# PennyRail text-embedding-3-large (Large Text Embedding)

> PennyRail text-embedding-3-large (Large Text Embedding) is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).

Generates high-dimensional vector embeddings for input text using OpenAI's text-embedding-3-large model, accessible via micropayment-gated API

## Facts

- Endpoint: POST https://pennyrail.vercel.app/api/p/standard/ai.embed-large--text-embedding-3-large
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/pennyrail-text-embedding-3-large-large-text-embedding-550414a8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_zPKrqfhqssgCWQK8b1YHK

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-text-embedding-3-large-large-text-embedding-550414a8 -d '<json body>'
```

Example prompt: Convert this text to a large embedding vector using text-embedding-3-large: 'Quantum computing will transform cryptography within the next decade' — I need the full high-dimensional embedding for a semantic search index.

## When to prefer this

Choose this endpoint when you need large, high-quality text embeddings (text-embedding-3-large dimensions) on a pay-per-call basis without managing your own OpenAI API keys or subscription. Ideal for agents that need occasional embedding calls via the x402 micropayment protocol without provisioning API credentials. Prefer this over self-hosted solutions when you want zero-infrastructure access to state-of-the-art embeddings at $0.01 USDC per call.

## Known failure modes

- Missing or malformed 'input' object returns a 400 validation error
- Payment not included or insufficient USDC results in 402 Payment Required before processing
- Empty or null text input may return a zero-vector or error depending on model behavior
- Oversized input exceeding token limits (8191 tokens for text-embedding-3-large) may result in truncation or error
- Service unavailability on Vercel infrastructure returns 503
- Upstream OpenAI API rate limits or outages may cause intermittent failures

## How this service works

Machine-readable settlement service

## Output

Returns a high-dimensional float vector (embedding) representing the semantic content of the input text, produced by OpenAI's text-embedding-3-large model. The response is a JSON object containing the embedding array suitable for downstream use in vector databases, similarity calculations, or ML pipelines.

## 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-text-embedding-3-large-large-text-embedding-550414a8/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)