Grok 4.6

xAI — Grok 4.6

Model Details

Grok 4.6 is xAI's frontier model built for coding, agentic tasks, and knowledge work. It builds on previous generations of Grok with a particular focus on long-running agents and more ambitious interactive work. It offers 500K context window and configurable reasoning efforts (low, medium, high, xhigh).

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime endpoint

Features supported using bedrock-mantle endpoint

Pricing

All prices are per 1 million tokens. Pricing shown is for the Standard tier.

Priority and Flex tier support: In addition to Standard, Grok 4.6 supports the Priority and Flex service tiers. Priority is billed at 2x the Standard per-token rate (a 100% premium) in exchange for prioritized processing and faster response times, while Flex is billed at 0.5x the Standard rate (a 50% discount) for flexible, non-time-sensitive workloads. Apply these multipliers to the Standard rates shown above to determine Priority and Flex pricing. For more information, see service tiers.

Programmatic Access

Use the following model IDs and endpoint URLs to access this model programmatically. For more information about the available APIs and endpoints, see APIs supported and Endpoints supported.

For example, if region is us-west-2 (Oregon), then the bedrock-mantle endpoint URL will be "https://bedrock-mantle.us-west-2.api.aws/openai/v1". On bedrock-runtime, the base URL is "https://bedrock-runtime.{region}.amazonaws.com/openai/v1" and requests must name the geographic cross-Region inference ID us.xai.grok-4.6 or global.xai.grok-4.6 as the model.

Service Tiers

Amazon Bedrock offers multiple service tiers to match your workload requirements. Standard provides pay-per-token access with no commitment (set "service_tier": "default" or omit the field). Priority delivers faster, prioritized processing for a price premium (set "service_tier": "priority"). Flex provides lower-cost access for flexible, non-time-sensitive workloads (set "service_tier": "flex"). For more information, see service tiers.

Regional Availability

Regional availability at a glance

Amazon Bedrock offers three inference options: In-Region keeps requests within a single Region for strict compliance, Geo Cross-Region routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and Global Cross-Region routes anywhere worldwide when there are no residency constraints. Refer to the Regional availability by models page for more details.

Availability differs by endpoint.

Availability using the bedrock-mantle endpoint

Availability using the bedrock-runtime endpoint

Quotas and Limits

Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota increase request. For more information, see Quotas for Amazon Bedrock documentation and see the limits for the model.

Sample Code

Step 1 - AWS Account: If you have an AWS account already, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.

Responses API / Chat Completions API

pip install openai

Converse API

pip install boto3

Step 4 - Set environment variables: Configure your environment to use the API key for authentication.

bedrock-mantle

OPENAI_API_KEY="<provide your Bedrock API key>"
OPENAI_BASE_URL="https://bedrock-mantle.us-west-2.api.aws/openai/v1"

bedrock-runtime

OPENAI_API_KEY="<provide your Bedrock API key>"
OPENAI_BASE_URL="https://bedrock-runtime.us-east-1.amazonaws.com/openai/v1"

Step 5 - Run your first inference request: Save the file as bedrock-first-request.py

Responses API (bedrock-mantle)

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="xai.grok-4.6",
    input="Can you explain the features of Amazon Bedrock?"
)
print(response)

Responses API (bedrock-runtime)

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="us.xai.grok-4.6",
    input="Can you explain the features of Amazon Bedrock?"
)
print(response)

Chat Completions API (bedrock-mantle)

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="xai.grok-4.6",
    messages=[
        {"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}
    ]
)
print(response)

Chat Completions API (bedrock-runtime)

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="us.xai.grok-4.6",
    messages=[
        {"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}
    ]
)
print(response)

Converse API (bedrock-runtime)

import boto3

client = boto3.client("bedrock-runtime", region_name="us-east-1")

response = client.converse(
    modelId="us.xai.grok-4.6",
    messages=[
        {"role": "user", "content": [{"text": "Can you explain the features of Amazon Bedrock?"}]}
    ]
)
print(response["output"]["message"]["content"][0]["text"])

Usage Considerations and Limitations