{"uid":"cap_m7e9FVUzydNrJ-zlPIu50","slug":"openai-embeddings-via-mm-family-x402-f4bf639d","name":"OpenAI Embeddings via mm.family (x402)","description":"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","url":"https://openai.mm.family/x402/v1/embeddings?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"type":["string","array"]},"model":{"enum":["text-embedding-3-large","text-embedding-3-small","text-embedding-ada-002"],"type":"string"},"dimensions":{"type":"integer"},"encoding_format":{"enum":["float","base64"],"type":"string"}}},"responseSchema":{"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"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_905_BUhPWR8MVJayV1jZ4","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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","exampleAgentPrompt":"Embed this text using text-embedding-3-large and return a float vector: 'The quick brown fox jumps over the lazy dog'","exampleUseCases":[{"title":"Semantic search over knowledge base","prompt":"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."},{"title":"RAG pipeline document chunking","prompt":"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."},{"title":"Legacy ada-002 embedding for existing index","prompt":"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.'"}],"resultDescription":"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).","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-02T12:42:58.668Z","isFirstParty":false,"canonicalSlug":"openai-embeddings-via-mm-family-x402-f4bf639d"}