Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimen
Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimensions). Higher accuracy than the small model. Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.
Answeringour last check, 2026-09-24
1 of 1checks answered this week
919 msmedian answer time
$0.01listed price per call
$0.01price it asked us
Paid test badge: not yet. The checks above are free: we call the tool without paying and read the payment request it sends back. The Verified badge needs paid calls whose answers match the promised output, and nobody can buy a badge.
Endpoint
POST https://agent402.tools/api/embed-large
| Category | Search and research |
|---|---|
| Provider host | agent402.tools |
| Networks | algorand:wGHE2Pwdvd7S12BL5FaOP20EGYesN73ktiC1qzkkit8=, eip155:10, eip155:1329, eip155:137, eip155:143, eip155:42161, eip155:42220, eip155:43114, eip155:4663, eip155:8453, solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp, stellar:pubnet |
| Payment schemes | exact, upto |
| Self-reported calls, 30 days | 3 from 1 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-09-24 | valid payment request | 402 | $0.01 | 919 ms |
Example input (from the provider)
{
"body": {
"text": "Agent402 is an open-source x402 tool server."
},
"bodyType": "json",
"method": "POST",
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"text": {
"description": "Text to embed (max 32,000 chars)",
"type": "string"
}
},
"required": [
"text"
]
},
"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": {
"properties": {
"example": {
"properties": {
"dimensions": {
"type": "integer"
},
"embedding": {
"items": {
"type": "number"
},
"type": "array"
},
"model": {
"type": "string"
},
"provider": {
"type": "string"
},
"usage": {
"properties": {
"total_tokens": {
"type": "integer"
}
},
"type": "object"
}
},
"required": [
"model",
"provider",
"embedding",
"dimensions",
"usage"
],
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}