Test · Compare · Evaluate · Reuse
Build AI workers you can trust.
Test models against real tasks. Compare results. Reuse the winner.
Test AI models against real tasks. Compare quality, speed, and cost. Save the best setup as a versioned worker and reuse it across your workflows.
For AI automation builders and small agencies — not another prompt playground.
- 1Test a real task
- 2Compare models
- 3Evaluate with clear criteria
- 4Save a reusable worker
Start with one task.
Find the model that does it best. Save it. Reuse it.
Tell us what you want done. We'll show you which models do it best.
Same task. Different models. Different results.
Run one task across models and compare real outputs — then keep the winner.
Task
EXAMPLEIllustrative preview — run a live experiment to compare real results.
GLM-5.2
Z.ai · 262K context
Hook, proof, ask — a tight launch email you can send today.
Sample preview — not a live result
GLM-5.3
Z.ai · 1311K context
Customer problem first, short bullets, one owner and deadline.
Sample preview — not a live result
DeepSeek R1 Distill 32B
DeepSeek · 80K context
Subject + what shipped + why it matters + what to try next.
Sample preview — not a live result
From task to trusted worker.
A short loop you can repeat when instructions or models change.
Test
Start with a real task and representative examples.
Compare
Run the same inputs across candidate models.
Evaluate
Review outputs with explicit criteria and measured cost and latency.
Reuse
Save the winning setup as a versioned worker and retest on change.
Built for repeatable client work.
Three starting points with clear inputs, outputs, and quality checks.
Customer support triage
Input: incoming request. Output: category, urgency, key facts, draft reply. Check: correct category and urgency, valid structure.
Lead qualification
Input: lead details. Output: fit classification, evidence, missing info, next step. Check: criteria match, no invented details.
Business reporting
Input: supplied figures and notes. Output: summary, exceptions, supporting data. Check: supplied facts kept separate from interpretation.
Starter templates load working experiments with example data. Human review remains required before consequential actions or external messages.
Stop guessing. Test your next AI workflow.
Different models respond differently to the same task. Compare them on your inputs, find failure cases before deployment, and keep a reusable configuration you can retest when things change.
A worker is a tested setup, not a prompt.
Keep the instructions, model, settings, and test history together. Rerun the same examples after a change and see what moved.
Product Launch Writer
EXAMPLE- Job
- Writes concise product launch communications.
- Model
- Your winning model from the experiment.
- Instructions
- Your saved, tested prompt.
- Performance
- Based on your experiments and evaluations in History.
One workspace. Task to worker.
Stop guessing. Test your next AI workflow.
Start with one task. Compare models. Save the winner as a worker.