A 10M-Token Budget Is a Test Plan: C++ Patch Trials on MonkeyCode's Free Tier

#ai #cpp #testing #llm

A team gets handed a free allocation: ten million tokens and a server that costs nothing. The first instinct is to treat it like a lottery ticket. Generate everything. Paste patches into the codebase. See what sticks.

The second instinct is better. Treat the allocation as a measurement instrument. A fixed budget forces a team to decide, in advance, what a useful patch looks like. That decision is the actual deliverable. The tokens are just fuel.

MonkeyCode is an open-source AI coding project. Its free tier includes 10 million tokens and a free server option, which means the usual excuses — no GPU, no key, no budget — stop applying. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The exact numbers matter less than the constraint they create. This article shows how to spend that budget on C++ patch trials without wasting it, and how to know when the free tier is no longer the right environment.

Why a hard budget changes the experiment

Unlimited tokens hide sloppy prompts. When a generation costs nothing, nobody minimizes the input. They paste a 400-line translation unit and hope the model finds the race. With a 10M cap, prompt size becomes a design decision.

A budget also forces a stop condition. Before the first request, a team must define what counts as a passing patch. Compile clean? TSan clean? Timeout respected? If the definition is vague, the budget will be spent on output that nobody can judge.

The current wave of AI coding tools is full of free tiers. Access is no longer the differentiator; discipline is. The free server option removes the infrastructure excuse, so the remaining variable is the evaluation design. That is the point of this workflow.

Three decisions before the first token

Define the unit of work. One task equals one minimal reproducer plus one expected behavior. No production code in the prompt, no speculative features.
Define the pass condition. For C++ concurrency fixes, a defensible bar is: compiles with -fsanitize=thread, runs under TSan without reports, and exits within a timeout.
Define the stop rule. Spend at most 80% of the allocation. The last 20% is reserved for re-runs and for the patches that almost passed.

The budgeted trial script

The script below is the artifact. It reads a task list, sends each reproducer to the free server endpoint, compiles the returned patch, and runs it under TSan. Token accounting comes from the response metadata. The client call is a placeholder, because the exact invocation depends on the endpoint you are using. Treat the script as a template, not a shipped tool.

#!/usr/bin/env bash
# budget_patch_trial.sh — C++ patch trial under a hard token budget
set -euo pipefail

BUDGET_TOKENS="${1:-8000000}"   # 80% of the 10M free allocation
TASKS_FILE="${2:-tasks.txt}"
OUT_DIR="results"
ENDPOINT="${MONKEYCODE_ENDPOINT:?set to your free server endpoint}"

mkdir -p "$OUT_DIR"
spent=0
passed=0
failed=0

while IFS= read -r task; do
  [ -z "$task" ] && continue
  echo "==> $task"

  prompt="Fix the data race in this C++ reproducer. Output only the patched file.
$(cat "tasks/$task/repro.cpp")"

  # Placeholder client call — replace with the SDK or curl command for your endpoint.
  response=$(monkeycode-complete \
    --endpoint "$ENDPOINT" \
    --prompt "$prompt" \
    --max-tokens 2048)

  echo "$response" > "$OUT_DIR/$task.patch.cpp"

  if g++ -std=c++20 -fsanitize=thread -g "$OUT_DIR/$task.patch.cpp" \
       -o "$OUT_DIR/$task.bin" 2>/dev/null \
     && timeout 10 "$OUT_DIR/$task.bin" >/dev/null 2>&1; then
    echo "PASS"
    passed=$((passed + 1))
  else
    echo "FAIL"
    failed=$((failed + 1))
  fi

  # Token accounting from response metadata; exact fields vary by endpoint.
  prompt_tokens=$(echo "$response" | jq -r '.usage.prompt_tokens')
  completion_tokens=$(echo "$response" | jq -r '.usage.completion_tokens')
  spent=$((spent + prompt_tokens + completion_tokens))
  echo "spent=$spent / $BUDGET_TOKENS"

  if [ "$spent" -ge "$BUDGET_TOKENS" ]; then
    echo "Budget exhausted. Stopping."
    break
  fi
done < "$TASKS_FILE"

echo "passed=$passed failed=$failed spent=$spent"

The script is deliberately boring. That is the point. No model leaderboard, no demo gifs, no "it works on my machine" claims. The output is a pass count, a fail count, and a token ledger.

Reading the results

Three numbers matter after a run: pass rate, tokens per task, and where the tokens went.

If completion tokens dominate, the model is over-generating. Tighten --max-tokens and re-run. If prompt tokens dominate, the reproducer is too large. Minimize it. A race that needs 300 lines to reproduce usually needs a better reproducer, not a bigger prompt.

If the pass rate is low, do not blame the model first. Check the harness. A common failure: the patch is correct, but the reproducer has unrelated undefined behavior that TSan flags. The harness must distinguish "the patch broke it" from "the reproducer was already broken." One way is to run the original reproducer through the same harness once, before any model sees it, and record the baseline.

When the free tier is enough

The decision to stay on the free tier is not about token price. It is about fit. Use this matrix as the framework:

Signal	Stay on free tier + free server	Move to paid or self-hosted
Patches evaluated per week	Under 50	Over 200
Concurrency complexity	Small reproducers, one or two threads	Production-scale multi-threaded code
Latency tolerance	Batch runs, async review	Interactive, blocking workflow
Data sensitivity	Public or open-source code	Proprietary or regulated code
Verification cost	Compile + TSan under a minute	Long builds, multiple sanitizers
Budget consumption	Less than 70% of the allocation per month	Hitting the cap every week

Two signals matter more than the rest. If the verification step is heavier than the generation step, the free tier will not save you — the bottleneck is your build, not the model. And if the code cannot leave your network, a hosted free server is disqualified by policy, not by price.

Limitations

The 10M-token allocation is a trial budget, not production throughput. The free server's latency and availability are outside your control; do not build a blocking workflow on it. Model behavior on a free tier can differ from paid or self-hosted models, so measure the pass rate before you extrapolate.

Who should not use this approach: teams with strict data-residency rules, teams whose builds take longer than the generation, and anyone who needs a latency guarantee. For those cases, the matrix points to self-hosting or a paid tier — not because the free tier is bad, but because it is the wrong instrument.

The budget is the test plan

A 10M-token allocation is not a prize. It is a test plan with the numbers filled in. The discipline it forces — minimal prompts, a defined pass condition, a hard stop — is the same discipline that makes paid tiers useful later.

If this workflow fits your evaluation problem, MonkeyCode's free tier is a reasonable place to start: 10 million tokens, a free server, and an open-source project you can inspect. Point the budget at your own reproducers before you point it at your wallet.

MonkeyCode provides free models that can run this workflow.

DEV Community