The 10M Token Question

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Most developers ask the wrong question about AI coding tools, and I used to be one of them. We argue about model rankings and leaderboard scores, yet the assistants that actually change our workflow are rarely the smartest ones; they are the ones we can afford to run badly. That is why I now evaluate every coding tool the same way: give it a hard token budget, put it on a free server, and watch what it does while the meter is running.

Benchmarks measure the model, not the loop, and that distinction is the whole game. A model can score beautifully on a curated suite and still burn forty thousand tokens explaining a syntax error, because no leaderboard measures the cost of a rambling conversation. The price signal is the only honest feedback loop we have, and most paid subscriptions hide it behind a flat monthly fee where waste feels free.

This is where MonkeyCode enters the picture, and I will be honest about my bias up front: the project gives you free model access, a free server option, and a ten-million-token allowance, which sounds like marketing until you treat it as an experiment budget. Ten million tokens is roughly enough to run a small feature through a coding assistant a few hundred times, which means you can finally measure the variance between a good day and a bad day. Generosity, in other words, is a debugging strategy rather than a giveaway, because it removes the excuse not to look at your own usage.

The free server matters just as much as the token budget, and localhost is a liar when it comes to agent behavior. An assistant that feels instant on your laptop will stall, retry, and quietly fail once it faces real network latency, cold starts, and rate limits, and a free server reproduces those conditions without asking for a credit card. If a tool cannot survive a constrained environment, it will not survive your production environment either, and you deserve to learn that before you pay for it.

The budget test

So here is the reproducible artifact I use, a small budget test that treats a real task as the unit of measurement instead of a benchmark question. Save the script below, point it at your tool's CLI or any OpenAI-compatible endpoint, and run it against a task file from your own repository. It takes about ten minutes, and if your endpoint is free, the only thing you spend is attention.

#!/usr/bin/env bash
# budget_test.sh — measure tokens per real task, not per benchmark
set -euo pipefail

TASK_FILE="${1:-task.md}"
LOG="budget_$(date +%Y%m%d_%H%M%S).jsonl"

run() {
  local label="$1" prompt="$2"
  local start end response usage
  start=$(date +%s)
  # Point ENDPOINT and API_KEY at your tool's CLI or compatible endpoint.
  response=$(curl -s "${ENDPOINT:-http://localhost:8080/v1/chat/completions}" \
    -H "Authorization: Bearer ${API_KEY:-test}" \
    -H "Content-Type: application/json" \
    -d "{\"model\":\"${MODEL:-default}\",\"messages\":[{\"role\":\"user\",\"content\":\"$prompt\"}]}")
  end=$(date +%s)
  usage=$(printf '%s' "$response" | jq -c '.usage // {prompt_tokens:0, completion_tokens:0}')
  printf '{"label":"%s","seconds":%d,"usage":%s}\n' "$label" "$((end-start))" "$usage" >> "$LOG"
}

run "explain" "Read the failing test in $TASK_FILE and explain the root cause in three sentences."
run "plan" "Propose a fix for $TASK_FILE without writing any code."
run "patch" "Write the minimal patch for $TASK_FILE."

jq -s '{runs:length,
        total_seconds:(map(.seconds)|add),
        total_prompt_tokens:(map(.usage.prompt_tokens)|add),
        total_completion_tokens:(map(.usage.completion_tokens)|add)}' "$LOG"

Run it three times on the same task, because one sample tells you nothing about variance, and then read the ledger like a doctor reads a chart. If the explain step burns more than a couple thousand tokens, your task file is too vague or the tool cannot see your codebase. If the plan step simply repeats the prompt back at you, the tool is ignoring your constraints, and no amount of clever prompting will fix that.

The decision table below is the part that turns the script into a decision, and I trust it more than any benchmark score. It converts three runs into a diagnosis, which is the difference between guessing and knowing. Read the row that matches your worst result, and act on it before you spend another token.

Observation	Diagnosis	Action
explain step > 2k tokens	Context too vague or codebase invisible	Tighten the task file, shrink the repo scope
plan echoes the prompt	Tool ignores constraints	Switch tool or endpoint before paying
patch fails twice in a row	Model cannot see the relevant code	Reduce file size, add explicit file paths
seconds grow across runs	Server or context degradation	Test again on a different free server

Limitations

Who should not use this approach? Teams with production SLAs should not run real workloads on a free server, and anyone in a regulated environment should check data policies before sending code anywhere. Free tiers also change without notice, so a ten-million-token budget is a starting point for measurement, not a permanent promise, and my script assumes an OpenAI-compatible endpoint or a thin CLI wrapper around whatever tool you choose.

The uncomfortable truth is that most of us do not know how many tokens we waste in a normal day, because the tools we pay for make waste invisible. A free server and a generous budget flip that equation, turning cost into a signal you can actually read, and that is why I now recommend the cheapest setup to everyone who asks. The next time somebody asks me which coding assistant is best, I ask them a different question: how many tokens did you waste yesterday?

If you do not know the answer, go find a free server, grab a budget, and start measuring, because the best assistant is the one whose waste you can see. MonkeyCode's free tier is a fine place to run this ledger, and the open-source codebase means you can read what the tool does before you trust what it says. If you run the budget test on your own tasks, I would genuinely like to hear what your token ledger tells you.