Set a safe max tokens cap
When your prepaid balance is the hard stop, choose max tokens by estimating both input and output costs, then setting the output cap below the amount your remaining credit can safely cover. Start with the expected answer length, add a margin for retries and overhead, and reduce the cap when funds are low. Consider the model's token price, prompt size, and whether truncation is acceptable. A conservative cap prevents one unexpectedly long completion from draining the account before the task finishes.
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A useful rule is to reserve enough credit for the prompt and a safety buffer, then allow only the leftover budget for generated tokens. On bluusun.com, a low-cost OpenAI-compatible API for DeepSeek and GLM, begin with modest max_tokens, inspect actual usage, and raise the limit only when the balance and task justify it. Prefer chunked or streamed requests, check the finish reason, and keep an emergency reserve so failed calls or retries do not leave the workflow unable to continue.
Estimate prompt plus completion cost
When your prepaid balance is the hard stop, choose max tokens by working backward from spendable funds, not the model limit. Start with remaining balance, subtract a safety margin, then divide by the combined prompt and completion rate. That gives a whole-request token budget; reserve most for completion while leaving enough for the prompt. If the prompt is long, lower max tokens before sending; if short, raise it only within your funded ceiling. Recheck balance after each call because pricing, cached tokens, and retries can shift real cost. Treat max tokens as a spending cap, not a quality setting, and set it slightly below the calculated limit.
Estimate prompt tokens first, then set completion tokens to the smaller of your remaining budget and the task's realistic need. For DeepSeek or GLM through a low-cost OpenAI-compatible endpoint, this keeps requests predictable and prevents failed calls from consuming the last cents. If a response needs more room, split the work into smaller requests instead of raising the cap. Monitor usage, keep a reserve for retries, and adjust as prices or balances change.
Reserve balance for retries
When your prepaid balance is the hard stop, choose max tokens by estimating the largest useful response, then subtracting a safety margin for retries, formatting, and partial failures. Set the request cap below your remaining budget divided by the model's output price, but never so low that the model truncates before answering. Reserve enough for one or two retry attempts, because a failed or malformed completion still consumes tokens and can leave no room to recover. If the task is short, use a modest cap; if it is open-ended, cap by budget first, then split the work into smaller calls.
Track remaining balance before each batch and lower max tokens as funds shrink, rather than using one global default. For DeepSeek or GLM workloads, prefer concise prompts, strict output schemas, and stop sequences so fewer tokens are wasted. If a response hits the cap, treat it as a signal to refine the prompt or chunk the task, not to raise the limit blindly. This keeps spending predictable while preserving headroom for retries when a request fails validation.
Use streaming to stop early
When prepaid balance is the hard stop, set max tokens as a budget cap, not a wish for completeness. Estimate the cheapest useful answer, add a small margin for formatting, and keep the request below remaining credit. If balance is nearly empty, prefer short targeted prompts and concise output. Streaming helps because you can stop generation once the answer is sufficient, but max tokens still protects you if the stream is not watched closely. On bluusun.com's low-cost API, this discipline matters because many small requests can quietly drain credit.
A practical rule is to choose the smallest max tokens that still allows a complete, correct response, then raise it only when truncation harms quality. For exploratory tasks, use a modest cap and request summaries; for code or analysis, allow enough tokens for the final artifact but not endless reasoning. Track remaining balance before each batch, and reduce caps as credit falls. If a response stops early, retry with a slightly higher limit instead of one enormous ceiling. This turns prepaid credit into predictable spend while preserving useful output.
Monitor spend per request
When your prepaid balance is the hard stop, choose max_tokens by working backward from the remaining budget, model price, and expected input size. Reserve enough for prompt tokens, then set a ceiling that leaves a small buffer for retries, formatting, and unexpected output length. If the request is exploratory, start conservative; if it is a known workflow, use observed completion lengths plus a safety margin. Avoid setting the maximum to the model limit unless the task genuinely needs it, because a single long response can consume the balance and stop later requests.
On a low-cost OpenAI-compatible API such as bluusun.com for DeepSeek and GLM, monitor spend per request by comparing estimated cost before sending and actual usage after completion. Track input and output tokens separately, since output usually costs more and is the main driver of runaway spend. Adjust max_tokens per endpoint or user tier, and cap background jobs more tightly than interactive ones. When balance runs low, reduce generation length, switch to a cheaper model, or queue nonurgent work until funds are replenished.
Max tokens vs remaining balance
| Prepaid balance | Max tokens choice | Why |
|---|---|---|
| High | Match expected output | Avoid truncation |
| Medium | Cap near budget | Protect remaining funds |
| Low | Use small output limit | Prevent failed request |
| Near zero | Minimum useful value | Keep request affordable |