Alright, so your team is thinking of adopting Aider, but you want to move past "it feels faster" and get some real, reproducible numbers on how it impacts your actual workflow. Good call. Here's how we set up a simple but effective benchmark.
You'll need:
* A **dedicated Git branch** for the benchmark.
* A **small, representative codebase** – maybe a core service module or a set of common components.
* A **pre-defined task list** that mirrors your team's real work (e.g., "Add error logging to X function," "Refactor Y class to use dependency injection," "Write unit tests for Z module").
Our process:
1. **Baseline:** Time yourself (or a team member) completing each task *without* Aider. Record the time and commit the final, working code for each.
2. **Reset & Run:** Hard reset the branch. Now, tackle the same tasks *with* Aider, using the same prompts/instructions. Record the time again and the final code.
3. **Compare:** Look at total time saved/lost, but also at code quality in the diffs. Did Aider introduce subtle bugs? Did it save more time on boilerplate than on logic?
Key things we track:
* Total elapsed time per task.
* Number of "chat turns" or clarifications needed with Aider.
* Manual intervention required (e.g., having to fix a broken import).
This gives you hard data on if Aider speeds up *your* workflow, not just a synthetic benchmark. It also helps the team learn its quirks. We found it cut boilerplate time by ~40%, but needed careful prompting for complex logic. Your mileage will vary!
What metrics would your team care about most? 🧐
--ash
data over opinions
That's a solid setup for getting concrete data. We did something similar, but we also captured the Git commit hash after each task completion, both with and without Aider. This let us generate a simple diff between the AI-assisted and human-only versions of the same functional change.
We fed those diffs into our standard PR review checklist and found the comparison super useful. It highlighted where Aider was surprisingly good at repetitive patterns but would sometimes miss a subtle conditional edge case a human would catch.
Tracking the number of clarifications needed is key. In our runs, if that number was high, the total time saved often evaporated.
Cloud cost nerd. No, I don't use Reserved Instances.
Capturing the commit hash for a diff-based review is a clever addition. It moves the analysis from "how fast" to "what kind of code," which is where the real TCO discussion happens.
Your point about clarifications directly impacting time saved is crucial. We formalized that as a cost metric. If a task took 10 minutes with Aider but required 5 clarification loops, we logged the total *calendar* time for completion, not just active coding minutes. That often changed the ROI picture entirely.
We found those diffs were most valuable during contract renewal talks with vendors. Being able to show concrete examples of where the AI assistance added versus introduced subtle tech debt gave us much better negotiation leverage.
Buy once, cry once.
The methodology you've outlined is fundamentally sound for isolating the variable of speed. However, a hard reset of the branch for the Aider run might inadvertently skew the results if your tasks have any logical dependencies.
We structured our benchmark with independent, atomic tasks for exactly that reason, ensuring each could be started from the same clean branch state. Otherwise, the order in which you complete tasks with Aider could artificially inflate or deflate its performance if task B builds on changes from task A, which wouldn't mirror the baseline human run.
Another metric we layered on was tracking the LLM's context window consumption per task by logging token counts from the Aider conversation. A high number of clarification turns paired with a high token count often correlated with the AI losing the thread on architectural patterns, which is a useful signal for where its utility drops off sharply.
Data over dogma
Good point about the atomic tasks. The context window consumption metric is interesting, but I'd be more concerned about cost creep than just signal quality.
> a high token count often correlated with the AI losing the thread
That's a useful signal, but it's a downstream effect. The real problem is when the model starts spinning its wheels, racking up API costs while you're trying to steer it back. You're paying for those lost-thread tokens, too. Logging the cost per clarification loop is where you'll see the ROI really collapse for certain types of tasks.
Did you find any correlation between specific task types (like refactoring vs. new feature) and that token bloat?
- Nina
Oh, absolutely. The correlation is strong and it's exactly where the cost hemorrhage starts.
Refactoring tasks were our worst offenders for token bloat. The AI would get stuck in a loop proposing different, subtly broken variations of the same change. You're paying for every single one of those bad drafts. It feels like funding a junior dev's indecision, but at API call prices.
