Master the n8n Model Selector Node: Dynamic AI Routing, Cost Optimization & Multi-Model Setup

Let’s be honest — if you’re building AI workflows in n8n, you’ve probably hit that moment where you think, “I wish I could easily switch between models like GPT-4, Claude, Mistral… depending on the situation.” Maybe it’s about cost. Maybe it’s about performance. Maybe you’re running experiments and want to compare different outputs side-by-side. Or maybe you’re building a system where flexibility is the whole point.

That’s where the n8n Model Selector Node comes in — and once you understand how it works, it’ll become one of your favorite building blocks in any LLM-powered automation.

In just a few minutes, this guide will teach you how to power your n8n automations with dynamic AI model routing—choose the best LLM for each task, optimize cost, and boost resilience without complex logic.

Let’s dive in.

So… What Is the n8n Model Selector Node?

The n8n Model Selector Node is one of the newer additions to n8n’s AI agent toolkit. It solves a key problem: when you’re working with multiple large language models (LLMs) like OpenAI, Anthropic, Mistral, Vertex AI, or Azure, how do you decide which one should handle a given input?

Traditionally, you’d have to build conditional logic with If/Switch nodes and manually wire your way through multiple branches. It gets messy.

But the Model Selector node simplifies all that.

It integrates seamlessly with the AI Agent node, allowing you to inject whichever LLM you choose as the Chat Model.

Why Is This Useful?

At first glance, the n8n Model Selector node might seem like a straightforward utility — but in practice, it plays an important role in making your AI workflows more adaptable, efficient, and scalable. Here’s what makes it so powerful:

1. You Can Optimize for Cost

Let’s say you’re running a support bot. For basic queries, Mistral (a faster, cheaper model) might be fine. But for more complex interactions, you might want GPT-4 or Claude 3. With the Model Selector, you can route requests accordingly and keep your OpenAI token bill under control.

2. You Can Improve Output Quality

Different models shine in different areas:

• GPT-4: General knowledge, structured output, step-by-step reasoning.
• Claude: Longer context, safer answers, great with documents.
• Mistral: Fast and affordable, great for classification or short form.
• Vertex/Gemini: Good for tight Google Cloud integration.
• Azure OpenAI: Enterprise-friendly, regionalized deployment.

With the selector, you’re no longer locked into one model per workflow — you can pick the right tool for the job.

3. You Can Run A/B or Multi-Model Evaluations

This one is big for anyone building production systems or researching LLMs. Want to compare GPT-4’s output to Claude on the same input? Just route both through the Model Selector, send the same prompt, and evaluate the results downstream. No need to copy-paste workflows or hardwire branches.

4. You Can Design for Resilience

Maybe OpenAI goes down. Maybe Anthropic is rate-limited. The selector gives you an elegant way to build fallback logic — if one model fails, choose another. That’s how you build AI systems that don’t break under pressure.

How It Works (Under the Hood)

Let’s break this down step by step.

1. Add the Model Selector Node to Your Workflow

In n8n, drag in the Model Selector node from the sidebar (or search for it). It looks like a dark purple block with several “model” ports underneath.

2. Connect Your Models

Now wire in your LLMs. These might be an OpenAI Chat Model, Mistral Cloud Chat Model, Google Vertex Chat Model, Anthropic Chat Model, or Azure OpenAI Chat Model. Each one gets plugged into a separate port on the selector. You can connect up to 10 different models, which should be more than enough for most use cases. Just set the number of inputs accordingly in the node settings.

3. Define Your Selection Logic

Open the Model Selector and set up your logic using one of the two supported modes. This is where the routing magic happens. You have two ways to control which model gets selected — depending on how you want to route tasks.

Option 1: Rule-Based Conditions (Descriptive & Human-Readable)

Use conditional rules to tell the Model Selector when to route to a specific model. This works great when your model routing logic is based on task types or descriptive values.

Example setup:

{{ $('Basic LLM Chain').item.json.text }} is equal to "OpenAI Chat Model"

This rule checks if the result of a previous node (e.g., an LLM output, or a system-generated tag) matches the string, and routes accordingly. You can add multiple conditions — one for each connected model. The first matching condition is used.

Option 2: Index-Based Selection (Compact & Expression-Friendly)

If you prefer to centralize your logic in one place, you can have a node (like an LLM Chain or a custom script) return a number, and route models based on that index.

Example logic:

const task = $json.chatInput?.toLowerCase();
if (task?.includes('summarize')) {
  return 2; // Google Gemini Chat Model
} else if (task?.includes('story') || task?.includes('text')) {
  return 1; // OpenAI Chat Model
} else {
  return 1; // Default to OpenAI Chat Model
}

In the Model Selector, switch to Expression mode and use:

{{ $('Basic LLM Chain').item.json.text.toNumber() }}

This will use the model at that index in the order they are connected.

4. Feed Output into the AI Agent

Once the selector decides which model to use, the output connects directly into the Chat Model input of the AI Agent node. From there, your conversation logic continues as usual — with the selected model now powering the interaction.

Go deeper into some practical strategies.

As mentioned earlier in this article, you can use the n8n Model Selector Node to optimize for cost, improve output quality, run A/B or multi-model evaluations, and design for resilience. Let’s dive into several practical strategies for using the Model Selector Node in n8n. These strategies help you dynamically choose the best large language model (LLM) for the task, balancing performance, cost, and user control.

Below, we break down each of these strategies in detail with code examples, real-world applications, and evaluation techniques.

1. Dynamic Task Routing

(Route based on the nature of the input or task type)

This strategy analyzes the prompt content and chooses a model that best matches the task type—whether it’s writing, coding, or summarizing.

