Cost per Accepted Diff: The Number to Compare Free, Paid, and Self-Hosted Inference

The cheapest inference seat is almost never the one with the lowest sticker price; it is the one with the lowest cost per accepted diff, measured on your own task set before you commit. If you have spent the last month watching your team juggle three AI tools and still argue about which one pays for itself, this post is for you. Two of the most-discussed DEV posts in the past week circled the same fatigue from a cultural angle, and I want to answer it with arithmetic instead of vibes. So let me ask the uncomfortable question first: when did you last measure what an AI seat actually produces per dollar?

Below is the workflow I use: a fixed task set, a small harness you can run against any OpenAI-compatible endpoint, a review log, and a decision table that tells you when self-hosting or a paid seat is genuinely worth it. The code is a template, not a benchmark result — I am not going to hand you numbers from someone else's repo and pretend they transfer to yours.

Why the free-versus-self-hosted argument goes in circles

Most comparisons die because each side measures a different thing: one team quotes a monthly price, another quotes GPU-hours, a third quotes latency. None of those numbers survive contact with the only thing that matters, which is how many changes you accept after review. The result is a debate that never converges, and a pile of subscriptions nobody can defend at budget time. Isn't that exactly the fatigue everyone is writing about?

The fix is to make the unit of comparison the accepted diff, with review time folded in as a real cost. Review time is the part people forget, and it is usually the largest cost in the loop once a model is competent enough to be useful. A seat that produces twice as many usable patches but triples your review minutes is a worse seat, not a better one.

The metric, written down

Here is the definition I use, and it is deliberately boring:

cost_per_accepted_diff =
  (prompt_tokens * in_price + completion_tokens * out_price)  # metered spend
  + (review_minutes / 60 * loaded_hourly_rate)                # human spend
  + (setup_hours / amortization_months)                       # fixed spend
  / accepted_diffs                                            # denominator

Every term is an input you can defend in a budget meeting, and every term is something you can log mechanically except accepted. That one needs a human, which is why the workflow below spends most of its effort on a review step rather than on prompt tricks. Yes, it takes an afternoon — and no, you cannot get this from a leaderboard.

Step 1: Freeze a task set of 20 real changes

Pull twenty tasks from your own backlog, not from a public eval: six small (single function or config edit), ten medium (one file plus a test), and four deliberately nasty cases your current tool gets wrong. Store each as a JSON object with an id, the prompt you would actually type, and the files it may touch. Keep the set frozen for at least a quarter so that later runs are comparable, because a task set you keep editing measures nothing.

The four nasty cases matter more than the sixteen easy ones, because they are what separates a tool you keep from a tool you cancel. If you have no nasty cases handy, use the last four PRs that needed a second review round. Write them down before you run anything, or you will unconsciously grade in favor of whichever seat you already paid for.

Step 2: Run one harness against every seat

Any seat that speaks the OpenAI chat-completions shape can be driven by the same script, which is the whole reason this comparison is cheap to run. Save each output to disk, because your reviewer needs to read the diff, not your terminal scrollback.

# seatbench.py - one fixed task set, any OpenAI-compatible endpoint
import csv, json, time, urllib.request
from pathlib import Path

def ask(base, key, model, prompt, timeout=120):
    req = urllib.request.Request(
        base.rstrip("/") + "/chat/completions",
        data=json.dumps({"model": model, "temperature": 0,
                         "messages": [{"role": "user", "content": prompt}]}).encode(),
        headers={"Content-Type": "application/json",
                 "Authorization": f"Bearer {key}"})
    t0 = time.monotonic()
    with urllib.request.urlopen(req, timeout=timeout) as r:
        body = json.loads(r.read())
    usage = body.get("usage", {})
    return body["choices"][0]["message"]["content"], usage, time.monotonic() - t0

tasks = json.loads(Path("tasks.json").read_text())
seats = json.loads(Path("seats.json").read_text())  # label, base_url, model, key_env
writer = csv.writer(open("results.csv", "w", newline=""))
writer.writerow(["seat", "task", "tok_in", "tok_out", "latency_s",
                 "accepted", "review_min", "outfile"])

for seat in seats:
    import os
    key = os.environ[seat["key_env"]]
    for t in tasks:
        out, usage, dt = ask(seat["base_url"], key, seat["model"], t["prompt"])
        path = f"out/{seat['label']}-{t['id']}.md"
        Path(path).parent.mkdir(exist_ok=True)
        Path(path).write_text(out)
        writer.writerow([seat["label"], t["id"], usage.get("prompt_tokens", ""),
                         usage.get("completion_tokens", ""), round(dt, 1),
                         "", "", path])

seats.json is just a list of endpoints, so the same loop covers a hosted free tier, a paid API, and your own served model. Set the key through an environment variable and never inline it, and keep the whole directory out of your repo's commit history.

