Make vs Celigo
Three of fourteen models named Make first on the direct prompt; two named Celigo. Make was named by fourteen of the fourteen models and Celigo by twelve and Make carries 55 labels and Celigo 18, so the shares are not directly comparable.
Named in seven categories this edition.
Named in three categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the workflow automation and ipaas page.
By framing
MakeFirst choices, of fourteen modelsCeligo
Across every category in the October 2026 Edition, Make and Celigo were named in the same answer twenty-two times, of the 172 answers naming Make and the 48 naming Celigo. In those answers Celigo took the first choice six times and Make two.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | |||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | | |
| Grok 4.20 | | | | | | |
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | ||||
| GPT-6 Luna | | | | | | |
| Muse Glimmer 30B | | | | |
Make Celigo first choice named as an alternative argued againstblank: not namedEach cell is one answer, Make on the left and Celigo on the right.The direct prompt
Make first, Celigo not the choice
3 of 14 modelsCeligo was named in the answer but not as the choice, or not at all.Celigo first, Make an alternative
2 of 14 modelsMake was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Make0%#14 of 14Celigo0%#9 of 14What the models said about Make
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“Make.com given reliability decline and support issues” Kimi K2 · negative prompt · hard negative
“Examples: Complex setups on Make (formerly Integromator) or Retool ... they encourage bad architecture if not strictly governed.” Qwen 3.7 Flash · negative prompt · soft negative
“platforms like Make or n8n can be powerful, but they become harder to maintain as flows grow more branched” Perplexity Sonar · negative prompt · soft negative
“For most budget-conscious companies, Make offers the best balance of affordability, capability, and ease of use” Kimi K2 · budget prompt · first choice
“For most mid-market B2B companies, Make offers the best balance of capability, visual ease-of-use, and cost.” Kimi K2 · direct prompt · first choice
“Best Overall for Non-Developers: Make.com ... Make is the industry-standard budget choice.” Gemini 3.5 Flash · budget prompt · first choice
What the models said about Celigo
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Celigo and Workato are top picks due to their balance of ease of use, scalability, and strong B2B-focused features” Mistral Small · paraphrase prompt · first choice
“→ Start with Celigo. It hits the sweet spot of capability, cost, and usability for mid-market B2B.” Kimi K2 · paraphrase prompt · first choice
“Celigo for most NetSuite‑centric or SaaS‑heavy stacks with a mix of IT and business users” GLM 4.7 FlashX · paraphrase prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.