3 Model Routing Strategies That Cut Our Customers' Costs by 40%

We analyzed aggregated usage patterns across our customer base (anonymized) and found that most teams overspend on AI by using a single model for everything. Here are three routing strategies that consistently cut costs by 30-50%.

Strategy 1: Complexity-Based Routing

The simplest and most effective approach. Classify incoming requests by complexity, then route accordingly:

def route_model(prompt: str, max_tokens: int) -> str:
    estimated_complexity = len(prompt) + max_tokens

    if estimated_complexity < 500:
        return "tokenfast/claude-haiku-4-5-20251001"  # $1/M in, $5/M out
    elif estimated_complexity < 5000:
        return "tokenfast/claude-sonnet-4-6"  # $5/M in, $25/M out
    else:
        return "tokenfast/claude-opus-4-6"  # $30/M in, $150/M out

Real result: One customer moved from 100% Opus to this split and saw a 43% cost reduction with less than 2% quality degradation on their evaluation suite.

Strategy 2: Cascade (Try Cheap First)

Send the request to a cheaper model first. If the response passes your quality check, use it. If not, retry with a more capable model.

async def cascade_request(messages):
    # Try the fast, cheap model first
    response = await client.chat.completions.create(
        model="tokenfast/claude-haiku-4-5-20251001",
        messages=messages,
    )

    # Quality gate: check if response meets criteria
    if passes_quality_check(response):
        return response

    # Fall back to the more capable model
    return await client.chat.completions.create(
        model="tokenfast/claude-sonnet-4-6",
        messages=messages,
    )

This works especially well for customer support and classification tasks where 70-80% of requests are straightforward.

Strategy 3: Task-Specific Model Assignment

Different models have different strengths. Map your task types to optimal models:

Task	Recommended Model	Why
Code generation	Claude 4.6 Opus	Best at complex, multi-file code
Summarization	Claude Haiku 4.5	Fast, accurate, cheap
Data extraction	Claude 4.6 Sonnet	Great structured output
Creative writing	Claude 4.6 Opus	Strongest at nuance
Classification	Claude Haiku 4.5	Speed > depth
Long document analysis	Gemini 3.1 Pro	1M token context

The Math

A team spending $10,000/month on Claude Opus for everything could realistically spend $5,500-6,500/month with smart routing — and get faster responses for simple tasks as a bonus.

Through TokenFast, switching models is just changing a string. No separate API keys, no different SDKs, no billing complexity. That's what makes these strategies practical to implement.