New feature tasks were cleaner, but only if the spec was insanely tight. A vague "add a new endpoint" would balloon. The sweet spot was boilerplate generation - adding a standard logging wrapper or a new DTO class. Minimal clarifications, low token count, positive ROI.
But you've hit the nail on the head: logging the *cost per clarification loop* is the only metric that matters for the bean counters. Time saved is soft, but an invoice line item showing $4.72 to add a try-catch block? That gets a VP's attention real fast.
Your framework is a good starting point, but it's missing a key variable: operator skill level. Timing yourself without Aider is clear, but the benchmark will be skewed if the person running the Aider test isn't proficient with its prompting conventions.
You need to factor in a learning curve buffer or, better yet, have the same person run both tests only after they've reached a baseline competence with the tool. Otherwise, you're measuring unfamiliarity with Aider's workflow, not its inherent capability.
independent eye
Totally valid point. The learning curve is a real, silent cost.
We ran into this by accident when our senior dev's numbers looked great, but the mid-level dev's initial runs showed negative ROI for weeks. The difference was familiarity with Aider's command set and knowing when to just take the wheel.
A buffer period with non-benchmark tasks is key. But you have to measure the time/cost to reach that baseline proficiency too. If it takes 10 hours of paid dev time to get competent, that's a real upfront investment.
Ask me about hidden egress costs.
Good starting framework, and I like that you're focusing on the core metrics of time and code quality. The key for us was making those "pre-defined tasks" incredibly granular. We started too broad and the variance in the Aider runs was huge.
One addition: you might want to record the *quality* of the initial prompt given to Aider for each task. We found that even with a pre-defined task list, the wording we used changed between the baseline and Aider runs, which added noise. Standardizing that prompt language as part of the benchmark script gave us cleaner data.
That's an excellent addition. Standardizing the prompt language as part of the benchmark script is a great way to reduce that variable.
It reminds me of a related pitfall: even with a standardized prompt, the quality of the *context* you give Aider before starting matters just as much. If the initial file map or `/ls` output is too sparse or too cluttered, it changes the starting conditions dramatically. You might standardize the words but not the working environment, which introduces another layer of noise.
—daniel
You're onto something crucial with the context problem. Standardizing the prompt is just one side of the coin.
> the quality of the *context* you give Aider before starting matters just as much
Exactly. And that's nearly impossible to fully standardize unless you're benchmarking against a sterile, toy codebase. In a real project, the "working environment" is dynamic. The state of the file map changes with each prior Aider interaction during a session, which these atomic task benchmarks often ignore. You might standardize the initial `/ls`, but by task three, the context is polluted with the model's own earlier, potentially confusing, edits.
This turns every benchmark into a best-case scenario, not a real-world one.
Data skeptic, not a data cynic.
Solid starting points. Your step 3 is where I think most teams will get tripped up, though. Comparing diffs for subtle bugs is a lot harder than it sounds. It's easy to spot a syntax error, but a logic regression from a "helpful" refactor might only surface later. We started running the Aider-generated code through our full test suite and static analysis for every task, not just eyeballing it. That extra step gave us much clearer data on "quality" beyond just time saved.
You're framing this like a tidy science experiment, but I think the "pre-defined task list" is already setting you up for unrealistic results. The real value, or lack thereof, of these tools is often in the messy, undefined tasks - the "what's broken in this legacy module?" or "can you untangle this dependency?" The benchmark you propose optimizes for the kind of work Aider is already good at, giving you a best-case scenario that won't hold up when things get ambiguous.
But what about the edge case?
This is a really helpful starting point, thanks for laying it out. I'm already thinking about step 2, though. When you say "using the same prompts/instructions" for the Aider run, how do you actually *record* those? Do you write down the exact command you'd type in your head before starting the manual task? I worry my mental prompt for myself is way too vague compared to what I'd need to type for the AI, which skews the baseline timing.
Exactly. And the obsession with "standardizing the context" is a rabbit hole that leads to benchmarking in a fantasy sandbox.
You can't standardize the working environment in a real project. The file map is different for everyone because git history, open tabs, and terminal scrollback differ. Even the *order* of tasks changes the model's internal context window.
So you're benchmarking a sterile lab condition, not the messy kitchen where you actually cook. The noise *is* the signal. If your benchmark can't handle dynamic context, it's measuring the wrong thing.