What Is Multi-Model Routing and Why It Cuts LLM Costs
The problem with single-model strategies
Most teams pick one LLM — usually GPT-5.6 or Claude Sonnet 4.6 — and route every request to it. This is simple but expensive. A customer asking "what are your business hours?" costs the same as a request to analyze a 50-page legal contract. The simple query could be handled by a model that costs 90% less.
Multi-model routing solves this by matching each request to the model that can handle it at the lowest cost. The result: 40-70% lower API bills with no noticeable quality loss.
What is multi-model routing?
Multi-model routing is a gateway pattern where a single API endpoint accepts requests and forwards each one to the most appropriate model based on:
- Task complexity — simple queries go to cheap models, complex reasoning goes to frontier models
- Context length — short contexts use standard models, long contexts use extended-context models
- Latency requirements — real-time chat uses fast models, batch jobs use slower but cheaper models
- Cost targets — route to the cheapest model that meets a quality threshold
The key insight: not every request needs a frontier model. Most don't.
How much can you save?
Consider a typical customer support application with 10,000 requests per day:
| Request type | % of traffic | Model | Cost per 1M tokens | Daily cost | |---|---|---|---|---| | Simple FAQ | 60% | DeepSeek V4 ($0.27/$1.10) | ~$0.50 blended | ~$1.50 | | General chat | 25% | GLM-5.2 ($1.40/$4.40) | ~$2.00 blended | ~$2.50 | | Complex reasoning | 10% | GPT-5.6 ($2/$8) | ~$4.00 blended | ~$2.00 | | Long context | 5% | Gemini 2.5 Pro ($1.25/$5) | ~$2.50 blended | ~$0.63 |
Total with routing: ~$6.63/day
Compare to routing everything to GPT-5.6: ~$40/day for the same volume.
That is an 83% cost reduction. Even a more conservative estimate — routing only 50% of traffic to cheaper models — saves 40-50%.
How routing works in practice
Rule-based routing
The simplest approach is rule-based routing. You define rules that map request characteristics to models:
# Pseudocode for rule-based routing
def route_model(messages, max_cost=None):
total_tokens = sum(len(m["content"]) for m in messages)
if total_tokens > 500_000:
return "gemini-2.5-pro" # Long context
elif len(messages) == 1 and len(messages[0]["content"]) < 100:
return "deepseek-v4-flash" # Simple query
elif any(kw in messages[-1]["content"] for kw in ["analyze", "review", "debug"]):
return "claude-sonnet-4-6" # Complex reasoning
else:
return "gpt-5" # DefaultThis approach is predictable and easy to reason about, but requires manual tuning.
Automatic routing
Riven Auto handles routing automatically. Instead of writing rules, you set model: "riven-auto" and the gateway selects the best model for each message based on:
- Message content and complexity
- Available models and their capabilities
- Current pricing and latency
- Your budget settings
This is the most hands-off approach — you get cost savings without managing rules.
Hybrid routing
The most sophisticated approach combines rules with automatic fallback:
- Explicit model selection for critical paths (e.g., "always use Claude for legal analysis")
- Riven Auto for general chat
- Cost-capped routing for high-volume batch jobs
Implementing multi-model routing with Riven
Riven's gateway supports multi-model routing natively through the OpenAI-compatible API:
Per-request model selection
# Simple query → cheap model
curl https://api.rivenai.io/v1/chat/completions \
-H "Authorization: Bearer rvn_..." \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-chat",
"messages": [{"role": "user", "content": "What are your business hours?"}],
"max_tokens": 100
}'
# Complex reasoning → frontier model
curl https://api.rivenai.io/v1/chat/completions \
-H "Authorization: Bearer rvn_..." \
-H "Content-Type: application/json" \
-d '{
"model": "claude-sonnet-4-6",
"messages": [{"role": "user", "content": "Analyze this contract for risk clauses..."}],
"max_tokens": 2000
}'Automatic routing
# Let Riven Auto pick the best model
curl https://api.rivenai.io/v1/chat/completions \
-H "Authorization: Bearer rvn_..." \
-H "Content-Type: application/json" \
-d '{
"model": "riven-auto",
"messages": [{"role": "user", "content": "Explain quantum computing to a 5 year old"}],
"max_tokens": 500
}'Using the SDK
from riven import Riven
client = Riven()
# Route by model
response = client.chat.completions.create(
model="deepseek-chat", # Cheap model for simple tasks
messages=[{"role": "user", "content": "Summarize this in one sentence: ..."}],
)
# Let Riven Auto decide
response = client.chat.completions.create(
model="riven-auto", # Gateway picks the best model
messages=[{"role": "user", "content": "Write a Python web scraper..."}],
)Governance and cost control
Multi-model routing is powerful, but it needs guardrails. Riven Control provides:
- Per-key budgets — cap spending per API key
- Per-key rate limits — prevent runaway costs
- Model allowlists — restrict which models each key can access
- Full audit log — every request is metered and attributable
- Real-time usage tracking — monitor costs as they accrue
This means you can give each developer or service their own key with its own budget, and routing will optimize within that budget automatically.
Common routing patterns
| Pattern | When to use | Example | |---|---|---| | Tier-based | Different quality tiers for different users | Free users → DeepSeek, Pro users → GPT-5.6 | | Task-based | Different models for different task types | Classification → cheap model, generation → frontier | | Cost-capped | Hard budget constraints | Route to cheapest available model within $X/1M | | Latency-optimized | Real-time applications | Route to fastest model that can handle the request | | Fallback | High availability | If model A fails, fall back to model B |
Getting started
Multi-model routing is available on all Riven plans, including the free tier. Sign up to get an API key and start routing requests across 75+ models with transparent per-token pricing.
For a deeper dive into which models to use for which tasks, see our model choosing guide.