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Balance and deductions

LLMTune uses a prepaid balance (your “wallet”), measured in USD. You add funds up front, and usage outside your Coding Plan quota — inference on non-plan models, embeddings, fine-tuning, and Agent requests — is deducted from your wallet in real time. There are no end-of-cycle invoices; you only pay for what you use.
If you subscribe to a Coding Plan, requests against your plan’s included GLM models consume your request quota, not your wallet. Your wallet is only charged for usage outside the plan — non-plan models, fine-tuning, and embeddings. See Deposit funds for the deposit flow.
Because billing is prepaid, a request only runs if your balance can cover it. If it can’t, you get a 402 Payment Required instead of the response — see What happens when balance runs out.

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How balance is used

WorkloadHow it charges
Inference (/v1/chat/completions)Per request, based on input + output tokens.
Embeddings (/v1/embeddings)Per request, based on input tokens.
Fine-tuning (/api/training)Per GPU hour, billed while the job runs (not while queued).
Agent (/api/agent/v1/...)Per request / per step, like inference.
To add funds, see Deposit funds. To track what’s been spent, see Billing & usage.

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How deductions work

  1. When you call inference, embeddings, or the Agent, the platform:
    • Validates your API key.
    • Checks that your balance is sufficient (using an estimate or actual cost).
    • If sufficient — runs the request, computes usage (tokens), and deducts the cost from your balance.
    • If insufficient — returns 402 Payment Required and does not run the request or deduct anything.
  2. For fine-tuning, the job is billed per GPU hour while it runs. Insufficient balance can prevent the job from starting or cause it to fail — so fund the account before launching large jobs.
  3. Failed requests are not charged. If a request errors (4xx/5xx), no tokens are billed. Only successful completions are deducted.
Every request outside your Coding Plan quota — including free / Confidential (own-your-data) models, fine-tuning, embeddings, and non-plan models — is checked against your wallet balance first. A $0.00 balance returns 402 regardless of model tier. Requests against your plan’s included models consume quota, not wallet. See Coding Plans.

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What happens when balance runs out

When your balance can’t cover a request, the API returns 402 Payment Required with this body: 
{
  "error": "Insufficient balance",
  "message": "Insufficient balance. Required: $0.0012, Available: $0.0000"
}
  • Meaning — Your balance is too low for this request (or its estimated cost).
  • What to do — Add funds in the dashboard at Deposit funds, then retry. In your app, surface a clear message (e.g. “Add funds”) and link to billing.

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Handling 402 in code

Treat 402 as a billing issue, not a transient failure. Don’t retry the same request in a loop — it will keep failing until the balance is topped up. 
from openai import OpenAI, APIStatusError

client = OpenAI(base_url="https://api.llmtune.io/v1", api_key="sk_YOUR_API_KEY")

try:
    response = client.chat.completions.create(
        model="z-ai/GLM-5.2",
        messages=[{"role": "user", "content": "Hello"}],
    )
except APIStatusError as e:
    if e.status_code == 402:
        # Stop and ask the user to add funds — don't auto-retry.
        print("Balance too low. Please add funds at https://llmtune.io/billing.")
    else:
        raise

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Best practices

  • Monitor balance — Track spend on the Usage dashboard and top up before it hits zero.
  • Handle 402 explicitly — Catch it in code, prompt the user to add funds, and do not retry until balance is updated.
  • Estimate before batch — For large batch or fine-tuning jobs, check balance first so you don’t start work that will fail mid-way.
  • Fund before fine-tuning — Training is billed per GPU hour; ensure enough balance for the whole run.
  • Watch for unexpected drain — A sudden balance drop often means a loop or runaway job. Check audit logs and usage by model/endpoint.

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