Lindy vs n8n (2026): What ChatGPT, Claude & Gemini Say

AI models name Lindy a little more often than n8n. Lindy appears in 33 of 50 recorded answers and is named first in 9. n8n appears in 29 and is named first in 8. The gap is four answers and one first mention, so the totals are close. The model-by-model split is where the two pull apart.

The counts cover what AI answers name when a startup asks how to build AI agents without a big engineering team. They do not rate either product.

TL;DR

• Lindy is the more-named product on every measure: more answers, more first mentions and an earlier average position.
• The lean follows the model family. Anthropic and Perplexity models name Lindy more. OpenAI and Google models name n8n more.
• Sonar Reasoning Pro gives the sharpest split. It names Lindy in all 5 of its answers and n8n in none.
• ChatGPT barely names either product. It names n8n once and Lindy never.
• The captured guides put the two on different jobs. Lindy suits people who want to delegate work conversationally, and n8n suits technical teams who care about cost at volume and control.
• The billing units differ. Lindy bills per person plus credits, and n8n bills per workflow run or runs free when self-hosted.

How often do AI models recommend Lindy and n8n?

Lindy is named in more answers, named first more often and placed earlier in the answers. The margin is small on all three.

Answer share is the share of the 50 recorded answers that named the product. Named first is the share of answers where it appeared before any other tracked product. Average position is its place in the order of tracked products an answer names, so a lower number means earlier.

Lindy leads all 20 AI agent builders the models named, so the category leader is already in the table. n8n is second. Zapier matches n8n’s 29 named answers, but n8n is named first more often, 8 times against 6.

Four answers out of 50 is a narrow lead. Both products share a bigger pattern: each is named far more often than it opens an answer. Lindy’s named share runs 48 points above its first share, and n8n’s runs 42 points above.

Which models prefer Lindy, and which prefer n8n?

Three models name Lindy more, three name n8n more and four name them equally. The split follows model families, and one model holds the widest gap.

Lindy’s lead comes from the Perplexity and Anthropic models. Sonar Reasoning Pro names Lindy in all 5 answers and n8n in none, and no other model splits the pair that far. Perplexity and Claude each name Lindy once more than n8n. At family level, Perplexity models give Lindy (90% of their answers) its strongest showing. Anthropic models name Lindy (86.7% of their answers) just ahead of n8n (80%). Lindy is the single most-named product for six of the ten models: Claude Opus 5, Claude, Claude Fable 5, Gemini, Perplexity and Sonar Reasoning Pro.

n8n’s lead comes from the OpenAI models and Gemini 3.5 Flash. GPT-5.6 Sol is the only model whose most-named product is n8n (60% of its answers). Sol names Lindy twice. ChatGPT names n8n once and never names Lindy. Its own most-named product is Zapier (80% of its answers). Gemini 3.5 Flash names n8n three times and Lindy twice, and it also names Zapier most. At family level, OpenAI models name n8n (46.7% of their answers) ahead of Lindy (33.3%). Google models name n8n (70%) ahead of Lindy (60%).

Four models call it level. Claude Opus 5 names both in every one of its answers. GPT-5.6 Luna, Claude Fable 5 and Gemini name them equally often. Luna names Voiceflow most (80% of its answers).

A buyer asking the same question gets a different pair depending on the assistant. On ChatGPT, neither product is likely to come up. On Sonar Reasoning Pro, Lindy came up in every recorded answer and n8n in none.

Lindy’s own website also feeds the answers. The models cited lindy.ai 23 times across these responses, which puts it among the five vendor-owned sites they cite most. n8n’s own domain is not on that list.

What do the answers say about each?

The recorded answers describe Lindy as the no-build option for non-technical teams and n8n as a workflow layer that trades setup work for control and cost. Five short extracts, each verbatim:

“Lindy is my top recommendation for a small team that wants useful business agents without hiring engineers.”

GPT-5.6 Sol, asked to compare the top options.

“n8n Cloud is the best general-purpose choice”

GPT-5.6 Sol again, asked for the best platform for a startup. The same model puts a different product on top when the wording of the question changes, and the panel asks every model five questions to catch swings like this one.

“best if you want to launch business agents quickly with minimal setup”

Perplexity, describing Lindy.

