Compare different LLM responses side-by-side with Google Sheets

This workflow allows you to easily evaluate and compare the outputs of two language models (LLMs) before choosing one for production. In the chat interface, both model outputs are shown side by side. Their responses are also logged into a Google Sheet, where they can be evaluated manually or automatically using a more advanced model. Use Case You're developing an AI agent, and since LLMs are non-deterministic, you want to determine which one performs best for your specific use case. This template is designed to help you compare them effectively. How It Works - The user sends a message to the chat interface. - The input is duplicated and sent to two different LLMs. - Each model processes the same prompt independently, using its own memory context. - Their answers, along with the user input and previous context, are logged to Google Sheets. - You can review, compare, and evaluate the model outputs manually (or automate it later). - In the chat, both responses are also shown one after the other for direct comparison. How To Use It - Copy this Google Sheets template (File > Make a Copy). - Set up your System Prompt and Tools in the AI Agent node to suit your use case. - Start chatting! Each message will trigger both models and log their responses to the spreadsheet. Note: This version is set up for two models. If you want to compare more, you’ll need to extend the workflow logic and update the sheet. About Models You can use OpenRouter or Vertex AI to test models across providers. If you're using a node for a specific provider, like OpenAI, you can compare different models from that provider (e.g., gpt-4.1 vs gpt-4.1-mini). Evaluation in Google Sheets This is ideal for teams, allowing non-technical stakeholders (not just data scientists) to evaluate responses based on real-world needs. Advanced users can automate this evaluation using a more capable model (like o3 from OpenAI), but note that this will increase token usage and cost. Token Considerations Since each input is processed by two different models, the workflow will consume more tokens overall. Keep an eye on usage, especially if working with longer prompts or running multiple evaluations, as this can impact cost.