Copilot, Cursor and other AI tools: how to measure value for a development team

There is no one-size-fits-all answer to which tool gives the biggest speed boost: studies show both significant speedups and slowdowns depending on the task, developer experience, and familiarity with the code. Therefore, the choice between GitHub Copilot, Cursor, Claude Code or other tools should be made not by other people's percentages, but by a short team experiment on your tasks, with the measurement of the time to the accepted change, defects after the merger and costs.

It is also important not to mix levels. Copilot and Cursor are products with their own interface, settings and workflow. Claude is Anthropic's family of language models, and Claude Code is a stand-alone code tool. A model can run inside different products, and the same product can use different models.

After the article, you will be able to stratify the choice, draw up a pilot protocol and decide whether you need one tool for everyone or different ones for different roles.

What exactly is compared: IDE, assistant, model and enterprise settings

The question "which is better - Copilot, Cursor or Claude" compares things at different levels. It is useful to spread it into four layers.

Development environment. Where the developer writes code: VS Code, JetBrains, Cursor as a separate editor, terminal. Changing the environment is a separate solution with its own cost that should be thought about independently of AI.

Assistant or agent. What the tool can do: complete lines, reply in chat, edit multiple files, run commands and tests, prepare changes for review. It depends on what tasks can be entrusted to him.

Model. Which language model generates the response. Here it is important to remember that the models are updated several times a year. Comparing "with a model that was relevant a year ago" says little about today's choice; commit a specific version of the model to the date of the pilot.

Corporate Settings. Who administers access, what data is shared with vendors, whether it is used for training, how costs are controlled, whether certain models or features can be banned.

When the team says "we tried tool X and it didn't help," it's worth clarifying at which layer the problem occurred: the model doesn't understand your stack well, the agent doesn't have access to the right commands, the environment is uncomfortable, or security settings cut off the context.

Why are the research numbers so different?

Two of the most frequently cited studies provide seemingly opposite conclusions. In fact, they measure different things.

Experiment with Copilot (2023). The developers were given one clearly formulated task: to implement an HTTP server on JavaScript as soon as possible. The group with access to GitHub Copilot completed the task 55.8% faster than the control group. This is a result for a new, limited and well-defined task, not an estimate of the team's annual performance on a complex product.

Randomized Study (2025). 16 experienced developers of large open source projects—with an average of over 22K stars on GitHub and over a million lines of code—performed 246 real-world tasks on repositories they had worked with for years. The tasks were randomly divided into those where AI is allowed and those where it is not. Participants used mostly Cursor Pro with Claude 3.5/3.7 Sonnet models. With access to AI, tasks took 19% more time. That being said, before the start the developers expected a 24% speedup, and after finishing they still believed that the AI ​​​​speeded them up by about 20%.

Why these two numbers cannot be averaged:

• challenge type: new small program against changes in large familiar code with strict quality standards;
• experience and familiarity: in the second study, the developers knew their projects deeply, so the AI ​​had less to help;
• readiness criterion: in large projects, the author must prepare a change that will meet the standards and pass the review. The study measured the time until an author was ready to submit a change for review, not the review time after submission.
• generation of tools: both studies refer to tools of their time.

A later update of this research project separately highlighted the limitations of such measurements. In February 2026, the authors published an update of their results: using raw data from a new round with end-2025 instruments, the estimate shifted toward a shorter time, but the confidence intervals include zero. The authors directly call this data an unreliable signal: some developers refused to participate because they did not want to work without AI; in surveys, 30–50% of participants reported that they did not submit some tasks because they did not want to do them without AI; time per task is difficult to measure when a developer is running multiple agents in parallel. Therefore, none of these numbers are the "true acceleration percentage" for your team.

Another conclusion of the 2025 study is useful for any pilot: the subjective feeling of acceleration can differ significantly from the measured time. The "did you get faster" survey is not a substitute for measurement.

Team work scenarios

The usefulness of AI tools strongly depends on the type of work. It is useful to evaluate them separately according to scenarios.

A new module or service from scratch. A clear specification, little existing code, standard libraries — conditions close to experimenting with an HTTP server. Here, the generation of the framework and typical parts often saves time, but requires careful verification of architectural solutions.

Tests. Generating tests for existing code is a frequent scenario. It's worth checking that the tests are actually testing the behavior and not just repeating the current implementation, including its bugs.

