ToolJet vs Superblocks: published test evidence

Source: https://blog.tooljet.com/tooljet-vs-superblocks-ai-app-builders/

Extracted from the public article on 2026-10-17. Historical test results, not a new run.

Publisher and test operator: ToolJet. This is vendor-produced evidence, not an independent evaluation.

These are the displayed published prompts, including WordPress typography; this is not a raw submission-log export.

Current models, plans and prices may differ. No original logs, app exports or dataset snapshot are included. Screenshots and detailed outcomes remain in the source article.

Reviewer protocol: https://tooljet.com/evidence/ai-builder-tests-2026-10/reviewer-protocol.txt


PUBLISHED TEST METHOD


Both products got the same prompts, word for word apart from one stray word noted at the end, and we judged what each built without touching it by hand. These are the prompts from our Retool, Lovable and Emergent comparisons.

Expense tracker (one page, with a design brief): add expenses, filter by category and month, show the month’s total, in a specified look.

Property manager (eight pages): dashboard, properties, units, tenants, leases, rent, maintenance and vendors, with seeded data and working forms.

Two follow-up edits on the eight-page app: a Reports page with CSV export and costs on past jobs, then a navy theme, money without cents and a new column.

Save checks: add an expense; add a vendor and renew a lease; each confirmed after a reload.

Your own data: a three-page analytics dashboard on live Databricks tables.

Plans. Superblocks has no free plan, so we used a paid Teams plan and its AI builder, Clark, on default settings; Clark has no model picker, and Superblocks does not say which models it uses. ToolJet ran on GPT-6 Luna, its cheapest model, and GPT-6.1 Sol, a stronger one. The two platforms therefore ran different models, so these results compare the products as a whole and cannot separate the model from the platform.

Dollars. Superblocks meters AI in Governed Agent Units (GAUs): Teams includes 100 a month for $125 billed monthly, so we value a GAU at $1.25, the same as its top-up rate. ToolJet sells 100 credits for $1. Superblocks reports GAUs per Clark turn, so every Superblocks figure is exact. These dollar figures are the value of the AI usage each run consumed, not extra charges per run; the scaling section uses annual prices.

Rules. One run per prompt, no hand edits. When an agent stopped for approval, we approved and counted the stop; time excludes minutes spent waiting on us. When Clark asked how to store data, we chose browser localStorage. This is a same-prompt test, not a benchmark.

Who ran this. ToolJet ran this test. We report where Superblocks did better, and every prompt is at the end. Tested on 5 October 2026.


PUBLISHED RUN LEDGER

Columns separated by tabs; qualifiers and units are retained.

Platform	Build	Model	Time	AI units used	Cost

ToolJet	Expense tracker	GPT-6 Luna	4m 18s	2.9 credits	$0.03

ToolJet	Expense tracker	GPT-6.1 Sol	2m 25s	22 credits	$0.22

ToolJet	Property manager	GPT-6 Luna	17m 24s	21 credits	$0.21

ToolJet	Property manager	GPT-6.1 Sol	11m 11s	78.55 credits	$0.79

ToolJet	Edit 1	GPT-6 Luna	10m 26s	8.1 credits	$0.08

ToolJet	Edit 2	GPT-6 Luna	11m 13s	9.2 credits	$0.09

ToolJet	Edit 1	GPT-6.1 Sol	8m 1s	64.5 credits	$0.65

ToolJet	Edit 2	GPT-6.1 Sol	7m 25s	61.17 credits	$0.61

ToolJet	Databricks dashboard	GPT-6 Luna	12m 49s	10.75 credits	$0.11

ToolJet	Databricks dashboard	GPT-6.1 Sol	9m 13s	87.44 credits	$0.87

Superblocks Teams	Expense tracker	Clark	About 4 min of building	7.40 GAUs	$9.25

Superblocks Teams	Property manager	Clark	About 13 min	15.52 GAUs	$19.40

Superblocks Teams	Edit 1	Clark	About 5 min	12.57 GAUs	$15.71

Superblocks Teams	Edit 2	Clark	About 4 min	14.88 GAUs	$18.60

Superblocks Teams	Databricks dashboard	Clark	About 9.5 min	9.36 GAUs	$11.70


PUBLISHED PROMPTS AND EDITS

Read the setup and exception notes before repeating a run.

Every prompt below was sent word for word to both products. The Superblocks property-manager run reported here carried a stray word, “merg”, left in Superblocks’ prompt box from an earlier draft, at the end of the prompt.

Expense tracker:

Build an expense tracker. Let me add an expense with date, category (travel, meals, software, office, other), amount and a note. Show all expenses in a table I can filter by category and month, with the month's total at the top.
Design: a clean, modern light theme on a near-white background; a header with the app name and a short subtitle; the month's total as a large figure in a white card, with the number of expenses beside it; the add-expense form in its own card with the fields side by side and one primary button; category shown as coloured tags (travel blue, meals amber, software violet, office teal, other grey); indigo (#4F46E5) as the primary colour; money as $1,234.56 and dates as Mar 4, 2026.

