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OpenAI text-embedding-3-large (via mm.family x402)
x402
OpenAI text-embedding-3-large (via mm.family x402) is a paid API for AI agents from openai.mm.family, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-02).
Generates high-dimensional vector embeddings for text using OpenAI's text-embedding-3-large model, billed per call in USDC via x402 micropayments.
Price / call$0.001
Success—
StatusUnknown
How this service works
text-embedding-3-large: OpenAI Text Embedding 3 Large embeddings, paid per call in USDC. OpenAI list $0.13 per 1M input tokens, plus $0.0005 (Base) or $0.0005 (Solana) per call; minimum $0.001 per call; up to 8192 tokens per input. Standard OpenAI body. Rates: https://openai.mm.family/x402/pricing
Output
Returns a JSON object containing an array of embedding objects, each with an index, object type 'embedding', and the embedding as a float (or base64-encoded) array. Also includes the model name used and token usage counts (prompt_tokens, total_tokens).
Example use cases
Semantic search index population
“I need to embed all these product descriptions using text-embedding-3-large so I can store them in my vector database for semantic search — can you generate float embeddings for each one?”
RAG pipeline document encoding
“For my RAG pipeline, embed this paragraph using text-embedding-3-large with 1536 dimensions so I can retrieve it later: 'Our return policy allows exchanges within 30 days of purchase.'”
Sentence similarity scoring
“Generate text-embedding-3-large embeddings for these two sentences so I can compute their cosine similarity: 'I love machine learning' and 'AI is my passion'.”
Technical details
Endpoint
POSThttps://openai.mm.family/x402/v1/models/text-embedding-3-large/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need high-quality, large-dimensional text embeddings from OpenAI's best embedding model and want to pay per call in USDC without a subscription. Prefer it over text-embedding-3-small when embedding quality and recall matter more than cost. Ideal for RAG pipelines, semantic search, document clustering, and similarity tasks requiring OpenAI's state-of-the-art embedding model with x402 micropayment support.
Side effects
- Input exceeds 8192 token limit — request rejected
- Insufficient USDC balance for x402 payment — 402 Payment Required
- Invalid model field value (must be 'text-embedding-3-large') — 400 Bad Request
- Malformed input type (not string or array) — 400 validation error
- Invalid encoding_format value — 400 Bad Request
- Network timeout or provider outage — 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | — | |
| model | string | |
| dimensions | integer | |
| encoding_format | string |
Output
Schema · Response
{
"type": "json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
0.01,
-0.02
]
}
],
"model": "text-embedding-3-large",
"usage": {
"total_tokens": 1,
"prompt_tokens": 1
},
"object": "list"
}
}Tags
x402
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