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Delx Token Budget Split
x402
Delx Token Budget Split is a paid API for AI agents from api.delx.ai, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-13).
Splits a model's total context window token budget into system-reserved, output-reserved, and available input token counts
Price / call$0.003
Success—
StatusUnknown
How this service works
Split a model token budget into system, output reserve, and available input — call when reserving system and output tokens from a context window. Use for SRE/ops math agents buy from broad utility catalogs: SLA, error budget, success rate, and health scores with zero upstream cost. Returns deterministic machine-readable JSON for $0.003 USDC via x402 on Base. Execution is first-party, local-only, stateless, and memory-only with no paid upstream, no input retention, and no claim of live chain tip…
Output
A deterministic JSON object showing the breakdown of the token budget: the system token allocation, the reserved output token count, and the remaining available input tokens calculated as total minus system minus reserved output.
Example use cases
Prompt engineer token headroom check
“I'm building a prompt for a 32,768-token model. My system instructions take up 1,500 tokens and I want to reserve 2,048 tokens for the output — how many tokens do I have left for the actual user message?”
LLM agent context window planning
“My AI agent uses a 128k context window. I need 4,000 tokens for the system prompt and 8,192 for the response. Can you tell me exactly how many tokens are available for the conversation history and input?”
Ops agent dynamic budget allocation
“Before I send a batch of documents to the model, calculate my token budget: the context window is 16,384 tokens, system prompt uses 800 tokens, and I'm reserving 1,024 tokens for output. What's the max input size I can use?”
Technical details
Endpoint
POSThttps://api.delx.ai/api/v1/x402/token-budget-splitWhen to prefer
Use this endpoint when you need a fast, deterministic, zero-dependency computation to partition a model's context window into its three standard buckets (system, output reserve, available input). Prefer it over manual math in agent code when you want a machine-readable JSON result that can be passed directly to downstream prompt-construction logic. It is especially useful in SRE/ops agents and prompt-engineering pipelines where consistent token budget accounting is needed across many calls at near-zero cost.
Side effects
- Missing required fields (total_tokens, system_tokens, or reserved_output_tokens) result in a validation error
- If system_tokens plus reserved_output_tokens exceeds total_tokens, the available input tokens will be zero or negative — the API may return an error or a zero/negative value
- Payment failure or insufficient USDC balance via x402 protocol results in a 402 response before computation
- Non-numeric or malformed token values result in schema validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| total_tokens | number | Model context window size in tokens. |
| system_tokens | number | Tokens already reserved for system/instructions. |
| reserved_output_tokens | number | Tokens reserved for model output. |
Output
Schema · Response
{
"type": "json",
"example": {
"schema": "delx/util-token-budget-split/v1",
"feasible": true,
"total_tokens": 8000,
"system_tokens": 500,
"available_input_tokens": 6500,
"reserved_output_tokens": 1000
}
}Tags
x402
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