AI21 Labs models

AI21 Labs models on Vertex AI offer fully managed and serverless models as APIs. To use a AI21 Labs model on Vertex AI, send a request directly to the Vertex AI API endpoint. Because AI21 Labs 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 AI21 Labs models as you use them (pay as you go). For pay-as-you-go pricing, see AI21 Labs model pricing on the Vertex AI pricing page.

Available AI21 Labs models

The following models are available from AI21 Labs to use in Vertex AI. To access a AI21 Labs model, go to its Model Garden model card.

Jamba 1.5 Mini

AI21 Labs's Jamba 1.5 Mini is a small foundation model built from a hybrid architecture that leverages the Mamba architecture and Transformer architecture to achieve leading quality at a competitive price.

With the SSM-Transformer hybrid architecture and a 256,000 context window, Jamba 1.5 Mini efficiently solves a variety of text generation and text comprehension enterprise use cases.

Jamba 1.5 Mini is ideal for enterprise workflows with tasks that are data-heavy and require a model that can ingest a large amount of information to produce an accurate and thorough response, such as summarizing lengthy documents or enabling question answering across an extensive organizational knowledge base. Jamba 1.5 Mini is well balanced across quality, throughput, and low cost.

Go to the Jamba 1.5 Mini model card

Jamba 1.5 Large

AI21 Labs's Jamba 1.5 Large is a foundation model built from a hybrid architecture that leverages the Mamba architecture and Transformer architecture to achieve leading quality at a competitive price.

With the SSM-Transformer hybrid architecture and a 256,000 context window, Jamba 1.5 Large efficiently solves a variety of text generation and text comprehension enterprise use cases. Jamba 1.5 Large has 94 B active parameters and 398 B total parameters lead to highly accuracy in responses.

Jamba 1.5 Large is ideal for enterprise workflows with tasks that are data-heavy and require a model that can ingest a large amount of information to produce an accurate and thorough response, such as summarizing lengthy documents or enabling question answering across an extensive organizational knowledge base. Jamba 1.5 Large is designed for superior-quality responses, high throughput, and pricing that is competitive with other models in its size class.

Go to the Jamba 1.5 Large model card

Use AI21 Labs models

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

• For Jamba 1.5 Mini, use jamba-1.5-mini
• For Jamba 1.5 Large, use jamba-1.5-large

We recommend that you use the model versions that include a suffix that starts with an @ symbol because of the possible differences between model versions. If you don't specify a model version, the latest version is always used, which can inadvertently affect your workflows when a model version changes.

Before you begin

To use AI21 Labs 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 Jamba 1.5 Mini model card
   • Go to the Jamba 1.5 Large model card

Make a streaming call to a AI21 Labs model

The following sample makes a streaming call to a AI21 Labs model.

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/ai21/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/ai21/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/ai21/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 non-streaming call to a AI21 Labs model

The following sample makes a non-streaming call to a AI21 Labs 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
    }
}

AI21 Labs model region availability and quotas

For AI21 Labs 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.