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Published on 22.03.2026
TLDR: Google Opal lets you build AI workflows with plain English for free, n8n gives full control with a steep learning curve, and Make.com sits in the middle. The real insight: output quality is roughly equal across all three because it depends on your prompts, not the platform.
If you have ever found yourself pasting a newsletter into ChatGPT and asking it to generate a LinkedIn post, you already know the pain. You re-explain your context every time, copy-paste manually, and the output is rarely platform-specific enough to publish without heavy editing. That is the gap AI automation tools claim to fill, and a recent comparison between Google Opal, n8n, and Make.com reveals what each one actually delivers.
Google Opal is the newcomer and arguably the most accessible. You describe what you want in plain English, and the agent parses your prompt, picks the right AI models, and generates the full workflow. Every Opal workflow has three pieces: a user input, a generation step, and an output to Google Docs, Slides, or Sheets. The new Agent feature is the upgrade here. Previously you had to manually pick which AI model to use, but now the agent figures out the right combination on its own. Since Opal is built by Google, all the AI models are Google's, Gemini for text, Nano Banana Pro for images, Veo 3 for video, Lyria for music. You can chain them together, so generating a thumbnail from your newsletter becomes a multi-step automated process. The whole thing takes minutes to set up.
But here is the part nobody shows in tool demos. The first-pass results from Opal look like what you would get from any chatbot. The LinkedIn post reads like AI-generated content. The Substack Notes are not platform-specific. The Twitter thread is flat. The prototype is a thin wrapper around a single AI generation step with a basic prompt. To get publishable content, you need to split the workflow into separate nodes per platform, each with its own detailed prompt. Then add a hook generator layer that applies frameworks like "most powerful," "big number versus small number," and "call out" to create refined opening options. Then add an editor layer that strips out AI writing patterns like the "it's not X, it's Y" rhetoric. That production workflow takes more work, but it all lives inside Opal's visual builder.
Then there is n8n. The moment you see a real n8n setup, the gap is visible. Webhook triggers, content normalization, platform-specific prompt queues, quality gates that check brand voice standards, revision loops, and direct API publishing to LinkedIn, Twitter, and Substack. Everything Opal requires you to do manually, like copying from Google Docs and pasting into LinkedIn, n8n handles automatically. Opal gets you building fast but stops at Google Docs. n8n requires real learning, but once built, it handles the entire pipeline from newsletter to published social post without you touching it.
What makes n8n especially interesting now is the Claude Code integration. Through the MCP server, you describe what you want in plain English and Claude Code creates the working n8n workflow. Even people who published n8n courses say they no longer manually build workflows. They connect Claude Code and describe their intent. And n8n just shipped a feature called "n8n as code" that represents workflows as readable code, making that integration even tighter.
Make.com sits in the middle. It costs nine to twenty-four dollars a month, has a moderate learning curve, supports multi-AI models including OpenAI, Anthropic, and Gemini, and offers direct publishing to social platforms through webhooks and HTTP modules. The visual drag-and-drop builder is more complex than Opal but still approachable.
The final scoring tells the story clearly. Opal wins on cost and speed, five out of five on both, with a perfect learning curve score. But it scores only two out of five on flexibility and three out of five on output quality. n8n wins on flexibility and output, both five out of five, but drops to two out of five on learning curve. Make.com balances everything at three to five across the board. The output quality is roughly equal across all three because it ultimately depends on your prompts.
I Built a Newsletter Repurposing Workflow on Google Opal, n8n, and Make