# PennyRail LLM-Mini Inference Endpoint

> PennyRail LLM-Mini Inference Endpoint is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.01/call, status down (last checked 2026-11-04).

Runs a mini LLM inference job via PennyRail's pay-per-call settlement layer for $0.01 USDC per request

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/standard/ai.llm-mini--llm?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: down
- Last checked: 2026-11-04
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-llm-mini-inference-endpoint-7a01a267
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_oKeVM24agm6aBAgIPvUEe

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability pennyrail-llm-mini-inference-endpoint-7a01a267 -d '<json body>'
```

Example prompt: Run a mini LLM inference job through PennyRail — send this input object and settle the $0.01 USDC micropayment via x402: {"prompt": "Summarize the following paragraph in one sentence: ..."}

## When to prefer this

Choose this endpoint when you need extremely low-cost LLM inference ($0.01 per call) with automatic USDC micropayment settlement via the x402 protocol — ideal for high-volume agentic pipelines where per-call cost control and on-chain payment auditability matter more than model capability. Prefer this over full-sized LLM APIs for lightweight subtasks like classification, short summarization, or template filling where a mini model suffices.

## Known failure modes

- Payment failure: insufficient USDC balance or x402 settlement rejection returns a 402 Payment Required error
- Invalid input: missing required 'input' field returns a 400 Bad Request
- Model overload or timeout: mini LLM backend may return 503 or timeout under load
- Malformed input object: passing non-object types to 'input' may produce unpredictable or empty responses
- Response schema variability: since additionalProperties is unconstrained, downstream parsers must handle unknown fields gracefully

## How this service works

Machine-readable settlement service

## Output

Returns a JSON object with the LLM's inference output — typically generated text or a structured response — shaped by the input payload. The response schema is open (additionalProperties: true), so the exact fields depend on what the mini LLM model returns for the given input.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "object",
 "additionalProperties": true
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/pennyrail-llm-mini-inference-endpoint-7a01a267/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from pennyrail.vercel.app](https://www.zero.xyz/host/pennyrail.vercel.app/llms.txt)