n8n Agent Nodes: Assign Cheap LLM APIs by Task

You're already running n8n AI workflows. You've added LLM nodes for intent parsing, reasoning, summarization, and output formatting. And at some point you looked at the monthly API bill and realized every node is calling the same frontier model — whether it's doing complex multi-step reasoning or just classifying a sentence into one of three categories.

That's the problem this guide solves. You'll get a node-by-node model assignment framework for your n8n AI workflow that reduces inference spend by up to 40% and helps you save tokens at every step — powered by WisGate as the cheap LLM API behind the examples.

→ See every model available on WisGate — pricing 20%–50% below official rates

The Hidden Cost Problem in n8n AI Workflows

Most n8n AI workflow costs don't come from one expensive node. They come from using a frontier model at every node by default. A reasoning node that genuinely needs GPT-5 or Claude Opus gets the same model as the trigger node that's just extracting a timestamp and classifying input type. The lightweight node doesn't need frontier capability — but it's billed at frontier rates on every execution.

At 10,000 workflow runs per month, the cost of that misassignment compounds fast. The fix isn't cutting LLM usage — it's routing each node to a model tier that matches what the task actually requires.

A cheap LLM API isn't just about the per-token rate. It's about having access to the full model tier range — lightweight through frontier — under a single key, so you can route intelligently without managing multiple vendor credentials.

What Is a Cheap LLM API (For n8n Developers)

A cheap LLM API is an inference endpoint that provides access to multiple model tiers — including lightweight, mid-range, and frontier models — at rates below the official provider pricing, with OpenAI-compatible request formatting so it drops into existing n8n credentials without workflow rebuilds.

WisGate provides this as a unified gateway: one API key, one base URL (https://wisgate.ai/v1), covering text, image, and video models at pricing typically 20%–50% below official rates. For n8n developers, that means the model field in your HTTP Request node body is the only thing that changes between a $0.002/call classification step and a $0.014/call reasoning step.

How AI Model Routing Works in n8n

AI model routing in n8n means assigning a different model to each node in the workflow based on the cognitive complexity of the task at that node — not on convenience or default settings.

The routing logic is simple: the LLM call at each node should be the lowest-capability model that reliably handles that specific task. A model that can classify intent in 3 tokens doesn't need 8K context. A model that summarizes a document into a single sentence doesn't need chain-of-thought reasoning. Routing correctly means you only pay for frontier capability when the task genuinely requires it.

The Node Assignment Framework

Apply this four-tier routing map to your n8n AI workflow:

Trigger & Input Nodes

Trigger and input nodes in an n8n AI workflow parse incoming data: extract intent, classify category, identify entity type. The LLM call here is low-complexity — pattern recognition against a known schema, not open-ended generation. A lightweight model handles this reliably. Assigning a frontier model to a classification node is spending $0.014 per call on a task a $0.002 model covers equally well. WisGate's catalog includes several lightweight options — verify current availability and pricing at wisgate.ai/models.

Reasoning & Decision Nodes

Reasoning nodes handle the workflow's hard problems: ambiguous inputs with multiple valid interpretations, multi-hop logic that requires holding context across several steps, decisions that depend on conflicting signals. This is where frontier model capability earns its cost. Assign your highest-tier model here — GPT-5, Claude Opus, or equivalent. Keep max_tokens capped at 1,200 to prevent output bloat on verbose reasoning chains.

Tool Call & Action Nodes

Tool call nodes generate structured outputs: JSON payloads for API calls, SQL queries, function arguments. The task is constrained — the model needs to follow a schema, not reason freely. A mid-range model handles structured generation reliably at roughly half the cost of a frontier model. Use output format constraints ("respond only in valid JSON") to reduce retries and save tokens on malformed outputs.

Output & Response Nodes

Output nodes format the final user-facing response: email copy, chat reply, summary paragraph. Tone and fluency matter here, but deep reasoning doesn't. A mid-range model produces output quality that users can't distinguish from frontier on well-scoped generation tasks. Assign mid-range here and reserve frontier budget for the reasoning node where the decision actually happens.

WisGate Integration: Step-by-Step

Step 1 — Navigate to your n8n Credentials panel and create a new OpenAI API credential.

Step 2 — Set the base URL to https://wisgate.ai/v1 (not the default OpenAI URL).

Step 3 — Enter your WisGate API key from wisgate.ai/hall/tokens as the API key value.

Step 4 — Add an HTTP Request node for each workflow node that calls an LLM. Configure the request body per node tier:

// Lightweight node — intent classification
{
  "url": "https://wisgate.ai/v1/chat/completions",
  "headers": { "Authorization": "Bearer $WISGATE_KEY" },
  "body": {
    "model": "gpt-4.1-mini",
    "max_tokens": 150,
    "messages": [{ "role": "user", "content": "{{ $json.userInput }}" }]
  }
}
// Frontier node — multi-hop reasoning
{
  "url": "https://wisgate.ai/v1/chat/completions",
  "headers": { "Authorization": "Bearer $WISGATE_KEY" },
  "body": {
    "model": "gpt-5",
    "max_tokens": 1200,
    "messages": [
      { "role": "system", "content": "{{ $json.systemPrompt }}" },
      { "role": "user", "content": "{{ $json.enrichedContext }}" }
    ]
  }
}

Step 5 — Test each node individually with a representative input before running the full workflow.

Switching models without re-engineering: because WisGate is OpenAI-compatible, changing the model at any node requires editing only the "model" field value. No new credentials, no SDK changes, no workflow rebuilds. That's the operational leverage of a unified cheap LLM API.

How to Save Tokens at Every Node

• Context trimming before frontier nodes: run a mid-tier summarization call to compress retrieved documents before passing them to the reasoning node. Two cheap calls often cost less than one unoptimized frontier call.
• Cap max_tokens per tier: lightweight nodes at 150, tool call nodes at 400, output nodes at 600. Uncapped output nodes can 3–4× expected token cost on verbose responses.
• Compress system prompts: audit for redundancy. A 1,200-token system prompt at a classification node wastes more than the model price difference saves. Aim for under 200 tokens at lightweight nodes.
• Enforce output format: "respond only in valid JSON" reduces hallucination-driven retries and output bloat simultaneously.
• Short-circuit with IF nodes: for inputs matching deterministic patterns — structured forms, exact keyword matches — use an n8n IF node to skip the LLM call entirely. Zero tokens = maximum savings.

WisGate Pricing vs. Official: Real Numbers

*Verify live rates at wisgate.ai/models before finalizing cost projections. Rates may change.*Common Mistakes in n8n Model Assignment

• Frontier model on every node by default — the most expensive mistake. Fix: apply the node assignment framework before the first workflow run.
• Same system prompt length across all tiers — a 1,200-token prompt at a lightweight node costs more per call than the model price difference saves.
• No max_tokens cap at output nodes — without it, a single verbose response can 3–4× the expected cost.
• Rebuilding workflows to switch providers — unnecessary with an OpenAI-compatible cheap LLM API like WisGate. Only model and url change.
• No per-node cost monitoring — without visibility into which nodes consume the most budget, optimization is guesswork. Cross-reference WisGate's dashboard with n8n's execution history.

The node assignment framework only delivers its full savings when the underlying cheap LLM API is priced competitively. WisGate provides that foundation — pricing 20%–50% below official rates, a single OpenAI-compatible key, and model breadth across lightweight through frontier tiers to route intelligently at every node. Free n8n workflow templates to get started are available at juheapi.com/n8n-workflows.

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