Make vs Celigo
Three of twelve models named Make first on the direct prompt; one named Celigo. Make was named by twelve of the twelve models and Celigo by eleven and Make carries 48 labels and Celigo 18, so the shares are not directly comparable.
Named in four categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve modelsCeligo
Across every category in the September 2026 Edition, Make and Celigo were named in the same answer twenty-one times, of the 151 answers naming Make and the 34 naming Celigo. In those answers Celigo took the first choice five times and Make four.
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 4.1 Fast | | | | | | |
| Mistral Small | | | | | ||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | | |
| MiniMax M2.5 | | | |
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 12 modelsCeligo was named in the answer but not as the choice, or not at all.Celigo first, Make not the choice
1 of 12 modelsMake was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
5 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 12 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
Celigo4%#– of 11Make0%#11 of 11What the models said about Make
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“powerful and often better value, but it is still best suited to app-to-app automation; if you need strong governance ... it may not be the right fit” Perplexity Sonar · negative prompt · soft negative
“only for lightweight, low-volume, non-critical automations; they're not designed for complex, high-volume B2B data integration” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Strong value for mid-complexity workflows... Avoid for long-running processes or data-sensitive industries.” DeepSeek V4 Flash · negative prompt · soft negative
“Choose Make if you are cost-conscious, manage complex agency workflows, or need to process hundreds of records at once cheaply.” Qwen 3.7 Flash · comparative prompt · first choice
“Complex Logic on a Budget | Make | Allows loops, deep branching, and data arrays at a fraction of Zapier's cost.” Gemini 3.5 Flash · comparative prompt · first choice
“4. Make (Best Value) ... For most small businesses with limited budgets, Make or n8n offer the best balance” GLM 4.7 FlashX · 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.
“Best Overall Balance: Celigo ... strikes a perfect middle ground between ease of use and power.” Qwen 3.7 Flash · paraphrase prompt · first choice
“Choose Celigo if you want faster time-to-value and lower operational overhead” DeepSeek V4 Flash · direct prompt · first choice
“Celigo: Best Overall for ERP-Centric & B2B E-Commerce Operations” Gemini 3.5 Flash · 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.