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Mistral Embed

Mistral Embed is Mistral AI's general-purpose text embedding model with 1024 dimensions, designed for semantic search and retrieval tasks with a 55.26 score on the MTEB benchmark.

index.ts
import { embed } from 'ai';


const result = await embed({
  model: 'mistral/mistral-embed',
  value: 'Sunny day at the beach',
})
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Frequently Asked Questions

  • What are the embedding dimensions for Mistral Embed?

    1024 dimensions per embedding vector.

  • What is the MTEB score for Mistral Embed?

    55.26 on retrieval tasks within the Massive Text Embedding Benchmark.

  • What is Mistral Embed designed to optimize for?

    Retrieval. The embedding space supports accurate nearest-neighbor search for semantic retrieval tasks.

  • Can I use Mistral Embed for non-English text?

    Yes. Mistral's models broadly support European languages; check Mistral AI's documentation for specific language coverage in the embedding model.

  • How does Mistral Embed compare to Codestral Embed?

    Mistral Embed is a general-purpose text embedding model. Codestral Embed was trained specifically for code and outperforms general models on code retrieval benchmarks. Use Codestral Embed when your corpus is source code.

  • Can I use Mistral Embed in a RAG pipeline with a Mistral AI generation model?

    Yes. Pairing Mistral Embed for indexing with a Mistral AI instruct or reasoning model for generation is a well-supported pattern.

  • Is Mistral Embed available via the OpenAI Embeddings API format?

    Yes. Access Mistral Embed through AI Gateway; see AI Gateway docs for the embedding API surface and request shape.