Property manager:

Build a property management app for a small residential landlord, with eight pages and a left sidebar to move between them.
Seed realistic sample data: 4 properties, 32 units, 26 tenants, a lease for each occupied unit, 6 months of rent payments, 20 maintenance requests and 8 vendors.
1. Dashboard: KPI cards for occupancy %, rent collected this month vs expected, total overdue balance and open maintenance requests; a 6-month rent collection chart; occupancy by property as a bar chart; and a list of leases ending in the next 60 days.
2. Properties: a card grid showing each property's name, address, unit count and occupancy %; clicking a card opens a panel listing its units.
3. Units: a table with property, unit number, beds, baths, square feet, monthly rent and status (occupied, vacant, notice given); filters for property and status; rent is editable inline.
4. Tenants: a searchable directory; selecting a tenant shows their contact details, current lease, payment history and open maintenance requests.
5. Leases: a list with tenant, unit, start and end dates, rent, deposit and status (active, expiring, ended); a form to create a lease for a vacant unit, which marks the unit occupied; and a Renew action that extends the end date by 12 months.
6. Rent: a ledger of payments by month; a form to record a payment against a lease; and an overdue list showing days late, with a 5% late fee added once a payment is more than 5 days late.
7. Maintenance: requests grouped by status (new, scheduled, in progress, done) with priority (low, medium, high, urgent); a form to log a request for a unit; and the ability to assign a vendor and change status.
8. Vendors: a list with trade, phone, email and rating (1 to 5); the number of jobs assigned to each; and a form to add a vendor.
Design: a clean, modern light theme on a near-white background, white cards with soft borders, emerald (#059669) as the primary colour for the main buttons and active navigation, status tags in consistent colours (green for good states, amber for warnings, red for overdue or urgent, grey for neutral), money shown as $1,234 and dates as Mar 4, 2026.

Edit 1: “Add a ninth page, Reports, to the sidebar: a chart of monthly rent income against maintenance spend for each property over the last 6 months, a table with the same numbers per property and month, and a button to export that table as CSV. Track a cost on each maintenance request so the spend is real data, and fill costs in for the existing completed requests.”

Edit 2: “Change the primary colour from emerald to navy (#1E3A8A) everywhere in the app, show all money without cents (for example $1,234), and add a ‘Days to lease end’ column to the Leases list, sorted with the soonest first.”

Databricks dashboard (sent after connecting the workspace’s sample tables on each platform):

Build a sales analytics dashboard for Bakehouse, a bakery franchise chain, on my Databricks data. Use my connected Databricks data source. The tables are samples.bakehouse.sales_transactions, samples.bakehouse.sales_franchises and samples.bakehouse.sales_customers; read them to learn their columns. The data is read-only: query Databricks live, and do not create, copy or change any tables, and do not use sample or seeded data.
Three pages with a left sidebar:
1. Overview: a date range filter (defaulting to the full range in the data), a franchise country filter and a payment method filter that drive everything on the page. KPI cards for total revenue, transactions, units sold, average order value and active franchises, each with the change against the previous period of the same length. Charts: revenue by day as a line chart with a 7-day moving average; revenue by product as a horizontal bar chart showing each product's share; revenue by hour of day; revenue by day of week; payment method mix as a donut chart; and the top 10 franchises by revenue as a bar chart.
2. Franchises: a sortable table of every franchise with name, city, country, size, revenue, transactions, average order value and revenue rank, with country and size filters; selecting a franchise shows its revenue by day, its product mix and its busiest hours. Add a scatter chart of franchises by transactions against average order value.
3. Customers: KPI cards for unique customers, revenue per customer and repeat customers (more than one transaction); revenue by customer country; customers by gender; and a table of the top 25 customers by spend with name, country, transactions, total spend and favourite product.
Design: a clean, modern light theme on a near-white background; a header on each page with the page title and a one-line description; KPI cards in a row of white cards with soft borders, the value large and the change as a green or red tag; charts in white cards with a title and a short subtitle, in a two-column grid; amber (#D97706) as the primary colour for buttons, active navigation and the main chart series, with warm neutral colours for other series; money as $1,234 on KPIs and charts and $1,234.56 in tables, and dates as Mar 4, 2026.


RERUN LIMITATIONS

Use disposable test data. A successful save check is not a security, accessibility, scale or production-readiness certification.

Do not generalize a single run into guaranteed cost, speed or quality. Same-family models are not identical-model controls.

Record subscription and infrastructure charges separately from the dollar-equivalent AI usage. Include coding-agent charges when comparing MCP/CLI routes.

Raw data and export gaps should be disclosed rather than filled with estimated observations.

The published Superblocks property-manager run had the extra word "merg" at the end. Appending it repeats that reported variant; omitting it is a clean-prompt variant and must be labelled separately.

Clark model identity was not disclosed. These runs cannot isolate the model from the platform. The reported storage choice was browser localStorage.