Mistral Embed API: Endpoint & Pricing
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.
Can you self-host Mistral Embed?
No. We found no public weights for Mistral Embed on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Skip the deploy — use LLM.API as your endpoint
No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.
Providers
LLM.API routes Mistral Embed to the providers below, with discounted effective rates versus list price.
List price by provider
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0.1; out $0 per 1M tokens | 8K tokens | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 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.
Switching from OpenAI? Change 2 lines.
- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"Everything else stays the same — SDK, message format, tools, streaming. Set model to mistral-embed and the call runs unchanged.
curl https://api.llmapi.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMAPI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-embed",
"messages": [
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}'from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAPI_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="mistral-embed",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.LLMAPI_KEY,
baseURL: "https://api.llmapi.ai/v1",
});
const resp = await client.chat.completions.create({
model: "mistral-embed",
messages: [
{ role: "user", content: "Give me three crisp launch checklist items." },
],
});
console.log(resp.choices[0].message.content);stream = client.chat.completions.create(
model="mistral-embed",
messages=[{"role": "user", "content": "Write a haiku about latency."}],
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta.content
if delta:
print(delta, end="", flush=True){
"model": "mistral-embed",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}ENDPOINTS
Supported endpoints & features
Base URL https://api.llmapi.ai/v1 · model ID mistral-embed · OpenAI-compatible.
POST /v1/chat/completionsChat Completions — supported
| Feature | Status | How to set it |
|---|---|---|
| Function calling (tools) | Not supported | tools, tool_choice |
| Parallel tool calls | Not supported | parallel_tool_calls |
| JSON mode | Not supported | response_format: {"type": "json_object"} |
| Structured outputs | Not supported | response_format: {"type": "json_schema", …} |
| Streaming | Not supported | stream: true |
| Vision (image input) | Not supported | image_url content parts |
| Web search | Not supported | web_search |
ERRORS
Errors & fallback routing
| Code | What it means | What to do |
|---|---|---|
401 | Missing or invalid API key. | Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active. |
429 | Rate limited, or the upstream provider is throttling the request. | Back off and retry with jitter; the gateway also retries the request on another provider where one is available. |
5xx | Upstream provider error or timeout. | Retry; fallback routing sends the retry to the next healthy provider for this model. |
When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.
Mistral Embed in production
What the model is good at.
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.
What you get when you call Mistral Embed through LLM.API.
Unified AI Routing
Production: Reach Mistral Embed and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.Cost Control
Compare provider price points and keep spend visible as you scale Mistral Embed.
More performance, less spend.Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.Observability
Production: Trace prompts, tokens, and errors for Mistral Embed alongside the rest of your stack.
See every token, everywhere.Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.Model Breadth
Practical: Swap Mistral Embed for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.
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.
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