Venice AI Embeddings

x402

Venice AI Embeddings is a paid API for AI agents from api.venice.ai, paid per call via x402, $10/call, status unknown (last checked 2026-10-11).

Converts text or token arrays into vector embeddings using a choice of embedding models, OpenAI-compatible API.

Price / call$10

Seller volume →—

Success—

StatusUnknown

How this service works

Generate embeddings.

Output

Returns a list of embedding vectors (arrays of floats), the model name used, and token usage (prompt_tokens and total_tokens). Each embedding object includes its index in the batch and the embedding array itself.

Example use cases

Example prompt

“Generate a float-format vector embedding for the text 'The quick brown fox jumped over the lazy dog' using the text-embedding-bge-m3 model on Venice AI.”

Technical details

Endpoint

POSThttps://api.venice.ai/api/v1/embeddings?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need OpenAI-compatible embeddings with a choice of diverse models (BGE, Qwen3, Gemini, E5, Nemotron, etc.) and want to pay per-call via USDC/x402 without a subscription. Prefer it over OpenAI's embeddings endpoint when you need multilingual models or crypto-native payment, and over self-hosted solutions when you want managed infrastructure.

Side effects

Empty string input returns validation error
Input exceeding 8192 tokens causes rejection
Array dimensions exceeding 2048 cause rejection
Invalid or unsupported model name returns error
402 Payment Required if x402 wallet balance is insufficient
Missing required fields (input or model) returns 400 error

Input

Schema · Request body

Field	Type	Description
user	string	This is an unused parameter and is discarded by Venice. It is supported solely for API compatibility with OpenAI.
inputrequired	—	Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens), cannot be an empty string, and any array must be 2048 dimensions or less.
modelrequired	—	ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.
dimensions	integer	The number of dimensions the resulting output embeddings should have.
encoding_format	string	The format to return the embeddings in. Can be either `float` or `base64`.

Output

Schema · Response

Field	Type	Description
datarequired	array	The list of embeddings generated by the model.
modelrequired	string	The name of the model used to generate the embedding.
usagerequired	object	The usage information for the request.
objectrequired	string	The object type, which is always "list"

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