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MA CHAIN · 20260131
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Mistral: Mistral Embed 2312

Origin
mistralai/mistral-embed-2312

Mistral Embed is a specialized embedding model for text data, optimized for semantic search and RAG applications. Developed by Mistral AI in late 2023, it produces 1024-dimensional vectors that effectively...

Pricing
Input · 0.12 MA / 1M tokens

Billing follows the provider's own reported cost. The unit prices here are for reference; a model marked "billed at actual usage" has no reliable pre-call price, so your usage page is the record of what you paid.

Call it

Embedding models are served on POST /v1/embeddings, with the same request and response shape as OpenAI's Embeddings API. input is a string or an array of strings, and each input gets one vector back.

curl https://macdecloud.com/v1/embeddings \
  -H "Authorization: Bearer dcld-sk-..." \
  -H "Content-Type: application/json" \
  -d '{"model":"mistralai/mistral-embed-2312","input":["first text","second text"]}'

Python (OpenAI SDK)

The official OpenAI SDK works as is; just point base_url at deCloud.

from openai import OpenAI

client = OpenAI(base_url="https://macdecloud.com/v1", api_key="dcld-sk-...")
resp = client.embeddings.create(model="mistralai/mistral-embed-2312", input=["first text", "second text"])
print(len(resp.data), len(resp.data[0].embedding))
Mistral: Mistral Embed 2312 (ORIGIN) · deCloud