Compare GPT-4, Claude & Gemini Responses with Contextual AI's LMUnit Evaluation
UnknownUnknown. This template uses community nodes we can't verify statically. Uses community nodes that can't be verified statically (unverified community packages are off by default in 3.0). Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 16 nodes and runs when a chat message arrives. Along the way it connects OpenAI, Google Gemini, Anthropic and Contextualai (community).
How it runs
Trigger: When a chat message arrives.
- OpenAI: uses OpenAI
- Gemini 2.5 Flash (Google Gemini): uses Google Gemini
- Claude 4.5 Sonnet (Anthropic): uses Anthropic
- Preprocess OpenAI Response (Edit Fields (Set)): sets 2 fields: provider, response
- Preprocess Gemini Response (Edit Fields (Set)): sets 2 fields: provider, response
- Preprocess Anthropic Response (Edit Fields (Set)): sets 2 fields: provider, response
- Combine responses (Merge): merges the data from its input branches
- Add unit tests to responses (Code): runs custom JavaScript code
- Iterate over each unit tests (Loop Over Items (Split in Batches)): loops over the items in batches
- Format Final Result (Code): runs custom JavaScript code
…plus 5 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
We can't fully verify this one: it relies on 1 community package, and n8n 3.0 turns unverified community packages off by default (N8N_UNVERIFIED_PACKAGES_ENABLED=false).
- Check that n8n-nodes-contextualai is verified or supports 3.0 (used in Run LMUnit).
Nodes used (11)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Contextual Ai
n8n-nodes-contextualai.contextualAi - Edit Fields (Set)
n8n-nodes-base.set - Merge
n8n-nodes-base.merge - Code
n8n-nodes-base.code - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Wait
n8n-nodes-base.wait - Chat
@n8n/n8n-nodes-langchain.chat - OpenAI
@n8n/n8n-nodes-langchain.openAi - Google Gemini
@n8n/n8n-nodes-langchain.googleGemini - Anthropic
@n8n/n8n-nodes-langchain.anthropic
From the author
PROBLEM Evaluating and comparing responses from multiple LLMs (OpenAI, Claude, Gemini) can be challenging when done manually. - Each model produces outputs that differ in clarity, tone, and reasoning structure. - Traditional evaluation metrics like ROUGE or…