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Track an AI coding agent with two numbers: a local estimate before each request and the usage record returned by the provider after it finishes. Add prompt, completion, and total tokens to a small append-only log, then sum that log against your budget. A 10-million-token allowance is a planning scenario, not a universal free-tier promise. If an edit averages about 12,000 total tokens, simple division suggests roughly 800 calls, but retries, tool output, conversation history, and provider-specific accounting can reduce that number.
The workflow below is based on Sam Yang’s Aug. 31, 2026 article. That article says it was prepared as part of MonkeyCode’s product outreach; its session figures are attributed examples, not an independent benchmark or a guarantee for any server.
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What does a “10M token budget” actually mean?
It means you have chosen 10,000,000 tokens as a spending ceiling for a project or account and will stop, shrink requests, or switch capacity before the cumulative total reaches it. The number is useful for planning even when a provider uses a different allowance model.
Free offers can be expressed as input tokens, output tokens, combined tokens, requests, credits, or a rate limit. Some grants recur, some are one-time signup credits, and some provide nominally free access but restrict concurrency or speed. A directory snapshot or an article can become stale quickly, so confirm the current provider terms, region, model, expiry date, and whether failed or retried requests consume quota.
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Do not describe 10 million tokens as what every free server supplies. Treat it as a worked budget. Your actual ceiling may be lower, split across models, or measured in requests rather than tokens.
How many edits does 10 million tokens buy?
Use your observed average, not a generic claim. If the average total is 12,000 tokens, the arithmetic is:
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Rounding down gives about 800 calls as a cautious headline figure. That estimate assumes every call has the same size, there are no retries or parallel agents, and all ten million tokens are available for the same activity. It is derived from the example article’s assumption, not a population study of code edits.
Which parts of a coding-agent request consume tokens?
A request is larger than the function you asked the agent to change. Count every piece of text that the provider actually receives, and account separately for the model’s response.
- System instructions: agent rules, tool descriptions, safety instructions, and repository conventions.
- Your task message: the requested change, acceptance criteria, and error output.
- Source context: selected files, symbols, documentation excerpts, and the current diff.
- Conversation history: previous turns that the integration resends on each call.
- Tool payloads: shell output, compiler messages, test results, directory listings, and file contents returned to the model.
- Completion: the model’s explanation, patch, tool calls, or structured response.
A local counter that measures only a prompt string and a diff will miss anything omitted from that string. It is still valuable as a quick estimate, but label it as an estimate and make the boundary explicit.
How do I estimate tokens before sending an edit?
Use the tokenizer example from the source article as a preflight check, then adjust it to include the exact material your integration sends. The example uses Python’s tiktoken package with cl100k_base. That encoding is an illustration, not a universally exact billing tokenizer for every current model.
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- Assemble the real payload. Put the system instructions, task text, selected code, diff, and any tool output that will be sent into one representation. Do not count a shortened preview if the agent will receive the full file.
- Tokenize the representation. Count encoded tokens locally and record the estimate before making the request.
- Keep the estimate beside the eventual usage record. The comparison tells you whether you omitted history, tools, wrappers, or provider-added fields.
- Decide whether to shrink the task. If the estimate is already a large fraction of your remaining budget, split the work or remove irrelevant context before calling the model.
import tiktoken
# Illustrative local estimate; replace these strings with the exact payload.
enc = tiktoken.get_encoding("cl100k_base")
system_text = "You are a coding agent. Follow the repository instructions."
task_text = "Fix the failing validation in the changed function."
diff_text = """@@ -42,7 +42,9 @@n- return parse(value)n+ return parse(value, strict=True)n"""
estimate = sum(len(enc.encode(part)) for part in (
system_text, task_text, diff_text
))
print({"estimated_input_tokens": estimate})The count above does not prove what the provider will bill. Chat wrappers may add role markers, JSON schemas, tool definitions, or hidden instructions. Some models use a tokenizer that differs from cl100k_base. The only reliable way to calibrate the estimate is to compare it with returned usage for the same integration.
What should the preflight counter include?
Include the complete conversation that is transmitted on that request, not merely the newest user message. Include tool results and the text of any file the agent can see. If your framework automatically adds a repository map, code index, or function signatures, either obtain that serialized payload or mark the estimate as incomplete.
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Keep a field such as estimate_scope with values like prompt_and_diff_only or full_serialized_request. That small label prevents a partial estimate from being mistaken for a provider-equivalent count months later.
How do I capture the usage returned after each response?
Read the usage metadata attached to the response object and append a compact record. Common fields are prompt tokens, completion tokens, and total tokens, but names and availability vary by provider, SDK, and agent framework. Inspect one real response rather than assuming a field path.
import json
from datetime import datetime, timezone
def append_usage(log_path, response, estimate, scope):
# Adapt these lookups to the response object used by your provider.
usage = getattr(response, "usage", None) or {}
record = {
"timestamp": datetime.now(timezone.utc).isoformat(),
"estimated_input_tokens": estimate,
"estimate_scope": scope,
"prompt_tokens": usage.get("prompt_tokens"),
"completion_tokens": usage.get("completion_tokens"),
"total_tokens": usage.get("total_tokens"),
}
with open(log_path, "a", encoding="utf-8") as file:
file.write(json.dumps(record) + "n")
This is a logging pattern, not a drop-in universal API. Some SDKs expose an object with attributes instead of a dictionary; some return cached-token fields separately; some omit usage for streaming, errors, or certain free endpoints. Adapt the extraction code, and preserve null when a value is genuinely unavailable instead of inventing a zero.
