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The 10-Task Gauntlet: Measuring a Free Coding Model Before You Trust It

MonkeyCode, an open-source coding assistant, has released a 10-task gauntlet to evaluate free coding models. The gauntlet is designed to measure model performance across greenfield, refactor, and debug tasks, with each task repeated three times and scored by behavior rather than text. The project provides a reproducible script and decision table to help developers quantify where free models and servers perform well and where they break.

read4 min views1 publishedAug 23, 2026

A teammate pasted a 400-line function. "AI can split this," they said. The model returned 200 lines of fresh code. The build broke. Nobody measured why.

This week's AI conversation repeats one claim: write less code. Another says constraints forge better work. Both are testable. Free tiers make the test cheap. Cheap is not the same as measured.

MonkeyCode is an open-source coding assistant. Its free tier currently includes 10 million tokens and a free hosted server. Terms change. Verify them in the repo before you depend on them. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

This article is a hands-on evaluation. It designs a reproducible experiment. It quantifies where a free model and free server perform well. It shows where they break. The artifact is a 10-task gauntlet. Run it yourself. Publish your own scorecard.

Most model reviews quote one demo. One demo proves one thing. It proves nothing about your codebase. A single success hides variance. A single failure hides context.

The gauntlet fixes three gaps. First, it uses multiple tasks. Second, it repeats each task three times. Third, it scores behavior, not text.

Ten tasks. Three classes. One pass criterion per task.

Task classes:

Each task ships with three files. A prompt. A seed workspace. An assertion script.

The assertion script is the judge. It checks behavior. It never checks wording. A model can pass with different code. A model can fail with pretty code.

The script below runs the gauntlet. It isolates the model call in one adapter function. Replace run_model

with your client. CLI, API, or web. The rest stays the same.

#!/usr/bin/env bash
set -euo pipefail

TASK_DIR="${1:-tasks}"
RUNS="${2:-3}"
REPORT="report.json"

run_model() {
  local prompt_file="$1" workspace="$2"
  :
}

pass=0; fail=0; results=()

for task in "$TASK_DIR"/*/; do
  name=$(basename "$task")
  for run in $(seq 1 "$RUNS"); do
    ws=$(mktemp -d)
    cp -r "$task/seed/." "$ws/"
    start=$(date +%s.%N)
    run_model "$task/prompt.md" "$ws"
    end=$(date +%s.%N)
    time_s=$(echo "$end - $start" | bc)
    if "$task/assert.sh" "$ws"; then
      verdict="pass"; pass=$((pass+1))
    else
      verdict="fail"; fail=$((fail+1))
    fi
    results+=("{\"task\":\"$name\",\"run\":$run,\"verdict\":\"$verdict\",\"time_s\":$time_s}")
  done
done

printf '{"pass":%d,"fail":%d,"results":[%s]}\n' \
  "$pass" "$fail" "$(IFS=,; echo "${results[*]}")" > "$REPORT"
echo "done: $pass pass, $fail fail -> $REPORT"

The output is a JSON report. One object per run. Parse it. Plot it. Keep it. Record token usage from your client's usage field. The free tier counts those tokens. The scorecard shows where they go.

Record every run in a table. Median time beats average. Three runs expose variance.

Task Class Pass criteria Run 1 Run 2 Run 3 Median time
T01 greenfield unit test passes
T02 greenfield test suite passes
T03 greenfield CLI exits 0
T04 refactor behavior preserved
T05 refactor tests pass after split
T06 refactor API call migrated
T07 debug failing test fixed
T08 debug null bug fixed
T09 debug race fixed under load
T10 debug query under threshold

Use the decision table. It turns raw counts into a verdict.

Three failure modes show up fast. The gauntlet turns each one into a number.

Nondeterminism. The same prompt produces different code. Three runs catch this. One run never will. Look for runs with different verdicts.

Context loss. Multi-file tasks degrade before single-file tasks. The gauntlet isolates both. Watch which class fails first. Refactor failures usually signal context limits.

Close-but-wrong output. The model produces plausible code. The assertion script rejects it. This is the most common failure. It is also the most dangerous one.

The gauntlet measures ten tasks. It does not measure your codebase. It measures one model version. Model versions change. Free quotas change. Server performance changes.

The harness does not measure security. It does not measure maintainability. It does not measure license risk. Pass means behavior matches. Pass does not mean production-ready.

Teams shipping regulated code. The free tier cannot promise guarantees. Nobody should.

Teams with huge monorepos. Context windows shrink. Multi-file edits drift. Review costs explode.

Teams that skip review. The gauntlet proves nothing if humans skip the diff. Review every generated change. Always.

Clone the task set. Plug in your client. Run the gauntlet. Publish your scorecard. Try the free tier. Measure it.

The free tier is a real offer. Ten million tokens and a free server are real constraints. Constraints are testable. This week's hot takes will fade. Your scorecard will not.

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