Mistral AI models

Mistral AI models on Gemini Enterprise Agent Platform offer fully managed and serverless models as APIs. To use a Mistral AI model on Agent Platform, send a request directly to the Agent Platform 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 Gemini Enterprise Agent Platform pricing page.

Available Mistral AI models

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

Mistral Medium 3 A versatile multimodal model designed for advanced reasoning, programming, long-context document understanding, single-node high throughput, and agentic workflows.

Mistral OCR (25.05) Optical Character Recognition API for rich document understanding, extracting interleaved imagery, tables, graphs, and LaTeX formatting for multimodal RAG pipelines.

Mistral Small 3.1 (25.03) A versatile, low-latency multimodal model with a 128,000 token context window, optimized for high-efficiency chat, programming, and instruction following.

Codestral 2 Specialized code generation model built for high-precision fill-in-the-middle (FIM) completion, code review, refactoring, and developer tooling.

Use Mistral AI models

You can use curl commands to send requests to the Gemini Enterprise Agent Platform endpoint using the following model names:

For Mistral Medium 3, use mistral-medium-3
For Mistral OCR (25.05), use mistral-ocr-2505
For Mistral Small 3.1 (25.03), use mistral-small-2503
For Codestral 2, use codestral-2

For more information about using the Mistral AI SDK, see the Mistral AI Gemini Enterprise Agent Platform documentation.

Before you begin

To use Mistral AI models with Gemini Enterprise Agent Platform, you must perform the following steps. The Agent Platform API (aiplatform.googleapis.com) must be enabled to use Gemini Enterprise Agent Platform. If you already have an existing project with the Agent Platform 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.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Gemini Enterprise Agent Platform API, if it is not already enabled.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the API

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

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Gemini Enterprise Agent Platform API, if it is not already enabled.

Roles required to enable APIs

Enable the API

Go to one of the following Model Garden model cards, then click Enable:

Make a streaming call to a Mistral AI model

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

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION: A region that supports Mistral AI models.
MODEL: The model name you want to use. In the request body, exclude the @ model version number.
ROLE: The role associated with a message. You can specify a user or an assistant. The first message must use the user role. The models operate with alternating user and assistant turns. If the final message uses the assistant role, then the response content continues immediately from the content in that message. You can use this to constrain part of the model's response.
STREAM: A boolean that specifies whether the response is streamed or not. Stream your response to reduce the end-use latency perception. Set to true to stream the response and false to return the response all at once.
CONTENT: The content, such as text, of the user or assistant message.
MAX_OUTPUT_TOKENS: Maximum number of tokens that can be generated in the response. A token is approximately 3.5 characters. 100 tokens correspond to roughly 60-80 words.
Specify a lower value for shorter responses and a higher value for potentially longer responses.

HTTP method and URL:

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

Request JSON body:

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

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

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"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$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

You should receive a JSON response similar to the following.

Response

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.

REST

After you set up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

Before using any of the request data, make the following replacements:

LOCATION: A region that supports Mistral AI models.
MODEL: The model name you want to use. In the request body, exclude the @ model version number.
ROLE: The role associated with a message. You can specify a user or an assistant. The first message must use the user role. The models operate with alternating user and assistant turns. If the final message uses the assistant role, then the response content continues immediately from the content in that message. You can use this to constrain part of the model's response.
STREAM: A boolean that specifies whether the response is streamed or not. Stream your response to reduce the end-use latency perception. Set to true to stream the response and false to return the response all at once.
CONTENT: The content, such as text, of the user or assistant message.
MAX_OUTPUT_TOKENS: Maximum number of tokens that can be generated in the response. A token is approximately 3.5 characters. 100 tokens correspond to roughly 60-80 words.
Specify a lower value for shorter responses and a higher value for potentially longer responses.

HTTP method and URL:

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

Request JSON body:

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

To send your request, choose one of these options:

curl

Save the request body in a file named request.json, and execute the following command:

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"

PowerShell

Save the request body in a file named request.json, and execute the following command:

$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

You should receive a JSON response similar to the following.

Response

{
    "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.

Model	Region	Quotas	Context length
Mistral Medium 3
	`us-central1`	QPM: 90 TPM: 315,000	128,000
	`europe-west4`	QPM: 90 TPM: 315,000	128,000
Mistral OCR (25.05)
	`us-central1`	QPM: 30 Pages per request: 30 (1 page = 1 million input tokens and 1 million output tokens)	30 pages
	`europe-west4`	QPM: 30 Pages per request: 30 (1 page = 1 million input tokens and 1 million output tokens)	30 pages
Mistral Small 3.1 (25.03)
	`us-central1`	QPM: 60 TPM: 200,000	128,000
	`europe-west4`	QPM: 60 TPM: 200,000	128,000
Codestral 2
	`us-central1`	QPM: 1,100 Input TPM: 1,100,000 Output TPM: 110,000	128,000 tokens
	`europe-west4`	QPM: 1,100 Input TPM: 1,100,000 Output TPM: 110,000	128,000 tokens

If you want to increase any of your quotas for Agent Platform, you can use the Google Cloud console to request a quota increase. To learn more about quotas, see the Cloud Quotas overview.

Mistral AI models Stay organized with collections Save and categorize content based on your preferences.

Available Mistral AI models

Use Mistral AI models

Before you begin

Make a streaming call to a Mistral AI model

REST

curl

PowerShell

Response

Make a unary call to a Mistral AI model

REST

curl

PowerShell

Response

Mistral AI model region availability and quotas

Mistral AI models