Workflow orchestration · October 2026 Edition
Make vs Kestra
Six of fourteen models named Make first on the direct prompt; zero named Kestra. Make was named by twelve of the fourteen models and Kestra by eight and Make carries 27 labels and Kestra 19, so the shares are not directly comparable.
Named in five categories this edition.
Named in four categories this edition.
First-choice share12%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate26%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#7A position in a field of 14; printed, not drawn.
Labels2719A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Make reading right to left. Rank and label count are printed, not drawn.n8n was named alongside these two in eight of the fourteen direct answers. Prefect vs Make · Prefect vs Kestra · Apache Airflow vs Make
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 orchestration page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
MakeFirst choices, of fourteen modelsKestra
Direct601 against Make
Paraphrase00
Comparative00
Budget-constrained121 against Make
Scale-constrained00
Negative005 against Make · 2 against Kestra
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Make and Kestra stood in it.
| 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 | | |||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | | |
Make Kestra first choice named as an alternative argued againstblank: not namedEach cell is one answer, Make on the left and Kestra on the right.The direct prompt
The plain question, one answer per model, grouped by where Make and Kestra stood in it.
Make first, Kestra not the choice
6 of 14 modelsKestra was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Make, Zapier alternatives: Prefect, n8n
Gemini 3.5 FlashMake, Prefect alternatives: Astronomer Astro, Dagster, Workato, n8n
Grok 4.1 FastMake, Zapier alternatives: Apache Airflow, Microsoft Power Automate, Prefect, Tray.io, n8n
Qwen 3.7 FlashMake alternatives: Kissflow, Microsoft Power Automate, Native HubSpot Workflows, Nintex
Kimi K2Make alternatives: Tray.io, Workato, n8n
GLM 4.7 FlashXMake alternatives: Zapier, n8n
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Perplexity SonarWorkato alternatives: HubSpot workflows, Make, SnapLogic, Zapier, n8n
GPT-6 LunaWorkato alternatives: Make, Prefect, Temporal, n8n
Muse Glimmer 30BWorkato alternatives: Make, Microsoft Power Automate, ServiceNow Workflow, Zapier
Neither was named
5 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniPrefect alternatives: Apache Airflow, Dagster, Temporal
Mistral SmallHubSpot Marketing Hub alternatives: Kissflow, Microsoft Power Automate, Revo, Tray.io
DeepSeek V4 Flashn8n alternatives: Microsoft Power Automate, Zapier
Llama 4 MaverickKissflow alternatives: HubSpot Marketing Hub, Microsoft Power Automate
MiniMax M2.5Microsoft Power Automate, Tray.io alternatives: Zapier
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
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Mid-marketThe figures above
Enterprise
Level: the same share of first choices.
Make0%#– of 16Kestra0%#13 of 16What 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.
“Avoid No-Code (Zapier/Make) for data infrastructure, ETL, and backend logic.” Gemini 3.5 Flash · negative prompt · hard negative
“If your budget is bleeding from expensive tools like Zapier or Make” Gemini 3.5 Flash · budget prompt · hard negative
“Good visual builder at a budget price ($9/mo) — but workflows get messy at scale.” DeepSeek V4 Flash · direct prompt · soft negative
“Make is usually the best overall choice if you want a low-cost, user-friendly automation platform with a generous free tier” Perplexity Sonar · budget prompt · first choice
“Make typically offers the best balance of features, pricing, and ease of use for mid-market B2B companies” GLM 4.7 FlashX · direct prompt · first choice
“Make.com is the most cost-effective and powerful engine for a mid-market B2B budget” Gemini 3.5 Flash · direct prompt · first choice
What the models said about Kestra
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Kestra | Need mature ecosystem, extensive peer validation ... "younger ecosystem"” Kimi K2 · negative prompt · soft negative
“excellent alternatives to Airflow. However ... they carry a distinct business risk” Gemini 3.5 Flash · negative prompt · soft negative
“Start with Prefect or Kestra for most teams - they offer the best balance of free licensing, modern features, and reasonable complexity.” GLM 4.7 FlashX · budget prompt · first choice
“n8n or Kestra offer the best balance of capability, ease of use, and zero licensing costs” Kimi K2 · budget prompt · first choice
“Kestra defines workflows in YAML rather than Python, making pipelines easier to version-control” Claude Haiku 4.5 · paraphrase prompt · alternative
Also compared
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.