Searches for the fastest AI coding CLI often compare one response time or a vendor demo. Real repository work includes context gathering, tool calls, test runs, retries, and human review. If you want to know which setup feels fastest for your work, measure the time from a clean starting point to a verified patch and record what happened along the way.
Define what speed means for your task
Time to first response, time to first edit, and time to a passing test measure different parts of the experience. A tool can begin writing quickly and still take longer to repair a wrong assumption. For a team, time to an accepted diff may matter more than the first token because a developer still has to understand and review the patch.
Pick the endpoint before the run. For example, stop the clock when the stated acceptance check passes and a reviewer can understand the change. Record human review separately so you can distinguish agent speed from the time needed to trust the result.
| Measure | What it tells you |
|---|---|
| Time to first response | How quickly the model begins replying |
| Time to first edit | How long context gathering and planning take |
| Time to passing check | Whether the agent can reach a tested result |
| Time to accepted diff | How much agent and human effort the task required |
Keep the comparison fair
Use the same repository commit, prompt, task, model tier where possible, machine, network, and test command. If the products require different account paths or model settings, record those differences rather than hiding them. Separate cold and warm cache runs because reusable context can change latency as well as cost.
Repeat each task. A single run can be affected by network conditions, background work, model routing, or an unusually short answer. Report the median and the range, and retain failed attempts. Do not discard the slow run simply because it does not fit the story.
- Choose one bug fix, one test task, and one contained feature.
- Start every run from a clean copy of the same commit.
- Use the same acceptance checks and define the stopping point.
- Record retries, tool time, test time, review time, and failures.
- Repeat each run and publish the settings with the results.
Look for why a run was fast or slow
A slower run may have read more files, waited for a test suite, used a more capable model, or spent time correcting a bad first attempt. A faster run may have skipped checks or produced an incomplete patch. Record the reason when you can rather than ranking tools by the final duration alone.
Keep quality measures beside speed. Track whether the acceptance test passed, the size and scope of the diff, the amount of human correction, and whether the result was kept. A fast answer that breaks behavior is not a fast coding workflow.
Publish a result readers can reproduce
Include the CLI and model versions, repository commit, task prompt, environment, plan or API path, cache state, timing definitions, number of runs, and test results. State whether the measurements include setup, compilation, and review. If a tool does not expose model latency or cache fields, say so instead of estimating them from a spinner.
The broader benchmark guide includes a worksheet for comparing cost, quality, usage, and review effort. Use that method when you want a result that helps another developer choose a tool instead of a headline that only sounds fast.