Any recommendations for the best free Copilot rank tracker tool

General
1

I’ve been experimenting with GitHub Copilot and similar AI coding assistants, and now I’d really like a free tool that can track or compare how well different Copilots perform over time. I’m mainly interested in features like ranking suggestions, monitoring accuracy, and maybe basic analytics so I can see which Copilot is actually helping my workflow the most. Are there any truly free Copilot rank tracker tools you’ve used that you’d recommend, and what are their limitations or hidden downsides

2

Short answer. There is no good “Copilot rank tracker” tool today that works like an SEO rank tracker, especially not free.

What you can do is fake it with a mix of scripts and logging. Some options that get close:

  1. Use Aider + logs
    • Aider is an open source AI coding assistant that runs in your terminal.
    • You can point it to different models, including GitHub Copilot Chat API (through OpenAI models), Claude, etc.
    • Because it runs local, you can log:

    • prompt
    • model name
    • tokens used
    • response time
    • whether you accepted or edited the suggestion
      • Repo: search “paul-gauthier/aider” on GitHub.

  2. Use VS Code + telemetry extensions
    • There is no single “Copilot ranker”, but you can roll your own.
    • Idea:

    • Use GitHub Copilot, Copilot Chat, Cursor, Codeium, etc.
    • Enable “edit history” or use GitLens to track changes.
    • Write a small script to parse your git history and count:
      • lines inserted by the AI
      • lines reverted or changed within 5 minutes
        • You tag sessions manually: for example commit message “copilot-github” vs “copilot-codeium”, then compare.

  3. Open source benchmarks for LLMs
    These do not track over time for your own workflow, but you can see public comparisons.
    • lmsys Chatbot Arena Leaderboard
    • OpenLLM Leaderboard on Hugging Face
    • They score models on coding tasks like HumanEval, MBPP, etc.
    You can run similar tests yourself:
    • Take a fixed set of prompts from your real work.
    • Call each provider through API.
    • Save outputs in JSON.
    • Review once a week and score 1 to 5 on:

    • correctness
    • compile success
    • test pass rate
      It is manual, but you get repeatable numbers.

  4. Simple “rank tracker” script idea
    If you are comfortable with Python, you can build a basic tracker in a weekend.
    Rough outline:
    • Write a test harness with 30 to 50 coding tasks.
    • For each provider:

    • call the model, feed the task
    • store response, time, tokens, model version
      • Auto run unit tests on generated code.
      • Save all results to SQLite or CSV.
      • Plot trends per provider once a week with matplotlib.
      That gives you an personal leaderboard.

  5. Things to track for your use case
    Based on your description, I would track:
    • Response latency in seconds.
    • Token cost per day.
    • Test pass rate for generated code.
    • Number of edits before code runs.
    • Subjective rating you click in a small prompt tool, like 1 to 5.

  6. Tools that help but are not exactly rankers
    • Continue.dev

    • Open source VS Code / JetBrains extension.
    • You can plug multiple backends, like Copilot-compatible, OpenAI, Anthropic, etc.
    • Good for side by side usage.
      • Zed / Cursor editors
    • Both have first class AI.
    • You can switch providers fast and feel which one fits your flow.
      For real “tracking”, you still need custom logging.

  7. Why no off the shelf free rank tracker yet
    • Every Copilot product has its own UX and EULA.
    • Automated logging of suggestions and your code touches privacy and IP issues.
    • Usage patterns differ a lot between users, so a generic rank metric is hard.

If you want the closest thing without writing code, I would:

  1. Use Continue.dev in VS Code with two models configured.
  2. Use GitLens and a simple git hook that logs which model you used per commit.
  3. Review commits weekly and rate “helpfulness” in a spreadsheet.

It is a bit manual and boring, but it works and stays under your control.

3

Short version: there isn’t a neat, free “Copilot rank tracker” yet, but you can get close if you’re willing to be a bit scrappy.

I mostly agree with @sternenwanderer, but I’d actually push you in a slightly different direction: instead of logging everything and building a huge benchmarking rig, focus on small, frictionless signals in your actual editor, because anything too manual will die after 3 days.

A few ideas that don’t repeat what was already suggested:

  1. Lightweight in‑editor rating hotkeys

    • Use something like a global hotkey tool (AutoHotkey on Windows, skhd/Karabiner on mac, whatever on Linux).
    • Bind:
      • Ctrl+Alt+1 = “this suggestion sucked”
      • Ctrl+Alt+2 = “meh”
      • Ctrl+Alt+3 = “good”
    • Each hotkey writes a JSON line to a local log file:
      {'ts':'2026-03-02T12:34:56Z','model':'github-copilot','score':3,'lang':'ts'}
    • You only press it when the AI suggestion actually mattered.
    • Once a week, quick script to aggregate by model & language.
      This is way less work than trying to auto‑track every single insertion.

  2. Model‑per‑day rotation instead of side‑by‑side
    I kinda disagree with the constant multi‑tool switching idea. Using 3 copilots at once tends to bias results toward “the one you used on easy stuff.”

    • Pick a schedule like:
      • Mon/Tue: GitHub Copilot
      • Wed: Cursor / Codeium
      • Thu/Fri: Claude / OpenAI
    • Keep everything else fixed: same editor, same plugins.
    • Tag each day in your log / rating system by model.
      You’ll get a cleaner signal on “how it feels to live with this thing” over time.

  3. Track pain instead of just success
    Most folks track “did it work,” but what usually makes a copilot bad isn’t that it never works, it’s that it wastes your time. Things that are actually measurable with minimal setup:

    • Count how often you hit “undo” right after an AI suggestion. If you use a consistent keybinding to trigger completions, you can watch for:
      • “AI key pressed → large paste → undo within 3 seconds.”
    • Count how many times a file switches from “tests passing” to “tests failing” within 2 minutes of inserting AI code. A simple git diff + test runner hook can approximate this.
      That effectively measures “regret rate” instead of just success rate.

  4. Language / stack‑specific scoring
    All the leaderboard stuff is nice, but it hides a big issue: a model that’s great at Python might be terrible on your React + TypeScript + weird internal API setup.

    • In your logs or spreadsheet, always store language and maybe project.
    • After a few weeks, you might find something like:
      • Copilot: awesome in TS, mediocre in infra
      • Another: better in SQL / data stuff
        Then you can route your tasks: one copilot for frontend, another for data / scripting.

  5. Simple “time cost” metric
    Instead of trying to auto‑measure response time per request, just track:

    • “How many minutes from first AI suggestion to green tests?”
      Use a small CLI “timer” you start and stop manually per task, and note the model used. Very low tech, but it gives you an actual business‑relevant metric: “who gets me to done faster.”

  6. UX friction as a metric
    This is super subjective, but still quantifiable with a 1–5 rating logged at the end of a coding session:

    • 1 = I turned this thing off out of frustration
    • 3 = neutral, didn’t help much
    • 5 = I would pay real money for this today
      One line append to a log when you stop for lunch or end your day. That “session score” will often correlate better to reality than synthetic benchmarks.

If you want a “closest thing to a rank tracker” without going full data‑scientist:

  • Rotate tools by day.
  • Use quick hotkeys to rate only meaningful suggestions.
  • Once a week, throw the log into a simple script or spreadsheet and graph:
    • avg score per model
    • per language
    • maybe per project

You won’t get a fancy dashboard with ELO scores for copilots, but you will know which one actually pulls its weight in your stack, for free, without turning your life into a research project.