Choose a coding assistant by the changes you can understand, verify and maintain in your own project. Keep a small task, a clean starting point and explicit checks. Then compare the complete effort needed to finish it, including your review.
Correction — September 24, 2026: an earlier version presented Copilot mainly as autocomplete and asserted that Cursor was better at project work. That comparison was incomplete and unsupported by a controlled test. We have replaced it with a task-based evaluation lesson.
What the documentation establishes
Cursor’s Agent documentation describes codebase exploration, edits across files and terminal commands. GitHub’s feature documentation also describes agent mode that selects files, makes edits and iterates, alongside a cloud agent for repository tasks. Copilot’s capabilities extend beyond inline suggestions.
Those documents establish that both products offer broader assistance. They do not establish which will perform better on your repository. Access, supported environments and account settings still need checking before your own trial.
Start with a requirement you can check
Our fictional teaching example is a small ordering function. This code was written for this lesson; it is not output observed from Cursor or Copilot.
function total(quantity, unitPrice) {
return quantity * unitPrice;
}The task is: “Accept a quantity only when it is a positive, safe integer supplied as a JavaScript number. Reject numeric strings and invalid quantities with a clear error. Keep the existing multiplication for accepted quantities. Assume unitPrice is already validated upstream.”
This boundary matters. Without it, a tool might silently convert strings, allow fractions or expand the task into unrelated price handling. Save the requirement before looking at any generated patch.
Inspect a plausible but incomplete patch
An intentionally flawed teaching patch adds this condition:
if (quantity <= 0) throw new Error('Invalid quantity');It catches zero and negative numbers. It still accepts a fractional quantity such as 1.5 and the numeric string “2”. NaN also passes that condition because its comparison with zero is false. A successful build would not prove that the requirement had been met.
A patch that implements the stated quantity requirement is:
function total(quantity, unitPrice) {
if (!Number.isSafeInteger(quantity) || quantity <= 0) {
throw new Error('Quantity must be a positive safe integer');
}
return quantity * unitPrice;
}The lesson is the connection between requirement, implementation and evidence. This function does not validate unitPrice, impose stock limits or implement a production payment system. Those would be separate requirements.
Use a small acceptance table
With unitPrice set to 12, use these inputs:
| Quantity input | Expected outcome | Reason |
|---|---|---|
| 1 | 12 | Positive safe integer |
| 3 | 36 | Existing calculation is preserved |
| 0 | Error | Zero is excluded |
| -1 | Error | Negative quantities are excluded |
| 1.5 | Error | Fractional quantities are excluded |
"2" | Error | The input must be a number |
| NaN or Infinity | Error | Neither is a safe integer |
| 9007199254740992 | Error | Outside the safe integer range |
If a proposed patch fails one of these rows, give the assistant the exact failed input and expected behavior. Ask for a bounded correction. Read the new diff to confirm that the fix did not weaken another condition.
Compare the whole task, including your review
For a real comparison, start each product from the same repository revision. Provide the same requirement and acceptance cases. Record the date, model or mode, account conditions and any extra instructions you supplied. Keep generated patches and actual test output.
Use a short record for each attempt:
- Did the first patch satisfy every acceptance case?
- Which unrelated files or dependencies changed?
- What did you have to explain again?
- How much active time did you spend reviewing and correcting it?
- Could another maintainer understand the final diff?
Separate active work from waiting time. Count failed attempts as well as the final success. One exercise can reveal a mismatch with your task, but it cannot establish a universal product ranking. Try a second, materially different task before making a broader choice.
Your next exercise
Add a maximum allowed quantity of 100 to the requirement. Write the expected outcomes for 99, 100 and 101 before changing the code. The first two should remain valid; 101 should throw an error. Then ask your assistant for the smallest patch and rerun the earlier cases.
For non-code answers, use the same habit: define what must be true, inspect the output and check the evidence. Our guide to verifying an AI answer applies this approach to factual claims.