OpenAI text-embedding-ada-002 Embeddings (x402/USDC)

x402

OpenAI text-embedding-ada-002 Embeddings (x402/USDC) 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's text-embedding-ada-002 model, billed per call in USDC via the x402 payment protocol.

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

text-embedding-ada-002: OpenAI Text Embedding Ada 002 embeddings, paid per call in USDC. OpenAI list $0.1 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 a list of embedding objects, each with an integer index, object type 'embedding', and a float array (or base64 string) representing the vector. Also includes the model name used, total token count, and prompt token count consumed.

Example use cases

Semantic search index population

“Convert these 50 product descriptions into text-embedding-ada-002 float vectors so I can index them in Pinecone for semantic search.”

Duplicate document detection

“Embed both of these support ticket texts using ada-002 and give me their float vectors — I want to compare cosine similarity to detect if they're duplicates.”

RAG query vectorization

“Turn this user question — 'What is the refund policy for annual subscriptions?' — into an ada-002 embedding so I can retrieve the closest matching chunks from my knowledge base.”

Technical details

Endpoint

POSThttps://openai.mm.family/x402/v1/models/text-embedding-ada-002/embeddings?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need OpenAI's text-embedding-ada-002 embeddings specifically and want to pay per call in USDC via x402 micropayments without managing an OpenAI API key or subscription. Ideal for agents operating in crypto-native or decentralized payment environments, or for serverless workloads that benefit from pay-per-use pricing. Prefer when ada-002 compatibility is required for consistency with an existing vector index trained on ada-002 embeddings.

Side effects

Input exceeds 8192 token limit — request rejected
Malformed input (non-string, non-array) — schema validation error
Insufficient USDC balance for x402 payment — payment failure
Invalid encoding_format value outside enum — request rejected
Rate limiting or server-side quota exceeded — 429/503 response
Model field set to unsupported model name — validation 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-ada-002",
    "usage": {
      "total_tokens": 1,
      "prompt_tokens": 1
    },
    "object": "list"
  }
}

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