10 Million Free Tokens: A Token-Budget Field Test on a Free Server

A teammate received a free AI coding allowance last month. He burned it in two days. Not on complex architecture. On repeated full-file rewrites. Each rewrite consumed thousands of tokens. The allowance died before the week ended.

Most developers cannot answer one simple question: the token cost of a refactor. This article answers it with a reproducible harness. The goal is planning a 10-million-token allowance instead of guessing.

The test target is MonkeyCode, an open-source AI coding project. It offers free model access and a free server option. The operator states the free allowance at 10 million tokens. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

The experiment measures one thing: how far the allowance goes on realistic tasks. It also shows where the free server performs well and where it breaks.

The measurement harness

Token counting is the foundation. Real tokenizers vary by model. A 4-characters-per-token heuristic is stable enough for budgeting. The harness logs every request.

# budget_harness.py
import csv
import time
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    system: str
    user: str

@dataclass
class Run:
    task: str
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int
    latency_s: float
    status: str

def estimate_tokens(text: str) -> int:
    # 4 chars per token is a safe budgeting heuristic.
    return max(1, len(text) // 4)

def call_model(system: str, user: str) -> str:
    """Replace with the provider SDK call."""
    raise NotImplementedError("Add your provider adapter here.")

def run_task(task: Task) -> Run:
    prompt_tokens = estimate_tokens(task.system) + estimate_tokens(task.user)
    start = time.perf_counter()
    try:
        output = call_model(task.system, task.user)
        status = "ok"
    except Exception as exc:
        output = str(exc)
        status = "failed"
    latency = round(time.perf_counter() - start, 2)
    completion_tokens = estimate_tokens(output)
    return Run(task.name, prompt_tokens, completion_tokens,
               prompt_tokens + completion_tokens, latency, status)

The harness writes a CSV log. Each row records task, prompt tokens, completion tokens, total, latency, and status. The call_model stub is the only part to replace. Swap in the provider SDK.

Five realistic tasks

Token cost depends on task shape. The benchmark set covers five common shapes.

TASKS = [
    Task("generate", "You write short, correct Python. No explanations.",
         "Write a pagination helper. Inputs: page, page_size, total. "
         "Return a dict with items, page, has_next, total_pages."),
    Task("debug", "You explain one root cause. Be brief.",
         "This traceback appears: [paste traceback]. What is the root cause?"),
    Task("tests", "You write pytest tests. Use asserts. Cover edge cases.",
         "Write tests for the pagination helper."),
    Task("refactor", "You refactor Python for readability. Keep behavior identical.",
         "Refactor this function: [paste 30-line function]."),
    Task("rewrite", "You rewrite this whole file. Keep the public API.",
         "Rewrite this file with better structure: [paste 200-line file]."),
]

Sample run: five repetitions per task, identical prompts.

Task	Prompt tokens	Completion tokens	Total per run
generate	~120	~180	~300
debug	~210	~140	~350
tests	~260	~390	~650
refactor	~350	~420	~770
rewrite	~1,400	~1,900	~3,300

Full-file rewrites cost eleven times a fresh generation. That is the entire budgeting problem in one table.

What 10 million tokens buys

Simple division turns the table into a plan.

• ~33,000 generation tasks
• ~28,000 debug sessions
• ~15,000 test-writing tasks
• ~13,000 refactors
• ~3,000 full-file rewrites

Task choice changes capacity by an order of magnitude. A team that defaults to rewrites gets 3,000 tasks. A team that defaults to targeted edits gets 33,000. The cheapest token is the one you never send.

Batch test on the free server

The harness then ran 50 tasks on the free server. Ten per task type. Sequential execution. No retries.

Sample results:

• Wall time: 31 minutes
• Success: 47 of 50
• Failures: 2 timeouts on rewrite tasks, 1 rejected burst on debug
• Median latency: ~28 seconds
• Slowest task: rewrite at ~90 seconds

The free server performs well on short, focused tasks. Generations and debug sessions completed without drama. It breaks on long completions and rapid sequential calls. Two rewrites exceeded the response window. One burst of quick calls was rejected.

Five budget rules

1. Count before you send. The harness logs prompt tokens per task. Know the cost before the request.
2. Prefer targeted edits. A refactor costs 2.5x a generation. A rewrite costs 11x.
3. Cap the context. Paste the relevant function. Not the whole file.
4. Batch small tasks. One session with many small prompts beats one giant prompt.
5. Track a running total. The CSV log tells you when to stop.

Who should not use this approach

Free tiers carry no guarantees. Teams with client SLAs should not build on them. Large monorepo work explodes token counts. Regulated environments need audit trails a free server does not promise.

Limitations

Sample size is 50 tasks over one day. The heuristic counts 4 characters per token. Real tokenizers differ. Quotas and model availability change. Verify current terms before relying on any number here.

The takeaway

Token blindness is a budget leak. A 10-million-token allowance is generous on small tasks. It evaporates on rewrites. The harness turns guessing into accounting. Fork it, run it, and share your numbers.