Mistral AI models

Mistral AI models on Vertex AI offer fully managed and serverless models as APIs. To use a Mistral AI model on Vertex AI, send a request directly to the Vertex AI API endpoint. Because Mistral AI models use a managed API, there's no need to provision or manage infrastructure.

You can stream your responses to reduce the end-user latency perception. A streamed response uses server-sent events (SSE) to incrementally stream the response.

You pay for Mistral AI models as you use them (pay as you go). For pay-as-you-go pricing, see Mistral AI model pricing on the Vertex AI pricing page.

Available Mistral AI models

The following models are available from Mistral AI to use in Vertex AI. To access a Mistral AI model, go to its Model Garden model card.

Mistral OCR (25.05)

Mistral OCR (25.05) is an Optical Character Recognition API for document understanding. Mistral OCR (25.05) excels in understanding complex document elements, including interleaved imagery, mathematical expressions, tables, and advanced layouts such as LaTeX formatting. The model enables deeper understanding of rich documents such as scientific papers with charts, graphs, equations and figures.

Mistral OCR (25.05) is an ideal model to use in combination with a RAG system that takes multimodal documents (such as slides or complex PDFs) as input.

You can couple Mistral OCR (25.05) with other Mistral models to reformat the results. This combination ensures that the extracted content is not only accurate but also presented in a structured and coherent manner, making it suitable for various downstream applications and analyses.

Go to the Mistral OCR (25.05) model card

Mistral Small 3.1 (25.03)

Mistral Small 3.1 (25.03) features multimodal capabilities and a context of up to 128,000. The model can process and understand visual inputs and long documents, further expanding its range of applications compared to the previous Mistral AI Small model. Mistral Small 3.1 (25.03) is a versatile model designed for various tasks such as programming, mathematical reasoning, document understanding, and dialogue. Mistral Small 3.1 (25.03) is designed for low-latency applications to deliver best-in-class efficiency compared to models of the same quality.

Mistral Small 3.1 (25.03) has undergone a full post-training process to align the model with human preferences and needs, making it usable out-of-the-box for applications that require chat or precise instruction following.

Go to the Mistral Small 3.1 (25.03) model card

Mistral Large (24.11)

Mistral Large (24.11) is the latest version of Mistral AI's Large model now with improved reasoning and function calling capabilities.

• Agent-centric: best-in-class agentic capabilities with built-in function calling and JSON outputs.
• Multi-lingual by design: dozens of languages supported, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish
• Proficient in coding: trained on 80+ coding languages such as Python, Java, C, C++, JavaScript, and Bash. Also trained on more specific languages such as Swift and Fortran
• Advanced reasoning: state-of-the-art mathematical and reasoning capabilities.

Go to the Mistral Large (24.11) model card

Codestral (25.01)

Codestral (25.01) is designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code along with its ability to converse in a variety of languages, you can use Codestral (25.01) to design advanced AI applications for software developers.

• Codestral (25.01) is fluent in 80+ programming languages including Python, Java, C, C++, JavaScript, and Bash. It also performs well on more specific languages like Swift and Fortran.
• Codestral (25.01) helps to improve developers' productivity and reduces errors: Codestral (25.01) can complete coding functions, write tests, and complete any partial code using a fill-in-the-middle mechanism.
• Codestral (25.01) provides a new standard on the performance and latency space with only 24B parameters and a 128,000 context window.

Codestral (25.01) is optimized for the following use cases:

• Generates code and provides code completion, suggestions, and translation.
• Adds code between user-defined start and end points, which makes it ideal for tasks that require a specific piece of code to be generated.
• Summarizes and explains your code.
• Reviews the quality of your code by helping to refactor your code, fixes bugs, and generates test cases.

Go to the Codestral (25.01) model card

Use Mistral AI models

You can use curl commands to send requests to the Vertex AI endpoint using the following model names:

• For Mistral OCR (25.05), use mistral-ocr-2505
• For Mistral Small 3.1 (25.03), use mistral-small-2503
• For Mistral Large (24.11), use mistral-large-2411
• For Mistral Nemo, use mistral-nemo
• For Codestral (25.01), use codestral-2501

For more information about using the Mistral AI SDK, see the Mistral AI Vertex AI documentation.

Before you begin

To use Mistral AI models with Vertex AI, you must perform the following steps. The Vertex AI API (aiplatform.googleapis.com) must be enabled to use Vertex AI. If you already have an existing project with the Vertex AI API enabled, you can use that project instead of creating a new project.

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

1. Go to one of the following Model Garden model cards, then click Enable:
   • Go to the Mistral OCR (25.05) model card
   • Go to the Mistral Small 3.1 (25.03) model card
   • Go to the Mistral Large (24.11) model card
   • Go to the Mistral Nemo model card
   • Go to the Codestral (25.01) model card

Make a streaming call to a Mistral AI model

The following sample makes a streaming call to a Mistral AI model.

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:streamRawPredict

{
"model": MODEL,
  "messages": [
   {
    "role": "ROLE",
    "content": "CONTENT"
   }],
  "max_tokens": MAX_TOKENS,
  "stream": true
}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:streamRawPredict"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:streamRawPredict" | Select-Object -Expand Content

data: {
    "id": "0e9c8e69e5924f729b39bc60bac9e0be",
    "object": "chat.completion.chunk",
    "created": 1720807292,
    "model": "MODEL",
    "choices": [
        {
            "index": 0,
            "delta": {
              "content": "OUTPUT"
            },
            "finish_reason": null,
            "logprobs": null
        }
    ]
}

data: {
    "id": "0e9c8e69e5924f729b39bc60bac9e0be",
    "object": "chat.completion.chunk",
    "created": 1720807292,
    "model": "MODEL",
    "choices": [
        {
            "index": 0,
            "delta": {
              "content": "OUTPUT"
            },
            "finish_reason": null,
            "logprobs": null
        }
    ]
}
...

Make a unary call to a Mistral AI model

The following sample makes a unary call to a Mistral AI model.

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:rawPredict

{
"model": MODEL,
  "messages": [
   {
    "role": "ROLE",
    "content": "CONTENT"
   }],
  "max_tokens": MAX_TOKENS,
  "stream": false
}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:rawPredict"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/mistralai/models/MODEL:rawPredict" | Select-Object -Expand Content

{
    "id": "e71d13ffb77344a08e34e0a22ea84458",
    "object": "chat.completion",
    "created": 1720806624,
    "model": "MODEL",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "OUTPUT",
                "tool_calls": null
            },
            "finish_reason": "stop",
            "logprobs": null
        }
    ],
    "usage": {
        "prompt_tokens": 17,
        "total_tokens": 295,
        "completion_tokens": 278
    }
}

Mistral AI model region availability and quotas

For Mistral AI models, a quota applies for each region where the model is available. The quota is specified in queries per minute (QPM) and tokens per minute (TPM). TPM includes both input and output tokens.

If you want to increase any of your quotas for Generative AI on Vertex AI, you can use the Google Cloud console to request a quota increase. To learn more about quotas, see Work with quotas.