# Mistral Embed

> 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)

## Facts

- Endpoint: POST https://mistral.mpp.paywithlocus.com/mistral/embed?utm_source=zero.xyz
- Price: $0.008/call
- Payment: MPP
- Status: unknown
- Last checked: 2026-10-13
- Activations on Zero: 0
- Provider: Mistral AI
- Docs: https://docs.mistral.ai
- Website: https://mistral.ai
- Tags: llm, chat, embeddings, code, reasoning, ai
- Canonical page: https://www.zero.xyz/c/mistral-ai-embed-ffdfb237
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LyChPY4XTm5Zp6a9wXCwS

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability mistral-ai-embed-ffdfb237 -d '<json body>'
```

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

## When to prefer this

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.

## Known failure modes

- 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

## 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 request

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

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "required": [
  "model",
  "input"
 ],
 "properties": {
  "input": {
   "anyOf": [
    {
     "type": "string"
    },
    {
     "type": "array",
     "items": {}
    }
   ],
   "description": "Text(s) to embed (string or array of strings)"
  },
  "model": {
   "type": "string",
   "description": "Embedding model (e.g. \"mistral-embed\", \"codestral-embed\")"
  },
  "encoding_format": {
   "type": "string",
   "description": "Output format: \"float\" (default) or \"base64\""
  }
 },
 "additionalProperties": true
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/mistral-ai-embed-ffdfb237/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from mistral.mpp.paywithlocus.com](https://www.zero.xyz/host/mistral.mpp.paywithlocus.com/llms.txt)