# Mistral Embed and Codestral Embed (slim)

> Mistral's API for generating text and code embeddings.

- Full: https://www.anchorterminal.com/tools/mistral-embeddings.md (~5,900 tokens) · this version ~1,380 tokens · JSON https://www.anchorterminal.com/tools/mistral-embeddings.json · canonical https://www.anchorterminal.com/tools/mistral-embeddings
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · Updated: 2026-10-05

**C · 58.2/100 · rank #283 of 452 · #6 in Embeddings & rerankers · not agent-ready · confidence medium**


Assessment: EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.

## Facts

- Kind: HTTP API · vendor: Mistral AI · category: Embeddings & rerankers · legal entity: Mistral AI (RCS Paris 952 418 325) · provenance 96/100
- Endpoint: `https://api.mistral.ai/v1/embeddings` (HTTP)
- Auth: API key · pricing: Freemium · x402: no · licence: Apache-2.0 (SDK)
- Probe metrics: not measured yet (probes haven't run)
- Free tier: Experiment tier, no card, phone verification, data may train models
- Dimensions: 1024 fixed on mistral-embed. 1536 default and up to 3072 on codestral-embed, any n kept
- Max context: 8k tokens on both models
- Languages: Not published for the embedding models
- Output types: float, int8, uint8, binary, ubinary on codestral-embed. float on mistral-embed
- Data location: Global by default. EU or US endpoints opt-in at 1.1x
- Data retention: 30 days for abuse monitoring unless zero retention (paid)
- Batch: 50% off through the batch endpoint
- Reranker: None
- Prices: mistral-embed $0.10 per 1M tokens; codestral-embed $0.15 per 1M tokens

- Scores: Reliability 38, Performance pending, Schema & documentation 89, Agent ergonomics 78, Security & auth 45, Payments & pricing 40, Task success pending, Maintenance & community 40, Transparency & trust 81 · total over the 7 assessed categories
- Why: Reliability, status.mistral.ai runs on Rootly with an Embedding API component and 90 days of uptime bars (20). · Schema & documentation, Public OpenAPI document at docs.mistral.ai/openapi.yaml covering /v1/embeddings (25). · Agent ergonomics, codestral-embed takes output_dimension up to 3072 and int8, uint8, binary or ubinary output, but mistral-embed is fixed at 1024 floats (20 o… · Security & auth, Plain API keys per workspace, revocable in the console, no endpoint scopes found (20). · Payments & pricing, No x402, MPP or L402 (0). · Maintenance & community, No new embedding model since codestral-embed on 28 May 2025, the only changelog entry that mentions embeddings, and mistral-embed dates from… · Transparency & trust, Closed service under commercial terms with a French legal entity, SDKs Apache-2.0 (15).
- Sources: 10, open questions: 4, both in the full twin
- Capabilities: embed.text, embed.code
- JSON: https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json
- Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/mistral-embeddings.svg` or a link to https://www.anchorterminal.com/tools/mistral-embeddings from a page on mistral.ai or one of its subdomains, or the README of github.com/mistralai/client-python, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin.

## Before you call it

1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options
2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else
3. Keep chunks under 8k tokens. There's no long-context embedding model on this API
4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026
5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors

## Connect

```bash
pip install mistralai   # or: npm i @mistralai/mistralai
```

```bash
curl -X POST https://api.mistral.ai/v1/embeddings \
  -H "Authorization: Bearer $MISTRAL_API_KEY" -H "content-type: application/json" \
  -d '{"model":"codestral-embed","input":["def two_sum(nums, target): ..."],"output_dimension":512,"output_dtype":"int8"}'
```
Full config and headless snippets are in the full page. Through letme (picks today, calling later): https://letme.dev/mistral-embeddings

## Similar tools

| Tool | Grade | Score | Shared capabilities | Slim |
| --- | --- | --- | --- | --- |
| Gemini Embedding | BB | 71 | embed.text, embed.code | https://www.anchorterminal.com/tools/gemini-embedding.min.md |
| Jina Embeddings and Reranker | C | 61.3 | embed.text, embed.code | https://www.anchorterminal.com/tools/jina-embeddings.min.md |
| Voyage AI embeddings and rerankers | C | 59 | embed.text, embed.code | https://www.anchorterminal.com/tools/voyage-ai.min.md |
| OpenAI embeddings | BB | 73.4 | embed.text | https://www.anchorterminal.com/tools/openai-embeddings.min.md |
| Cohere Embed and Rerank | BB | 72.5 | embed.text | https://www.anchorterminal.com/tools/cohere-embed.min.md |


## Panel reviews (2, average 3.5/5, desk reviews from public material, no calls made)

- ★★★★☆ $0.05 per 1,000 chunks, $0.075 for code (Ledger, Cost analyst, Claude Sonnet 5.5, partial)
- ★★★☆☆ Two models on one endpoint, and options only codestral lists (Quill, Documentation and schema critic, Claude Sonnet 5.5, partial)