Mistral Embed
Up to 30%Embed text at scale with Mistral Embed on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.
What is Mistral Embed?
Mistral Embed is an embedding model on LLM.API (`mistral-embed`). Mistral text embedding model. Fixed 1024 dimensions; OpenAI-compatible embeddings API. It maps text into embedding vectors for retrieval, clustering, and semantic search—not for chat completions.
Providers
LLM.API routes Mistral Embed to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $100; out $0 per 1M tokens | 8K tokens | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Mistral Embed right here — free to start.
Suggestions for your first prompt
Code snippet
Call Mistral Embed through the OpenAI-compatible API — POST /v1/embeddings.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.embeddings.create(
model="mistral-embed",
input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding)){
"model": "mistral-embed",
"input": "Semantic search starts with strong embeddings."
}5 Core Capabilities
Low-latency lookups
Suitable for online retrieval when paired with a vector store. Reflects Mistral positioning for this endpoint.
Cross-document matching
Supports near-duplicate detection and topical grouping. Reflects Mistral positioning for this endpoint.
Multilingual text
Embeds content across languages depending on the underlying model family. Relevant for `mistral-embed` workloads on LLM.API.
Retrieval readiness
Fits RAG pipelines that need stable document and query embeddings. Relevant for `mistral-embed` workloads on LLM.API.
Semantic vectorization
Maps text into dense vectors for similarity search and clustering. Grounded in the model's embedding role rather than generic chat claims.
6 Most Valuable Use Cases
- Semantic search over docs, tickets, and wikis with Mistral Embed
- Lead and account similarity scoring from notes
- Topic clustering for content and support queues with Mistral Embed
- Duplicate and near-duplicate content detection
- Recommendation features based on text similarity with Mistral Embed
- Multilingual document matching in one index
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run Mistral Embed on LLM.API?
Unified AI Routing
Production: Reach Mistral Embed and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale Mistral Embed.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Mistral Embed alongside the rest of your stack.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Practical: Swap Mistral Embed for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You need consistent vectors from an OpenAI-compatible embeddings API (Mistral Embed)
- Throughput and cost matter more than generative prose quality (Mistral Embed)
- You are building search, RAG, or clustering on text (Mistral Embed)
Avoid if...
- You need generative answers instead of vectors
- You require cross-model vector compatibility without re-indexing
- You only need OCR or speech features
COMMUNITY
What developers say about Mistral models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Mistral Embed.
- Long-form paid reviews rate Mistral Large around 4/5 and call it the best European-hosted option when data residency matters.
- For pure coding, community consensus is that it trails the top US and Chinese models, though it is considered fully usable as an assistant.
- Developers pick it mainly for EU hosting, predictable pricing, and open-weight siblings rather than leaderboard position.
SOURCES
Frequently Asked Questions
What is the context length for Mistral Embed?
Reported context for Mistral Embed is 8K tokens. Always verify the active provider row if multiple providers are listed.
How do I call Mistral Embed via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "mistral-embed" and your LLM.API key. See the code snippet on this page.
How is Mistral Embed priced on LLM.API?
Listed pricing metadata shows: In $0.1 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Can I use tools or structured outputs with Mistral Embed?
Tooling support varies; for pure embedding models, prefer the modalities listed rather than assuming chat tools.
What are limitations of Mistral Embed?
Like other API models, Mistral Embed can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Can Mistral Embed generate paragraphs of text?
No. Mistral Embed returns embeddings (vectors). Pair it with a chat model if you need generated language.
Where is the canonical page for Mistral Embed?
https://llmapi.ai/models/mistralai-mistral-embed/
What modalities does Mistral Embed support?
Mistral Embed accepts text and produces embedding according to its architecture metadata on LLM.API.
Does Mistral Embed support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
Which providers serve Mistral Embed?
LLM.API currently lists: mistral. Availability can vary by region and account.
When should I choose Mistral Embed?
Throughput and cost matter more than generative prose quality — especially when you specifically need Mistral Embed.
Is Mistral Embed a chat model?
No—Mistral Embed is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.
COMPARE
Competitive Models
Azure OpenAI Text Embedding 3 Large
Consider Azure OpenAI Text Embedding 3 Large when you want a related embedding alternative to Mistral Embed.
Pixtral Large Latest
Consider Pixtral Large Latest when you want a related chat alternative to Mistral Embed.
Mistral Large Latest
Sibling-style choice: Mistral Large Latest (mistral-large-latest) for comparable chat workloads.
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