mistralai

Mistral: Mistral Embed 2312

mistralai/mistral-embed-2312

Our take

AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.

OpenRouter's catalog describes mistralai/mistral-embed-2312 as a Mistral AI embedding model developed in late 2023 for text data. It accepts text and returns embeddings, with a listed context length of 8,192 and 1,024-dimensional vectors. The catalog positions it for semantic search and retrieval-augmented generation; this source pack supplies catalog metadata only, not independent measurements or workload-specific evaluations for this exact model in the supplied evidence.[1]

Strengths

Catalog-reported specialization in text embeddings.[1]
Accepts text input and returns embedding vectors.[1]
Listed context length of 8,192 and 1,024-dimensional output vectors.[1]

Limitations

Only OpenRouter catalog metadata was supplied; an independent model card was not available in this source pack.[1]
No measured benchmark or workload-specific results were included, so effectiveness must be validated on the intended data.[1]

Where to use it

Consider for semantic search over text collections, subject to workload validation.[1]
Consider as the retrieval embedding component in RAG pipelines that fit the listed context length.[1]

Sources

[1] OpenRouter catalog · Mistral: Mistral Embed 2312Sources checked: Nov 13, 2026

About this model

Description below is the original text supplied by OpenRouter, shown in the source language.

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...

Specifications

Model ID: mistralai/mistral-embed-2312
Creator: mistralai
Added to OpenRouter: 31.10.2025
Context length: 8,192
Modality: text->embeddings
Input modalities: text
Output modalities: embeddings
Token pricing: USD per 1M tokens: in $0.100 / out $0
Knowledge cutoff: —
Expiration date: —

Tasks and capabilities

A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.

Task	Grade	Source-declared capability
General quality	No measurement	—
Coding	No measurement	—
Agents	No measurement	—
Writing	No measurement	—
Reasoning	No measurement	—
Document analysis	No measurement	—
Vision	No measurement	—
Image generation	No measurement	—
Audio	No measurement	—
Video	No measurement	—
Embeddings	No measurement	output: embeddings

Evaluations

No evaluations are supplied for this model.

How grades work

Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.

S — S — top 10% of the cohort
A — A — next 20% of the cohort
B — B — next 30% of the cohort
C — C — below the B band

Providers

Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.

Provider	Context length	Price in /1M	Price out /1M	Latency p50 (30m, s)	Throughput p50 (30m, tok/s)	Uptime (30m)
Mistral	8,192	$0.100	$0	—	—	100%
Mistral	8,192	$0.110	$0	—	—	100%

Provider data retrieved: 13.11.2026

No published articles identify this model yet.