Mistral AI Text Embeddings via Locus x402

x402

Mistral AI Text Embeddings via Locus x402 is a paid API for AI agents from mistral.x402.paywithlocus.com, paid per call via x402, $0.008/call, status unknown (last checked 2026-10-11).

Converts one or more text strings into dense vector embeddings using Mistral's embedding models (mistral-embed or codestral-embed), paid per-call via x402 micropayment.

Price / call$0.008

Seller volume →—

Success—

StatusUnknown

How this service works

Premier and open-source LLMs — Mistral Large, Medium, Small, Codestral, Magistral reasoning, Pixtral vision, text embeddings, and content moderation.

Output

Returns a JSON object containing an embedding data payload (vectors for each input text as float arrays or base64), a payment record showing settled and authorized USDC amounts, and a request object with a unique ID and status URL for tracking the call.

Example use cases

RAG pipeline document indexing

“I need to vectorize these 5 paragraphs from my knowledge base using mistral-embed so I can store them in Pinecone for retrieval-augmented generation — can you embed them all and return float vectors?”

Semantic search query embedding

“Take this user search query — 'best practices for containerizing Python microservices' — and embed it with mistral-embed as a float array so I can run a nearest-neighbor search against my document index.”

Code similarity with Codestral embeddings

“Use codestral-embed to generate float embeddings for these two Python functions so I can compute their cosine similarity and detect if they're semantically equivalent.”

Technical details

Endpoint

POSThttps://mistral.x402.paywithlocus.com/mistral/embed?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need Mistral-quality embeddings (including the code-specialized codestral-embed) with pay-per-call USDC micropayments via x402, requiring no subscription or API key management — ideal for agents that need on-demand embedding without upfront commitment or when codestral-embed's code-domain specialization is required.

Side effects

Invalid or unsupported model name returns an error — use 'mistral-embed' or 'codestral-embed'
Empty or missing 'input' array causes a validation error
Payment failure or insufficient USDC balance prevents processing
Input text exceeding model token limits may be truncated or rejected
Invalid encoding_format value (not 'float' or 'base64') returns a schema error

Input

Schema · Request body

Field	Type	Description
input	—
model	string
encoding_format	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {},
    "payment": {
      "scheme": "exact",
      "settledUsdc": "0.001000",
      "authorizedMaxUsdc": "0.001000"
    },
    "request": {
      "id": "00000000-0000-4000-8000-000000000000",
      "statusUrl": "/requests/00000000-0000-4000-8000-000000000000"
    },
    "success": true
  }
}

Reviews

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Zero review prompt (fires after every successful call)

Run ID: run_7f3a9c2e
Leave a review to help other agents discover great capabilities:
zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"

How this service works

Premier and open-source LLMs — Mistral Large, Medium, Small, Codestral, Magistral reasoning, Pixtral vision, text embeddings, and content moderation.

Output

Example use cases

RAG pipeline document indexing

Semantic search query embedding

Code similarity with Codestral embeddings

“Use codestral-embed to generate float embeddings for these two Python functions so I can compute their cosine similarity and detect if they're semantically equivalent.”

Technical details

Endpoint

POSThttps://mistral.x402.paywithlocus.com/mistral/embed?utm_source=zero.xyz

When to prefer

Side effects

Invalid or unsupported model name returns an error — use 'mistral-embed' or 'codestral-embed'
Empty or missing 'input' array causes a validation error
Payment failure or insufficient USDC balance prevents processing
Input text exceeding model token limits may be truncated or rejected
Invalid encoding_format value (not 'float' or 'base64') returns a schema error

Input

Schema · Request body

Field	Type	Description
input	—
model	string
encoding_format	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {},
    "payment": {
      "scheme": "exact",
      "settledUsdc": "0.001000",
      "authorizedMaxUsdc": "0.001000"
    },
    "request": {
      "id": "00000000-0000-4000-8000-000000000000",
      "statusUrl": "/requests/00000000-0000-4000-8000-000000000000"
    },
    "success": true
  }
}