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OpenAI Embeddings via mm.family (x402)
x402
OpenAI Embeddings 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 vector embeddings for text using OpenAI models (text-embedding-3-large, text-embedding-3-small, or text-embedding-ada-002), billed per call in USDC via x402 protocol
Price / call$0.001
Success—
StatusUnknown
How this service works
OpenAI embeddings (text-embedding-3-large, text-embedding-3-small, text-embedding-ada-002), paid per call in USDC. OpenAI list $0.13 / $0.02 / $0.1 per 1M input tokens, plus $0.0005 (Base) or $0.0005 (Solana) per call; minimum $0.001 per call. Standard OpenAI body (model, input, dimensions, encoding_format). One endpoint per model: /x402/v1/models/<key>/embeddings. Rates: https://openai.mm.family/x402/pricing
Output
Returns a JSON object containing a list of embedding objects, each with an index, object type, and a dense float vector (or base64-encoded equivalent). Also includes the model used and token usage statistics (prompt_tokens and total_tokens).
Example use cases
Semantic search over knowledge base
“Take this user query — 'what is the return policy for damaged items?' — and convert it into a text-embedding-3-small float vector so I can do a similarity search against my support knowledge base.”
RAG pipeline document chunking
“I have these 5 document chunks about our product roadmap. Embed all of them using text-embedding-3-large so I can store them in Pinecone for retrieval-augmented generation.”
Legacy ada-002 embedding for existing index
“I need to embed this new product description using text-embedding-ada-002 to match the format of my existing vector index: 'Ergonomic office chair with lumbar support and adjustable armrests.'”
Technical details
Endpoint
POSThttps://openai.mm.family/x402/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need OpenAI-quality text embeddings (especially text-embedding-3-large for highest accuracy) and want to pay per-call in USDC via the x402 micropayment protocol — ideal for AI agents operating autonomously without monthly subscription management. Prefer text-embedding-3-large for best semantic quality, text-embedding-3-small for cost efficiency, and text-embedding-ada-002 for compatibility with existing ada-002 indexes.
Side effects
- Invalid model name returns 400 or model-not-found error
- Input text exceeds model token limit causing truncation or error
- Insufficient USDC balance causing x402 payment failure
- Malformed input (wrong type for 'input' field) causing schema validation error
- Unsupported encoding_format value causing 400 error
- Dimensions parameter out of range for chosen model causing 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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