Mistral: Mistral Embed 2312

Mistral Embed 2312 is an embedding model from Mistral, listed on api14 under the model id mistralai/mistral-embed-2312. It sits in the embeddings category, which means its job is to turn text into vectors rather than to generate chat replies. Mistral AI developed it as a specialized model for text data, and the provider release date recorded for it is 2025-10-31.

The model takes text as its only input and returns embeddings as its only output. It produces 1024-dimensional vectors, which is the representation you store and compare when you need similarity between pieces of text. The context window is 8192 tokens, and the maximum output is listed as 6553. There is no chat completion behaviour here, no tool calling and no image or audio handling, so you should treat it purely as a text to vector component in a larger pipeline.

Those capabilities point to a fairly specific set of uses. Semantic search is the obvious one: embed your documents once, embed the query at request time, and rank by vector similarity instead of keyword overlap. Retrieval augmented generation is the other main fit, where the embeddings feed a retriever that supplies relevant passages to a separate generation model. Because the model was optimized for semantic search and RAG, it also suits clustering, deduplication and classification tasks where you need a fixed numeric representation of text rather than a written answer.

You call it through api14's OpenAI-compatible API using the model id mistralai/mistral-embed-2312, so existing OpenAI client libraries work with only the base URL and key changed. The embeddings endpoint is the one to use, and you send your text in the input field. Note that the supported parameters list for this model is empty, so do not rely on extra tuning options such as dimensions or encoding format being accepted; send the model id and the input, and read the vector back from the response.

One practical limit to keep in mind is the 8192 token context. Very long documents need to be split into chunks before embedding, and chunking strategy will affect retrieval quality more than the model choice will. Also, since this model only produces embeddings, it cannot answer questions or write text on its own. If your task is generation, summarisation or conversation rather than retrieval, a chat or instruction model is the better fit, and Mistral Embed 2312 belongs earlier in the pipeline where text is converted into something searchable.