Mistral Embed

llmchatembeddingscodereasoningai

Mistral Embed is a paid API for AI agents from Mistral AI at mistral.mpp.paywithlocus.com, paid per call via MPP, $0.008/call, status unknown (last checked 2026-10-13).

Generates vector embeddings for text using Mistral's embedding models (mistral-embed, codestral-embed)

Price / call$0.008

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StatusUnknown

How this service works

Generate text or code embeddings. Returns 1024-dimensional vectors for retrieval, clustering, and classification.

Output

Returns a high-dimensional float (or base64) vector embedding representing the semantic content of the input text, suitable for similarity search, clustering, or use in a vector database.

Example use cases

Example prompt

“Use Mistral's mistral-embed model to generate a float vector embedding for this text: 'artificial intelligence and machine learning'.”

Technical details

Endpoint

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

When to prefer

Choose this endpoint when you need high-quality semantic text embeddings from Mistral's models (mistral-embed for general text, codestral-embed for code), especially when already using Mistral's LLM ecosystem or when integrating into a RAG pipeline, semantic search, or vector database workflow that benefits from consistent embedding quality.

Side effects

Invalid or unsupported model name returns 400 error
Empty or missing input field returns 422 validation error
Input text exceeds model's token limit returns 400 or truncation error
Invalid encoding_format value returns 400 error
Authentication or payment failure returns 402 or 401 error
Service unavailable or timeout returns 503 error

Input

{
  "input": "artificial intelligence and machine learning",
  "model": "mistral-embed",
  "encoding_format": "float"
}

Schema · Request body

Field	Type	Description
inputrequired	—	Text(s) to embed (string or array of strings)
modelrequired	string	Embedding model (e.g. "mistral-embed", "codestral-embed")
encoding_format	string	Output format: "float" (default) or "base64"

Output

{
  "_truncated": true,
  "_originalSize": 19749
}

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How this service works

Generate text or code embeddings. Returns 1024-dimensional vectors for retrieval, clustering, and classification.

Output

Returns a high-dimensional float (or base64) vector embedding representing the semantic content of the input text, suitable for similarity search, clustering, or use in a vector database.

Technical details

Endpoint

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

When to prefer

Side effects

Invalid or unsupported model name returns 400 error
Empty or missing input field returns 422 validation error
Input text exceeds model's token limit returns 400 or truncation error
Invalid encoding_format value returns 400 error
Authentication or payment failure returns 402 or 401 error
Service unavailable or timeout returns 503 error

Input

{
  "input": "artificial intelligence and machine learning",
  "model": "mistral-embed",
  "encoding_format": "float"
}

Schema · Request body

Field	Type	Description
inputrequired	—	Text(s) to embed (string or array of strings)
modelrequired	string	Embedding model (e.g. "mistral-embed", "codestral-embed")
encoding_format	string	Output format: "float" (default) or "base64"

Output

{
  "_truncated": true,
  "_originalSize": 19749
}