[Mistral](/ai-gateway/models/labs/mistral)

# Mistral Embed

Mistral Embed is Mistral's general-purpose text embedding model with 1024 dimensions, designed for semantic search and retrieval tasks with a 55.26 score on the MTEB benchmark. Your use is subject to Mistral's [Terms](https://mistral.ai/terms) & [Privacy](https://mistral.ai/terms#privacy-policy) Policies.

[Use with AI Gateway](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%3Futm_source%3Dgateway-model-page%26utm_campaign%3Dai-gateway-models&title=Get+Started+with+Vercel+AI+Gateway) [View docs](https://vercel.com/docs/ai-gateway)

```
1import { embed } from 'ai';
2

3const result = await embed({
4  model: 'mistral/mistral-embed',
5  value: 'Sunny day at the beach',
6})
```

[Read docs](https://vercel.com/docs/ai-gateway/sdks-and-apis/ai-sdk)

[Overview](/ai-gateway/models/mistral-embed) [About](/ai-gateway/models/mistral-embed/about) [Providers](/ai-gateway/models/mistral-embed/providers) [Similar](/ai-gateway/models/mistral-embed/similar) [FAQ](/ai-gateway/models/mistral-embed/faq)

## [Copy link to heading](#providers)Providers

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the [docs](/docs/ai-gateway/provider-options) for more info. Using a provider means you agree to their terms, listed under Legal.

| Provider |
| --- |

| Context | Input | Capabilities | ZDR | No Training | Release Date |  |
| --- | --- | --- | --- | --- | --- | --- |

| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [Mistral](/ai-gateway/models/providers/mistral) Legal:[Terms](https://mistral.ai/terms)•[Privacy](https://mistral.ai/terms#privacy-policy) |  | $0.10/M |  |  |  | 12/11/2023 |  |
| --- | --- | --- | --- | --- | --- | --- | --- |

## [Copy link to heading](#more-models-by-mistral)More models by Mistral

All

Text

Code

| Model |
| --- |

| Context | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | Providers | ZDR | No Training | Release Date |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/devstral\-small-2](/ai-gateway/models/devstral-small-2) | 256K | 0.3s | 133tps | $0.10/M | $0.30/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 12/09/2025 |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/mistral\-large-3](/ai-gateway/models/mistral-large-3) | 256K | 0.4s | 63tps | $0.50/M | $1.50/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 12/02/2025 |  |
| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/ministral-14b](/ai-gateway/models/ministral-14b) | 256K | 0.3s | 74tps | $0.20/M | $0.20/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 12/02/2025 |  |
| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/ministral-3b](/ai-gateway/models/ministral-3b) | 128K | 0.3s | 151tps | $0.10/M | $0.10/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 10/16/2024 |  |
| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/mistral-small](/ai-gateway/models/mistral-small) | 32K | 0.3s |  | $0.10/M | $0.30/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 09/17/2024 |  |
| ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) [mistral/codestral](/ai-gateway/models/codestral) | 128K | 0.3s |  | $0.30/M | $0.90/M |  | — |  | ![mistral logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Fmistral.png&w=48&q=75) |  |  | 05/29/2024 |  |

## [Copy link to heading](#about-mistral-embed)About Mistral Embed

Mistral Embed launched alongside La Plateforme as Mistral's retrieval-focused embedding endpoint. Mistral Embed produces 1024-dimensional vector representations and scores 55.26 on the Massive Text Embedding Benchmark (MTEB), a standard evaluation suite for embedding model quality.

The embedding space preserves semantic similarity for nearest-neighbor retrieval. Documents with similar meaning cluster closely, while semantically distinct texts land farther apart in the vector space.

Mistral Embed integrates into retrieval-augmented generation (RAG) architectures where a Mistral generation model handles question answering and Mistral Embed indexes the knowledge base. Using the same provider ecosystem for both embedding and generation simplifies the stack and keeps provider management consolidated through AI Gateway.

## [Copy link to heading](#what-to-consider-when-choosing-a-provider)What To Consider When Choosing a Provider

- Configuration: If your corpus is primarily source code rather than natural language, consider Codestral Embed, which was trained specifically on code and outperforms general embedding models on code retrieval benchmarks.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the [documentation](https://vercel.com/docs/ai-gateway/security-and-compliance/zdr) for details.
- Authentication: AI Gateway authenticates requests using an [API key](https://vercel.com/docs/ai-gateway/authentication-and-byok#api-key-authentication) or [OIDC token](https://vercel.com/docs/ai-gateway/authentication-and-byok#oidc-token-authentication). You do not need to manage provider credentials directly.

## [Copy link to heading](#when-to-use-mistral-embed)When to Use Mistral Embed

### Best for

- Semantic search: Retrieval over natural-language document collections where lexical search falls short
- RAG pipelines: Pair embedding with Mistral generation models
- Document similarity and clustering: Grouping and deduplicating content for organization or analytics
- Recommendation systems: Recommender architectures based on textual content similarity
- Multilingual retrieval: Covering European languages supported by the Mistral ecosystem

### Consider alternatives when

- Source code corpus: Use Codestral Embed, which is specialized for code
- Variable dimension embeddings: You need support for storage cost optimization
- Domain-specific retrieval: Highly specialized text may benefit from fine-tuned embeddings

## [Copy link to heading](#conclusion)Conclusion

Mistral Embed is a general-purpose retrieval foundation for Mistral-based stacks. Mistral Embed's 1024-dimensional representations and MTEB-evaluated quality make it a choice for teams building semantic search and RAG systems that want to keep their provider footprint within the Mistral ecosystem.