“popular with startups because you can self-host cheaply and it has execution-based pricing rather than per-seat”

Claude Opus 5, describing n8n.

“Lindy’s with Lindy”

Claude Opus 5 again. It warned that the vendor lists it found each rank their own product first, and it named Lindy’s list among them.

How do Lindy and n8n differ?

Lindy is billed per person plus credits, and n8n is billed per run or is free to self-host. The panel’s vendor records hold no pricing for either product.

Pricing model

Lindy’s plans run from $29.99 to $199.99 per user a month plus pooled credits. Plan credits do not roll over, and Lindy pauses when the credit pool runs low. Model use is included in the credits, so Lindy sends no separate token bill. The cost of a request is known only after it has run. Lindy’s own comparison post from December 2025 lists a free plan and a $49.99 monthly starting price. Macha’s September 2026 check reports that those terms belong to an older Lindy product.

n8n counts one run of a whole workflow as one execution, however many steps it has. Starter costs €24 a month (€20 billed yearly) for 2,500 executions. The self-hosted Community edition is free under the Sustainable Use License for internal business or personal use. The AI models an n8n workflow calls are a second bill. The software is source-available rather than open source.

Who each is for

Carly’s comparison places Lindy with founders and executives delegating inbox and calendar work. Macha describes the Lindy builder as anyone who can type a request, with admins setting up team routines.

Carly’s comparison places n8n with ops and engineering teams running high-volume automation. Macha describes the n8n builder as a technical person who designs workflows node by node.

Hosting and compliance

Lindy runs in Lindy’s cloud only. Lindy’s own comparison page states that Lindy meets SOC 2 and HIPAA standards.

n8n runs on n8n Cloud or on your own servers. The same Lindy-authored page states that n8n meets SOC 2 and that self-hosters are responsible for their own compliance.

What the models name each for

In the recorded answers, Lindy is named for speed and for teams where nobody wants to build workflows. n8n is named for integrations, self-hosting, execution pricing and control. Several answers add that n8n is low-code and needs someone comfortable with APIs.

When should you pick Lindy?

Pick Lindy when nobody on the team will build or maintain workflows and the work arrives as requests.

• Carly’s quick-reference table sends teams where nobody is technical to Lindy.
• Lindy’s credits cover model use, so there is no token bill to manage.
• A compliance checklist that names HIPAA points to Lindy’s own statement of SOC 2 and HIPAA.
• Buyers who research with Claude or Perplexity models meet Lindy more often. It leads the Anthropic and Perplexity families and is named in 33 of 50 answers overall.

Budget for the meter. Lindy’s cost per request is known only after the request runs.

When should you pick n8n?

Pick n8n when a technical person will own the workflows, or when volume, self-hosting or fixed rules matter.

• n8n expects a builder who designs workflows node by node.
• High run volume favours its billing unit, since a 14-step agent costs the same single execution as a 2-step one.
• Self-hosting keeps the data on your own servers.
• A builder can put fixed rules in logic the model cannot talk its way around.
• Buyers who research with OpenAI or Gemini models meet n8n more often. It leads both families and is GPT-5.6 Sol’s most-named product.

Budget for the build. n8n’s own users say the learning curve is real.

How this sits against the Lindy vs n8n guides

The captured comparison pages compare features and prices, and each one is written by a company with its own product in the space. Page by page:

Lindy’s “Lindy vs n8n vs Relevance AI” is written by Jack Jundanian, GM of New Verticals at Lindy. It was last updated on 6 December 2025. It compares automation depth, integrations, ease of use and pricing. Its section verdicts favour Lindy. It frames n8n as a visual builder with many integrations for teams that do not mind adding AI manually.

Lindy’s “n8n vs Zapier: An Outsider’s Comparison” is written by Michelle Liu, a Senior Product Manager at Lindy. Its table lists n8n for developers who want full control and self-hosting, and Lindy for businesses that want to automate with AI and avoid coding. It gives n8n the customisation category.

Carly’s “Lindy vs n8n: AI Assistant or Workflow Platform?” is dated 6 July 2026. It argues that the real difference is the billing unit, credits against executions. It disputes the claim that n8n has no real AI. It then promotes Carly, its publisher’s own AI executive assistant, as a third option.