Step 3: Make a human score every output

A reviewer opens each output file, applies it to a scratch branch if it looks plausible, and fills two columns: accepted (y/n) and review_min (minutes spent). Reject anything that does not apply cleanly, because a diff you had to rewrite by hand is not a win for the tool. Time your reviews with a stopwatch for the first run; estimations drift toward round numbers and flatter your favorite.

This step is where most comparisons become honest, and it is also where free tiers often lose ground they looked like they were winning. Cheap tokens that generate almost-good patches convert into expensive review minutes. Conversely, a slow, metered seat that returns one correct diff may beat everyone on the only number that survives the quarter.

Step 4: Compute and compare

Read the CSV back, fill in your own price per million tokens for each seat (zero is a legitimate value), and print the composite.

import csv
from collections import defaultdict

PRICE_PER_MTOK = {"free-tier": 0.0, "paid-api": 3.0, "self-host": 0.0}
LOADED_RATE = 85.0  # your fully loaded hourly cost, not salary

rows = list(csv.DictReader(open("results.csv")))
by_seat = defaultdict(list)
for r in rows:
    by_seat[r["seat"]].append(r)

for seat, rs in by_seat.items():
    acc = [r for r in rs if r["accepted"] == "y"]
    mtok = sum(int(r["tok_in"] or 0) + int(r["tok_out"] or 0) for r in rs) / 1e6
    review = sum(float(r["review_min"] or 0) for r in rs) / 60
    spend = mtok * PRICE_PER_MTOK[seat] + review * LOADED_RATE
    print(f"{seat:12} accepted={len(acc):2} spend={spend:7.2f} "
          f"per_accepted={spend / max(len(acc), 1):7.2f}")

For self-hosting, add a break-even line instead of a token price: monthly_host_cost / cost_per_accepted_elsewhere gives the number of accepted diffs you must produce per month before the fixed cost is justified. If that number exceeds your realistic monthly diff volume, self-hosting is a hobby, which is fine as long as you call it one.

Step 5: Decide with fit criteria, not with the ranking

The lowest cost per accepted diff is necessary but not sufficient, so put the candidates through a second filter before you sign anything.

Option	Pick it when	Refuse it when
Free tier / free server	You need a zero-commitment seat for a 20-task trial, or your volume is spiky and low	Your data cannot leave your boundary, or your latency budget is tight
Paid API	Accepted-diff cost is stable and your volume is predictable	Your tokens per accepted diff drift upward without a re-measure
Self-hosted	You already run capacity, and break-even diff volume is clearly inside your monthly reality	Nobody owns the serving stack after launch

This is where a free option earns its place in a serious workflow rather than as a novelty: it lets you run the whole five-step measurement with no procurement cycle. MonkeyCode is a project in that space, offering free model access and a free server option; the operator states a free allowance of ten million tokens at the time of writing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I have not independently verified the current quota or server terms, and quotas of that kind move, so read the pricing page before you design around a number.

Step 6: Set a re-measure trigger

A decision made in October should not silently govern January, so write down the conditions that force a re-run. Re-measure when a model behind any seat changes, when your task mix shifts by more than a fifth, or when review minutes per accepted diff move by 25 percent in either direction. Add one calendar checkpoint per quarter and put the CSV in the same place each time, or the comparison quietly becomes folklore.

Limitations and who should skip this

Twenty tasks produce a direction, not a confidence interval, and the four nasty cases dominate the denominator more than they should. Free tiers and hosted models change under you, so any number here has a shelf life measured in weeks. Human review timing is noisy, and a single slow afternoon can move a seat's ranking. If your weekly diff volume is under ten, or your data cannot touch a hosted endpoint at all, skip the framework and just pick the option your compliance team will actually approve.

What would change your mind about the seat you are paying for right now — and could you prove it with a CSV by Friday?