Model Routing Guide – Which AI Model for Which Task?

The Problem: One Model for Everything

Most teams make the same mistake: they use GPT-4o or Claude Sonnet for everything. Every email, every summary, every data extraction, every code review.

That's like taking a Porsche to the bakery. It works, but you're burning money.

The reality: 70% of all AI tasks don't need a frontier model. They need a fast, cheap model that gets the job done. The remaining 30% might even need more than a frontier model – they need a reasoning model.

Model routing solves exactly this: The right model for the right task. Automatically.

The Routing Matrix: 4 Tiers

We split tasks into four tiers – each with an optimal model type:

Tier 1: Commodity Tasks (Budget Models)

Cost: $0.40–0.60 / 1M Output Tokens

Recommended models:

• Gemini 2.0 Flash – $0.40/1M output, unbeatable price
• GPT-4o mini – $0.60/1M output, solid all-round performance
• DeepSeek V3 – $1.10/1M output, strong price-performance ratio
• Llama 4 Maverick (via Groq) – $0.60/1M output, open-source

Tier 2: Professional Tasks (Mid-Tier Models)

Cost: $4.00–6.00 / 1M Output Tokens

Recommended models:

• Claude 3.5 Haiku – $4.00/1M output, Anthropic quality at mid-tier price
• Mistral Large – $6.00/1M output, strong European model

Tier 3: Frontier Tasks (Premium Models)

Cost: $10.00–15.00 / 1M Output Tokens

Recommended models:

• GPT-4o – $10.00/1M output, the all-rounder
• Claude 3.5 Sonnet – $15.00/1M output, best for code + creative writing
• Gemini 2.5 Pro – $10.00/1M output, massive context window

Tier 4: Reasoning Tasks (Specialized Models)

Cost: $2.19–40.00 / 1M Output Tokens

Recommended models:

• DeepSeek R1 – $2.19/1M output, open-source reasoning at budget price
• OpenAI o3 – $40.00/1M output, strongest reasoning, but expensive

Routing in Practice: An Example

Imagine a support team processing 1,000 tickets per month with AI:

The quality of responses? Identical. A "where's my package?" ticket doesn't need a frontier model.

The Privacy Layer: Data Sensitivity as a Routing Criterion

Model routing isn't just a cost question – it's also a data protection question.

Our recommendation: a three-tier privacy routing:

1. 🟢 Public data → Cloud models (GPT-4o, Claude, Gemini)
2. 🟡 Internal data → EU-hosted models (Mistral, EU Bedrock)
3. 🔴 Sensitive data → Local models (Llama local, Nemotron Nano)

The principle: The data determines the model, not the other way around.

Sensitive HR data, salary information, or customer data should never go to US cloud APIs – regardless of how good the model is. A local Llama model is perfectly sufficient for classifying personnel files.

🔐 Deep Dive: Our Privacy Router Guide shows step-by-step how to implement the three-zone model – including EU AI Act compliance and kill-switch hierarchy.

How to Implement Model Routing

Option 1: Manual Routing (Immediately actionable)

Create a simple decision table for your team:

IF task = classification/extraction → Gemini Flash
IF task = content/code → Claude Haiku
IF task = strategy/creative → Claude Sonnet
IF task = reasoning/debugging → DeepSeek R1
IF data = sensitive → Local model

Option 2: Automated Routing (with make.com or n8n)

Build a workflow that analyzes incoming requests and automatically routes to the right model:

1. Classifier (Gemini Flash, costs almost nothing): Evaluates complexity + data sensitivity
2. Router: Forwards to the appropriate model
3. Fallback: If the budget model fails, upgrade to next tier

Option 3: OpenRouter as a Meta-Layer

OpenRouter aggregates 200+ models under one API. You can:

• Switch models per request
• Define fallback chains
• Track costs per model

The Cost Calculation

Run the numbers for your own setup: Our AI Token Calculator shows you what you get for your budget with every provider.

A typical company that introduces model routing saves 60–80% of AI costs – with equal or better output quality. Why better? Because reasoning models at reasoning tasks are actually better than frontier models.

The Bottom Line

Model routing isn't a nice-to-have. It's the first optimization every company should make before talking about AI strategy.

The logic is simple:

• 70% of your tasks need the cheapest model
• 25% of your tasks need a good model
• 5% of your tasks need the best model
• 0% of your tasks need the same model for everything

Whoever understands this doesn't just save money – they build better AI systems.

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🧮 What does your AI setup really cost? Run the numbers with our AI Token Calculator – free, no signup required.

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Want to introduce model routing for your team? Talk to us – we'll build the routing logic with you. In days, not months.