What belongs in the log?
For budgeting, metadata is usually enough:
- UTC timestamp
- model or endpoint identifier
- request or task identifier
- local input estimate and its scope
- prompt, completion, and total tokens returned by the provider
- success, timeout, or error status
- retry or parent-request identifier
Avoid storing full prompts, source files, or model output in a shared log unless you deliberately accept that retention. Code can contain credentials, customer data, private algorithms, or security findings. The workflow described here does not establish the privacy or security practices of any particular free host, so review its data terms separately.
How do I compare the estimate with actual usage?
Compare three values for every completed request: the local estimate, the provider’s prompt count, and the provider’s total count. The gap identifies where your preflight model is incomplete; the total is the number that should drive the cumulative budget when it is available.
| Example session | Prompt tokens | Completion tokens | Total tokens |
|---|---|---|---|
| 1 | 10,520 | 2,041 | 12,561 |
| 2 | 10,830 | 1,877 | 12,707 |
These two rows are the sample log reported in Yang’s 2026 article as a real-session example on a free server. They are not an independent reproduction and should not be treated as the typical cost of every edit.
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import json
used = 0
with open("usage.jsonl", encoding="utf-8") as file:
for line in file:
row = json.loads(line)
if row.get("total_tokens") is not None:
used += row["total_tokens"]
remaining = 10_000_000 - used
print({"used": used, "remaining": remaining})For the two sample rows, the cumulative total is 25,268 tokens. That number is a demonstration of addition, not a forecast. If a response has no usage metadata, keep it in a separate “unknown” bucket and use your local estimate only as a provisional reservation.
How can I reduce the tokens consumed by each edit?
Reduce the amount of context and output the model must process, and remove duplicate calls. These controls lower or bound usage, but the source article supplies no controlled percentage savings, so do not promise a fixed reduction.
Send the smallest useful code slice
Start with the changed function, its direct imports, the relevant test, and the failure message. Add another file only when the model needs it to resolve a dependency or interface. A whole repository dump makes every subsequent turn expensive and can bury the behavior you want changed.
Limit diff context
When a unified diff is sufficient, generate a narrower one. The article gives this example:
git diff --unified=5 -- path/to/file.pyThe value 5 means five surrounding lines in a standard Git diff. Choose a context size that preserves the surrounding control flow; fewer lines are not automatically better if the model then needs several clarification calls.
Cap completion length
Set the provider’s maximum output-token parameter when the task has a bounded answer, such as a patch or a short diagnosis. A cap prevents an unexpectedly long explanation, but an overly small cap can truncate code or force a retry. Record the cap with the request so a later comparison explains changes in completion size.
Avoid unnecessary parallel agents
Parallel agents multiply prompt and completion traffic and may contend for a shared server’s rate limit. Use one focused call for a small edit; parallelize only when the independent work justifies the additional budget and the provider permits the concurrency.
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Check for prompt caching
Some providers cache repeated system instructions or prefixes. If yours supports caching, determine which tokens are discounted or excluded from the allowance and whether the cache has a lifetime or minimum size. Caching can reduce repeated cost, but the cited material provides no fixed savings figure and does not make a large context free.
How do I handle retries, streaming, and failed calls?
Give every attempt its own record and link retries to the original task. A timeout may have consumed tokens even if no final answer reached your application; only the provider’s terms and usage record can settle that case.
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- Retry: increment an attempt number rather than overwriting the first request.
- Streaming: accumulate usage from the final stream event if the SDK supplies it; otherwise mark the total unknown.
- Tool loop: log each model turn, because a single user-visible edit can involve several model requests.
- Rate-limit response: record the error and wait according to the provider’s guidance instead of launching an uncontrolled retry storm.
- Partial output: do not count an absent completion as zero unless the provider explicitly reports zero completion tokens.
Reserve a small portion of the budget for retries and debugging rather than allocating the entire 10 million tokens to planned edits. The reserve is a budgeting policy, not a provider rule; choose it from your own error history.
Is a free server suitable for my coding task?
Free shared capacity is reasonable for learning, one-file fixes, small reviews, and experiments when delay or eviction is acceptable. It is a poor fit when interruption, confidentiality, or predictable throughput matters.
