Grok 4.3 on Amazon Bedrock: The Mantle Endpoint Changes Everything You Know About Bedrock Integration

On June 15, 2026, xAI’s Grok 4.3 became available on Amazon Bedrock (AWS What’s New — Grok on Bedrock; AWS Bedrock model card for Grok 4.3). The announcement is straightforward. The integration is not.

Grok 4.3 on Bedrock does not use the standard bedrock-runtime endpoint that every other Bedrock model uses. It does not support the Converse API. It does not support InvokeModel. It runs on a new infrastructure layer called Mantle — accessed via a completely different base URL — and it uses the OpenAI SDK rather than the AWS SDK for inference calls (AWS Bedrock model card, “APIs supported” / “Endpoints supported” table).

If you approach this like any other Bedrock model, you will hit errors immediately. This guide covers the actual architecture, the exact integration pattern, the reasoning configuration specifics, and how to decide whether Bedrock-hosted Grok is the right deployment path for your workload.

What “Mantle” Is

Amazon Bedrock launched a new inference engine alongside the Grok 4.3 integration called Mantle. AWS describes it as “designed for price performance” (AWS Bedrock model card for Grok 4.3), but the more consequential detail is what it changes architecturally.

Standard Bedrock inference — the path used by Claude, Titan, Llama, Mistral, and every other model in the catalog — runs through bedrock-runtime.{region}.amazonaws.com. Authentication goes through IAM, requests use InvokeModel or the Converse API, and response formats follow Bedrock’s normalized schema.

Mantle is a separate endpoint entirely: bedrock-mantle.{region}.api.aws (AWS Bedrock docs — “Inference using Responses API” / bedrock-mantle). It uses a Bedrock-issued API key rather than standard IAM access keys, generated from the Bedrock console (AWS credentials are also supported for raw HTTP requests, per the same docs page). And its path structure follows OpenAI’s API specification — specifically openai/v1/ — rather than Bedrock’s own schema.

In practice, Mantle is Amazon running an OpenAI-compatible inference endpoint that happens to be backed by Bedrock infrastructure. The authentication, the URL structure, and the SDK you use are all OpenAI-side conventions, not AWS conventions.

The Actual Model ID and Endpoint

Before anything else: the model ID is xai.grok-4.3, not grok-4.3 or xai/grok-4.3. The xai. prefix is how Bedrock namespaces third-party providers in its catalog (AWS Bedrock model card — “Programmatic Access” table).

The endpoint URL for us-west-2:

https://bedrock-mantle.us-west-2.api.aws/openai/v1

Grok 4.3 is currently listed as in-region-supported in three regions: us-west-2 (Oregon), us-east-1 (N. Virginia), and us-east-2 (Ohio) — swap the region name in the URL accordingly. Geo and Global cross-region inference are not supported for this model. (AWS Bedrock model card — “Regional Availability” table)

Note the openai/v1 path. Other models on the Responses API use /v1/responses. Grok 4.3 on Mantle uses /openai/v1/responses. The openai/ prefix is required for this model — omitting it returns a 404 (AWS Bedrock model card — note under “Programmatic Access”: “This model is available on the openai/v1/responses path on the bedrock-mantle endpoint. This is different from the v1/responses path used by other models on the responses endpoint.").

This is not a typo in the documentation. It is how Mantle routes requests to the xAI backend.

Getting Started: The Actual Integration

AWS provides straightforward setup steps (AWS Bedrock model card — “Sample Code”). First, generate a long-term API key from the Amazon Bedrock console. This is different from standard AWS access keys — it is a Bedrock-specific key for Mantle endpoints.

