There's a debate that keeps circling my feed: AI makes us write less code, and maybe that's the point; greatness, the argument goes, is forged by limitation. I think both camps are asking the wrong question. The question isn't whether AI coding tools are good in general, but whether one of them is good for you, and no benchmark on earth can answer that. The only honest test is the one you run yourself, on your own tasks, against a server you didn't have to pay for.
That's the reasoning behind an experiment I keep coming back to, and this time the tool is MonkeyCode, an open-source coding assistant with free model access and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The project being open source matters for an experiment like this, because you can see what the tool actually does instead of trusting a landing page. The current free allowance sits at ten million tokens, which sounds generous until you realize that the point of the experiment isn't the tokens at all. It's what the constraint does to your judgment.
Benchmarks are someone else's definition of a good day. A model that tops a coding leaderboard has solved problems that a benchmark author chose, on code that a benchmark author wrote, judged by criteria a benchmark author invented. Does that score tell you whether it can refactor your legacy module, or explain a nasty bug to a junior developer, or keep your API consistent across forty files? Of course it doesn't, and the more leaderboards I read, the more I suspect they measure the benchmark, not the model.
Here's where the free server becomes methodology instead of marketing. When you evaluate a tool you're paying for, your wallet argues on its behalf; you want the subscription to pay off, so you notice the wins and forget the misses. A free server removes that bias from the experiment, and a token allowance, however generous, forces you to treat every prompt like a line item in a budget. That's not a limitation to apologize for; it's the condition that makes the measurement honest.
So I wrote a small script that turns any chat-completions endpoint into a personal interview log. You give it a prompt file, it calls the model, and it records latency, token usage, and the first line of the answer in a CSV. If your endpoint speaks that protocol, and most free servers do, it works as-is; if not, the only thing you need to change is the one curl call. Run it against the endpoint your MonkeyCode free server gives you and you have a week-long experiment instead of a vibe.
#!/usr/bin/env bash
set -euo pipefail
# interview.sh — log one prompt against any chat-completions endpoint.
# Usage: BASE_URL=... MODEL=... ./interview.sh prompt.txt results.csv
BASE_URL="${BASE_URL:-}"
MODEL="${MODEL:-}"
PROMPT_FILE="${1:-prompt.txt}"
LOG="${2:-results.csv}"
if [[ -z "$BASE_URL" || -z "$MODEL" ]]; then
echo "Set BASE_URL and MODEL first." >&2
exit 1
fi
PROMPT="$(cat "$PROMPT_FILE")"
START=$(date +%s%N)
RESPONSE=$(curl -s "$BASE_URL/chat/completions" \
-H "Content-Type: application/json" \
-d "$(jq -n --arg m "$MODEL" --arg p "$PROMPT" \
'{model: $m, messages: [{role: "user", content: $p}]}')")
END=$(date +%s%N)
LATENCY_MS=$(( (END - START) / 1000000 ))
PROMPT_TOKENS=$(jq -r '.usage.prompt_tokens' <<<"$RESPONSE")
COMPLETION_TOKENS=$(jq -r '.usage.completion_tokens' <<<"$RESPONSE")
ANSWER=$(jq -r '.choices[0].message.content' <<<"$RESPONSE" | head -c 120)
echo "$(date -Iseconds),$LATENCY_MS,$PROMPT_TOKENS,$COMPLETION_TOKENS,\"$ANSWER\"" >> "$LOG"
echo "Logged $LATENCY_MS ms, $PROMPT_TOKENS + $COMPLETION_TOKENS tokens."
The script is deliberately dumb. It doesn't judge the answer, because that's your job; it just writes down what happened so you can't lie to yourself later. The most interesting column isn't latency; it's the ratio between prompt tokens and completion tokens. That ratio tells you whether you're delegating real thinking or just asking for autocomplete. You need curl and jq installed, and that's the whole dependency story. I keep the prompt files in the same repo as the results, so every row in the CSV points back to the exact words that produced it.
After each run, I answer three questions on paper. Did this output go into the codebase unchanged? Could I explain it to a colleague without rereading the transcript? Would I have been faster writing it myself? If two of the three are no, that task doesn't belong in my AI workflow, no matter what the leaderboard says. The questions look soft, but they're brutally effective at separating the tasks where AI amplifies you from the tasks where it just adds a middleman.
After a week, the CSV tells a story that no benchmark can. You'll see which tasks burn tokens without producing code, which prompts you keep rewriting because the model keeps misunderstanding, and which ones save you an hour every single time. That pattern is your personal benchmark, and it's the only one that matters when you decide whether to keep the tool around. The model isn't being tested; your collaboration is.
Let me be honest about the limits. The ten million token allowance is a snapshot, not a promise, so check the repository before you rely on it, and never treat a free server as production infrastructure. This experiment measures your workflow, not the model's ceiling, so it won't tell you which model is objectively best. If you're evaluating for a whole team, you need a bigger sample than one developer's backlog. It's also useless if you won't write prompts as carefully as you write code, because garbage prompts produce garbage measurements.
Benchmarks will keep arriving every week, and every week someone will tell you the new one changes everything. I'd rather spend that hour interviewing a model on my own code, with a free server, a token budget, and a CSV that doesn't flatter me. If you want to try the same experiment, grab the script, point it at the MonkeyCode free server, and run one real task from your backlog today. The result will tell you more than the next leaderboard ever will.
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