Macha’s “Lindy AI vs n8n (2026)” was published on 25 September 2026. It prices a 1,000-ticket support month on both tools from published rates and states that it did not build an agent on either. It flags that Lindy’s own comparison still quotes an old plan and a free tier. Macha sells an AI agent layer for help desks and closes by pitching it.

Those pages compare features. This page counts what AI answers name. Memetik’s panel adds three things none of the guides report: how often ten AI models name each product, which models lean which way, and the exact words those models use.

How the sample was built

10 models x 5 fixed prompts = 50 recorded answers. The edition is 2026-09, recorded on 2 September 2026. Each model answered each question once through its API.

The five questions, verbatim:

1. What is the best platform to build AI agents without a big engineering team for a startup? Name specific products.
2. Which platform to build AI agents without a big engineering team would you recommend to a startup in 2026?
3. Compare the top platform to build AI agents without a big engineering team options right now.
4. I’m a startup and I need a platform to build AI agents without a big engineering team. What should I use and why?
5. Best platform to build AI agents without a big engineering team for customer support agents?

The models, by family:

• OpenAI, 15 answers: GPT-5.6 Sol, ChatGPT (GPT-5.6 Terra) and GPT-5.6 Luna.
• Anthropic, 15 answers: Claude Opus 5, Claude (Claude Sonnet 5) and Claude Fable 5.
• Google, 10 answers: Gemini (Gemini 3.6 Flash) and Gemini 3.5 Flash.
• Perplexity, 10 answers: Perplexity (Sonar Pro) and Sonar Reasoning Pro.

The full category record, with every answer and the cited sources, is at AI agent builders. How the panel runs and what it counts is on the method page.

What these counts cannot tell you

These counts measure presence in AI answers. They say nothing about product quality, fit with your stack, uptime, support or pricing fairness. Being named differs from being recommended, because an answer can name a product only to warn against it.

Each model answered each question once, so every per-model figure is out of 5 and a one-answer difference is thin evidence. This is one dated snapshot from the 2026-09 edition, and answers change between editions. The answers came through model APIs with English prompts, and a consumer chat app with memory or location may answer differently. Products are matched by name, so an alias the matcher misses goes uncounted. The five questions ask about building agents without a big engineering team. A question about self-hosting or support tickets could split the pair differently.

Frequently asked questions

Is there a better alternative to n8n?

The panel counts names and does not rate quality. Lindy is named in 33 of 50 agent-builder answers. n8n and Zapier are named in 29 each. Macha’s comparison says that if n8n’s learning curve is the problem, Lindy or Zapier’s agents need less building.

Is Lindy AI any good?

This panel does not measure quality. It records that Lindy is the most-named AI agent builder in its category. Public reviews captured by Macha on 19 September 2026 are split, with G2 at 4.9 from 171 reviews and Trustpilot at 1.7 from 39.

What is the best agentic AI currently?

The models do not agree. Across the five agent-builder questions, the most-named product was n8n for one model, Zapier for two, Voiceflow for one and Lindy for six. Lindy is the most-named product across all 50 answers, which is a count of names and carries no judgement of quality.

What does Lindy AI cost?

Lindy’s plans run from $29.99 to $199.99 per user a month plus pooled credits, according to Macha’s September 2026 check of Lindy’s pricing page. Teammates who join through Slack get a 7-day free trial, and direct signups are billed straight away. Lindy’s own December 2025 comparison still shows a free plan and a $49.99 starting price.

Does ChatGPT recommend Lindy or n8n?

Rarely either. ChatGPT (GPT-5.6 Terra) names n8n in 1 of its 5 answers and never names Lindy. It names Zapier in 4. The other OpenAI models name both more often. GPT-5.6 Sol names n8n in 3 answers and Lindy in 2, and GPT-5.6 Luna names each in 3.

Do Claude and Gemini lean to Lindy or n8n?

Claude models lean slightly to Lindy. Anthropic models name Lindy (86.7% of their answers) against n8n (80%), and Claude Opus 5 names both in all 5 answers. Gemini models lean slightly to n8n. Gemini names each in 4 answers, and Gemini 3.5 Flash names n8n in 3 and Lindy in 2.