPIC_API_KEY
routing_preferences:
- name: creative_tasks
description: creative writing and content generation
model_aliases:
# Model aliases - friendly names that map to actual provider names
fast-model:
target: gpt-4o-mini
reasoning-model:
target: gpt-4o
# Aliases that can also participate in dynamic routing
creative-model:
target: claude-3-5-sonnet-20241022
This configuration allows clients to:
Use direct model selection:
model="fast-model"
Let the router decide: No model specified, router analyzes content
Example Use Cases
Here are common scenarios where Arch-Router excels:
Coding Tasks: Distinguish between code generation requests (“write a Python function”), debugging needs (“fix this error”), and code optimization (“make this faster”), routing each to appropriately specialized models.
Content Processing Workflows: Classify requests as summarization (“summarize this document”), translation (“translate to Spanish”), or analysis (“what are the key themes”), enabling targeted model selection.
Multi-Domain Applications: Accurately identify whether requests fall into legal, healthcare, technical, or general domains, even when the subject matter isn’t explicitly stated in the prompt.
Conversational Routing: Track conversation context to identify when topics shift between domains or when the type of assistance needed changes mid-conversation.
Best practicesm
💡Consistent Naming: Route names should align with their descriptions.
❌ Bad:
` {"name": "math", "description": "handle solving quadratic equations"} `
✅ Good:
` {"name": "quadratic_equation", "description": "solving quadratic equations"} `
💡 Clear Usage Description: Make your route names and descriptions specific, unambiguous, and minimizing overlap between routes. The Router performs better when it can clearly distinguish between different types of requests.
❌ Bad:
` {"name": "math", "description": "anything closely related to mathematics"} `
✅ Good:
` {"name": "math", "description": "solving, explaining math problems, concepts"} `
💡Nouns Descriptor: Preference-based routers perform better with noun-centric descriptors, as they offer more stable and semantically rich signals for matching.
💡Domain Inclusion: for best user experience, you should always include domain route. This help the router fall back to domain when action is not