Searching and understanding the code. "Where is the payment processed", "why does this call return null". For new team members and unfamiliar parts of the system, this can be a noticeable help, but less noticeable for the module author.

Debugging. Benefit depends on whether the tool has access to logs, stack, ability to run code. Without it, the assistant suggests hypotheses that the developer checks manually.

Review. Preliminary review of changes, finding obvious problems, explaining the big diff. Useful as a supplement to a review by a person, but not as a replacement.

Routine changes to many files. Renaming, API migration, dependency updates. Agent tools can help here, but the scale of the error is also larger — tests and a careful review are required.

Tool selection table

For each candidate, find out where code, requests and file context are processed, stored, whether the content can be used for training, and what controls are in place in your tariff and contract. Check pre-pilot terms and conditions: settings and rules may vary between plans.

Team pilot: how to measure results reliably

The purpose of the pilot is to compare the full time to the accepted change and the quality of the result under similar conditions. Not the speed of code generation and not the impression of participants.

Proposed pilot protocol

1. Task categories. Break typical tasks into 3-5 categories: small new functionality, bug fixes, tests, refactoring, integration.
2. Rotation. Within each category, tasks are performed randomly or alternately with and without AI by the same developers. Do not compare simple new problems of one group with complex bugs of another.
3. What to record for each task:

• development time before opening a merger request;
• revue time;
• the number and duration of corrections after the review;
• defects found after the merger for a certain period;
• tool costs for this task, if payment depends on use;
• subjective assessment of convenience — separately, without mixing with time.

1. Duration. Determine in advance the habituation period and the minimum number of compared tasks in each category. If the sample is small, consider the result a signal to the next pilot, not a proof of percentage acceleration.
2. Decision criteria. Determine before you start what difference in total time, defects, and costs will be practically meaningful to you; with a small sample, do not make a categorical conclusion.

Example: fast code, slower task

Looking at development alone, the AI ​​"speeded up" the task by about 43%. If you count the full time to the accepted change, the version with AI is even a little slower: in the training example, the review takes three times as long. These fictional numbers don't prove anything about real instruments, but they do show why a full cycle should be measured.

Typical pilot mistakes

Even a well-intended pilot can be easily ruined by a few seemingly small decisions:

• participants themselves decide which tasks to do with AI. Then tasks where the tool will obviously help fall into the "with AI" group, and difficult ones - into the "without" group. The result looks convincing, but does not prove anything. In the 2026 research update, this selection effect also made interpretation of the results difficult;
• compare different periods. The first month with AI fell on the release, the second month without AI — during a quiet period. The difference in load overrides the effect of the tool;
• only tasks that have been completed are counted. Tasks abandoned halfway or rewritten from scratch after the review should also be included in the summary;
• does not include training. The first weeks with a new tool are slower. If the pilot lasts two weeks, you will measure habituation first;
• ignore costs. Agent scenarios with a large number of requests can cost significantly more than a fixed license; without taking into account costs for the task, the comparison is incomplete;
• change tool or model in mid-pilot. If the vendor has updated the model, record the date: before and after results should not be mixed without a mark.

Decision by roles

A tool that visibly helps one part of the team may be neutral or even hinder another. Therefore, the results of the pilot should be analyzed not only in average, but also by roles and types of tasks.

Possible conclusions, each of which is a normal result:

• developers of new services the agent tool saves time on the framework and tests;
• developers accompanying a large familiar code benefit little, and the time for verification increases - for them, chat is enough for searching and explanations;
• QA-engineers are helped by the generation of test data and scenarios, but not by adding code;
• new team members are helped by the explanation of the code, but rules are needed to avoid replacing understanding with generated solutions.

Multiple tools in a team are acceptable as long as they are few and manageable: clear data rules, centralized administration, responsible for each tool and a separate budget.

What to rely on when the results differ by role:

It is worth returning to the decision when something significant changes: model, tariff, data processing conditions, team composition or type of tasks. The output of a pilot is a snapshot of specific conditions, not a permanent property of the tool.

What to do in your team

1. Layer the selection: environment, assistant, model, corporate settings.
2. Check the candidates against the selection table for the pilot, especially data and administration.
3. Identify 3-5 problem categories and compare within each.
4. Measure total time to accepted change, review time, fixes, post-merge defects, and costs.
5. Fix versions of models and tools at the date of the pilot.
6. Analyze results by role, not just team average.
7. Make a decision separately for each role and revise it when the tools change significantly.

Frequently asked questions