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| Task or requirement | Why free shared capacity may fit | Why it may not fit |
|---|---|---|
| One-file bug fix | Small context and bounded output make the budget easy to observe. | A long repository history can still inflate the request. |
| Small code review | A focused diff can be processed in one or a few calls. | Large generated diffs may exhaust a quota quickly. |
| Learning and experiments | Occasional delay is usually tolerable while you learn the logging workflow. | Availability can change without notice. |
| Large multi-file refactor | Only if you can split it into independently verifiable slices. | Repeated context, retries, and coordination can burn the allowance. |
| Continuous integration | Rarely a good match for a shared free endpoint. | Rate limits, eviction, and missing service commitments can block builds. |
| Sensitive or regulated code | Only when the provider’s written terms and controls meet your requirements. | Do not assume a free endpoint has suitable retention, isolation, or contractual protection. |
Sam Yang summarizes the operational stance as: “A free server is a sandbox, not a production machine.” Treat that as the author’s conservative heuristic, not a measured service-level comparison. Before relying on any host, check its current data-handling terms, quota behavior, region, rate limits, and service commitments.
What should I compare when choosing free or paid capacity?
Compare the allowance and the failure modes, not just the advertised token number. A paid endpoint can still have rate limits, while a free endpoint can be adequate for a small personal project.
- Allowance model: Is the limit input, output, combined tokens, requests, credits, or throughput? Does it recur, expire, or apply only once?
- Usage visibility: Does each response return prompt and completion counts? Are cached tokens, tool calls, and retries represented?
- Tokenizer alignment: Can you use the provider’s tokenizer, or must you rely on an approximate local encoding?
- Reliability: Are there shared-capacity delays, eviction, concurrency caps, or an actual service-level commitment?
- Task fit: Can the service handle your largest context and expected agent loop without forcing repeated retries?
- Data terms: May prompts or code be retained, reviewed, or used for improvement? Is your intended use permitted?
- Cost predictability: What happens at exhaustion? Does work stop, downgrade, queue, or incur paid overage, and can you set a hard spending limit?
Third-party free-tier lists can illustrate how different these offers are, but their dated figures are approximate and are not a substitute for the provider’s current documentation. Record the date and geography of any allowance you rely on.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I build a repeatable metering workflow?
Use the same sequence for every agent integration so that one project’s numbers are comparable with another’s.
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- Choose a warning threshold. For example, pause new work when the log shows that only a small reserve remains. The threshold is your policy, not a universal safe percentage.
- Estimate before the call. Tokenize the complete payload you can see and label what was excluded.
- Send one focused request. Keep the diff and task bounded; cap output where the API supports it.
- Capture returned usage. Store prompt, completion, and total fields exactly as exposed, plus status and attempt identifiers.
- Reconcile. Compare local estimate with provider prompt usage, investigate large gaps, and use returned total for the cumulative sum.
- Adjust the next task. Remove irrelevant files, shorten history, split a refactor, or move to capacity with a suitable limit.
- Review at the end of a session. Calculate average total per successful edit, average retries, and the share of requests with unknown usage.
This loop gives you an observable feedback system instead of guessing from request count. It also exposes a common surprise: one “edit” in the user interface may represent several model turns, each with its own prompt and completion tokens.
What are the most common metering mistakes?
Confusing an estimate with an invoice
A local cl100k_base count is useful for planning but may differ from the model’s actual tokenizer and wrapper overhead. Keep both values and label them separately.
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Counting only completion tokens
Long source context and repeated history often dominate the request. Budget prompt and completion tokens together when the provider reports a combined total.
Ignoring agent-generated traffic
Tests, shell commands, file searches, and tool results can trigger additional model turns. Meter at the response boundary, not only when a human types a message.
Assuming a free quota is permanent
Free offers change, expire, or move from tokens to credits and rate limits. Date your configuration and recheck the official terms before starting a long project.
Writing sensitive content into a convenient log
Usage accounting rarely requires the prompt body. Store identifiers and counts, protect the file, and define a retention period appropriate to the code involved.
What is the practical verdict on a 10-million-token plan?
It is a useful sandbox budget when you can keep edits focused, inspect usage metadata, and tolerate shared-server variability. It is not evidence that a provider will deliver 800 dependable production edits, because that figure assumes a 12,000-token average and excludes changes in context size, retries, model accounting, and availability.
Start with a small log, calibrate the preflight estimate against real responses, and let measured cumulative usage determine how large the next task should be. Move work to a service with explicit limits and data terms when interruption or confidentiality costs more than the free capacity saves.
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Do tool calls count if the model’s final answer is short?
Usually, the text sent in tool results and the additional model turns contribute to usage even when the visible final answer is brief. The exact treatment of tool traffic varies, so inspect the provider’s returned usage and record each turn separately.
What should I do when an API returns no usage object?
Keep the request in an unknown-usage bucket and retain your local estimate as a provisional value. Do not silently record zero; ask whether the SDK supports a usage option, a final stream event, or a provider-side usage export.
Can I safely store the full prompt to debug token differences?
Only if your data policy permits retaining source and prompts. A safer first pass is to store lengths, hashes, model identifiers, and estimate scope, then capture a redacted payload briefly when a discrepancy requires investigation.
Why did two edits with similar diffs use very different totals?
Hidden or repeated context can dominate a request: conversation history, system instructions, repository maps, tool output, and retries may differ even when the visible diff does not. Compare serialized request scope and per-attempt records before changing the tokenizer.
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