Example:

Prompt: “Write a short blog post about AI trends in 2025”

Use Model Selector logic like:

const task = $json.taskType?.toLowerCase();
if (task.includes('summarize')) return 3;
if (task.includes('write')) return 1;
if (task.includes('code')) return 2;
return 1; // default to OpenAI

Index	Model	Task
1	OpenAI	Content writing
2	Claude	Code generation
3	Gemini	Summarization, analysis

You can collect these outputs, log them, or send them downstream to Slack or email for human review.

When to Use: When your users submit various types of prompts and you want the workflow to intelligently route each prompt to the best-suited model.

Purpose: Match the model’s strengths to the task and automate intelligent routing without hardcoding each condition externally.

Use Case Examples: Intelligent chat agents handling writing, coding, summarization. AI assistants that dynamically respond to varied tasks.

2. Cost Efficiency Routing

(Route based on estimated token count or user-tier)

This pattern ensures you don’t overpay for LLM usage by using lighter models for simple prompts and reserving heavy models for complex input.

Scenario: Estimate tokens using prompt length and route accordingly:

const input = $json.prompt || '';
const tokenCount = input.length / 4; // Approximation
if (tokenCount > 1000) return 1; // GPT-4
if (tokenCount > 300) return 2;  // Claude
return 3; // Gemini (fast & cheap)

When to Use: When optimizing for performance-to-cost ratio in production.

Purpose: Reduce unnecessary compute spend. Optimize usage depending on prompt size and user subscription tier.

Use Case Examples: SaaS apps with usage-based billing. Free vs. premium tiered model selection. Batch job orchestration.

3. External Workflow Routing (via Webhook)

(Route based on external API input or trigger system)

This strategy gives control of model selection to the frontend or calling service, making your AI infrastructure more modular.

Sample Code: Webhook URL format:

const url = `${baseURL}?message=${encodeURIComponent(message)}&route=${route}`;

In the Model Selector:

const route = $json.route?.toString();
return parseInt(route); // Route ID = Model Index

When to Use: When external tools, dashboards, or apps need to specify which model to use.

Purpose: Allow client-side or user-based control of LLM selection. Avoid rebuilding workflows for each integration.

Use Case Examples: A/B testing dashboards for LLMs. AI routing based on form parameters. End-user configuration of model preferences.

4. Model Evaluation & Comparison

(Run multiple models and evaluate output quality)

This strategy lets you use multiple LLMs simultaneously and compare the output using automated metrics.

When to Use: When testing new prompts or evaluating LLM quality for your use case.

Purpose: Automate evaluation workflows for LLM benchmarking. Run multiple responses in parallel.

Use Case Examples: Comparing OpenAI, Claude, and Gemini output side-by-side. Using similarity metrics to rank best outputs. Internal QA testing of AI content.

With these four strategies, the Model Selector Node becomes a powerful router, evaluator, and optimizer for your AI workflows. Whether you’re building scalable agents or a cost-controlled SaaS backend, these techniques give you the control and flexibility needed for robust LLM operations.

A Complete Example: Sheet-Driven Model Routing

Let’s say you’re reading a Google Sheet where each row looks like this:

task	modelPreference
summarize news article	mistral
analyze contract	anthropic
generate tweets	openai

Here’s what your flow might look like:

1. Trigger (Execute manually or via Webhook)
2. Google Sheets node (Get rows)
3. Set node → sets modelPreference field from each row
4. Model Selector node:

   const model = $json.modelPreference?.toLowerCase();
   if (model === 'openai') {
     return 1;
   } else if (model === 'mistral') {
     return 2;
   } else if (model === 'anthropic') {
     return 3;
   } else {
     return 1; // Default
   }

5. AI Agent node → Chat Model comes from selector
6. Output or structured response

Now your entire system adapts based on user configuration — no extra branches required.

Important Tip: Don’t overlook same-provider model switching

Though we often connect different models from different providers to the Model Selector (e.g., OpenAI, Anthropic, Google), you can also use it to route between different models from the same provider. For example, you might compare or switch between gpt-3.5-turbo, gpt-4.1, and gpt-4o — all from OpenAI — depending on task complexity, latency, or cost.

This is another powerful use of the Model Selector — especially since certain tasks may behave very differently depending on the model version. You might find that gpt-4o excels at fast reasoning, while gpt-4.1 gives more detailed breakdowns for complex instructions. In such cases, the ability to route by model version becomes crucial for performance tuning and reliability.

For a deeper dive into how OpenAI integrates with n8n beyond model selection, check our OpenAI automation guide .

Common Gotchas to Avoid

Even though the Model Selector is powerful, here are some tips to avoid headaches:

• Invalid Conditions: If your logic doesn’t return any value, the selector won’t choose a model. Always have a fallback return.
• Not Passing Output to AI Agent Correctly: The Model Selector output must be wired directly into the Chat Model input of the AI Agent node. Don’t connect it elsewhere.

Real Use Case: Multi-Model News Summarizer

Imagine you’re building a daily news digest workflow:

1. Pull top stories from Hacker News.
2. Based on story category (security, startup, open source), choose the model:
   • Use Claude for security content
   • GPT-4 for startups
   • Mistral for dev-related items
3. Summarize each article
4. Format responses and email or post to Slack

With the Model Selector, you can route each item dynamically — ensuring the best model handles the most appropriate content.

Final Thoughts

The n8n Model Selector Node is one of those game-changing additions to n8n that unlocks the full power of AI orchestration. Whether you’re building smart agents, content pipelines, evaluators, or scalable assistants, this node helps you move beyond single-model thinking and into flexible, intelligent automation.

It brings modularity. It brings maintainability. And it brings control back to you.

If you’re already building AI-powered workflows — or even thinking about it — this is the next piece you need in your toolkit.

So go ahead. Plug it in, route your models smartly, and see just how far your AI agents can go when they’re making decisions on the fly.