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Api Endpoint

AgentUtility.ai

Embeds 1 to 100 strings into semantic vectors via Venice. Tier shorthand: 'default' → gemini-embedding-2-preview (newest, recommended), 'fast' → text-embedding-bge-m3, 'openai-compat' → text-embedding-3-small. You can also pass a full Venice embedding model name. Returns a list of vectors aligned with input order. Use it for text embedding, vector embedding, Venice embeddings, Gemini embeddings, or BGE-M3.

Open interactive test console

Offering

Provider
x402.agentutility.ai
Method
POST
Offering type
Api Endpoint
Transport type
http
Category
Development
Delivery
Not provided
Pricing model
Not provided
x402 version
2

Endpoint context

URL
https://x402.agentutility.ai/text-embedding
MIME type
Not provided
First seen
2026-10-16T01:50:32.458Z
Last seen
Tags
wordmint, Text, embedding

Website

Payment pricing

x402 payment terms are the authoritative price for calling this endpoint.

Payment option 1
0.005 USDC
Atomic price: 5000 · Network: eip155:8453 · Asset: USDC · Scheme: exact
Payment option 2
0.005 USDC
Atomic price: 5000 · Network: solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp · Asset: USDC · Scheme: exact
Raw x402 terms
[
  {
    "scheme": "exact",
    "network": "eip155:8453",
    "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
    "amount": "5000",
    "payTo": "0x6c0752c09e7F6fA6526fCDf40e456a159ebB5621"
  },
  {
    "scheme": "exact",
    "network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
    "asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
    "amount": "5000",
    "payTo": "FJqAJ4dXkFbrp3EEo8E9x98iaBGUQwaU8Srz5ePfUXLH"
  }
]

Observed health

Status
Healthy
Latest latency
3936 ms
Average latency
4019 ms
HTTP status
204
Last checked
Reachability
85.7% over 14 checks

Latency is measured to the first response headers. A reachable result does not verify response accuracy or merchant identity.

Input schema

{
  "additionalProperties": false,
  "properties": {
    "body": {
      "properties": {
        "model": {
          "description": "Tier shorthand ('default'|'fast'|'openai-compat') or full Venice embedding model name. Default 'default'.",
          "type": "string"
        },
        "texts": {
          "description": "1 to 100 strings to embed; each up to 30,000 chars.",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "texts"
      ]
    },
    "bodyType": {
      "enum": [
        "json",
        "form-data",
        "text"
      ],
      "type": "string"
    },
    "method": {
      "enum": [
        "POST"
      ],
      "type": "string"
    },
    "type": {
      "const": "http",
      "type": "string"
    }
  },
  "required": [
    "type",
    "method",
    "bodyType",
    "body"
  ],
  "type": "object"
}

Output schema

{
  "properties": {
    "example": {
      "properties": {
        "count": {
          "type": "integer"
        },
        "dimensions": {
          "type": "integer"
        },
        "embeddings": {
          "items": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "type": "array"
        },
        "model": {
          "type": "string"
        },
        "source": {
          "type": "string"
        },
        "tier": {
          "type": "string"
        },
        "usage": {
          "properties": {
            "prompt_tokens": {
              "type": "integer"
            },
            "total_tokens": {
              "type": "integer"
            }
          },
          "type": "object"
        }
      },
      "type": "object"
    },
    "type": {
      "type": "string"
    }
  },
  "required": [
    "type"
  ],
  "type": "object"
}