Then configure the OpenAI SDK (not the AWS SDK):

pip install openai

Set environment variables:

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

Chat Completions API:

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="xai.grok-4.3",
    messages=[
        {"role": "user", "content": "Review this contract clause for jurisdiction ambiguity."}
    ]
)
print(response.choices[0].message.content)

Responses API:

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="xai.grok-4.3",
    input="Review this contract clause for jurisdiction ambiguity."
)
print(response.output_text)

The Responses API is the recommended path for reasoning-aware workloads, because only the Responses API returns reasoning tokens and supports encrypted reasoning content for multi-turn context.

Reasoning Configuration

Grok 4.3 on Bedrock has reasoning always-on. The default effort level is low. This is not the same as no reasoning — low effort means the model is thinking before every response, which increases latency and token cost relative to a non-reasoning model. (AWS Bedrock model card — “Usage Considerations and Limitations”: “Reasoning is always active by default… low (default)")

The four effort levels:

Level	Behavior
`none`	Disables reasoning entirely. Fastest, lowest cost. Behaves like a standard completion model.
`low`	Default. Light reasoning on every response.
`medium`	Deeper chain-of-thought. Noticeably slower. Use for complex analysis tasks.
`high`	Maximum reasoning effort. Significant latency. Use for high-stakes single-turn tasks.

To override the default:

response = client.responses.create(
    model="xai.grok-4.3",
    reasoning={"effort": "high"},
    include=["reasoning.encrypted_content"],
    input="Analyze this credit agreement for cross-default provisions."
)

The include=["reasoning.encrypted_content"] parameter tells Bedrock to return the reasoning trace in encrypted form. You can pass this encrypted content back in subsequent turns to give the model context on how it reasoned in previous steps — useful for multi-step analysis tasks where you want consistent reasoning continuity. (AWS Bedrock model card — “Usage Considerations and Limitations”)

Important: The Chat Completions API does not return reasoning tokens at all (AWS Bedrock model card — “Usage Considerations and Limitations”: “The Chat Completions API does not return reasoning tokens."). If you need to inspect or loop back reasoning content, use the Responses API.

To fully disable reasoning:

response = client.chat.completions.create(
    model="xai.grok-4.3",
    messages=[{"role": "user", "content": "Summarize this document."}],
    extra_body={"reasoning": {"effort": "none"}}
)

Default Parameter Differences

Grok 4.3 on Bedrock uses defaults that differ from the OpenAI API specification. If you are porting code from an OpenAI integration without explicit parameter setting, these differences will affect output behavior (AWS Bedrock model card — “Usage Considerations and Limitations”):

Parameter	OpenAI default	Grok 4.3 on Bedrock default
`temperature`	1.0	0.7
`top_p`	1.0	0.95
`max_completion_tokens`	model-defined	131,072

The lower temperature default means outputs will be less random than what you get from GPT-4o or Claude at their defaults. If you are running deterministic or precision-sensitive tasks, temperature: 0.7 may actually be preferable. If you are running creative or varied-output tasks, explicitly set temperature higher or you will get more uniform outputs than intended.

Capabilities: What Is and Is Not Available

Grok 4.3 on Bedrock via Mantle supports (AWS Bedrock model card — capabilities table):

Text input ✓
Image input ✓
Text output ✓
Chat Completions API ✓
Responses API ✓
Tool calling ✓
Structured output ✓
Response streaming ✓

Grok 4.3 on Bedrock does not support (same source):

Audio input/output ✗
Video input ✗
Embedding output ✗
Converse API ✗ — the standard Bedrock multi-provider abstraction does not work
InvokeModel ✗ — the standard Bedrock invocation API does not work
bedrock-runtime endpoint ✗ — you must use the Mantle endpoint
Global or Geo cross-region inference ✗ — in-region only, in the three supported regions listed above
Reserved throughput ✗

Note: xAI’s own API documentation for Grok 4.3 also lists the model’s modalities as text and image input, text output only (xAI Docs — Grok 4.3) — it does not document native video input or voice cloning as capabilities of the xai.grok-4.3 model itself. Voice cloning (“Custom Voices”) and real-time video-in-voice-chat are separate xAI products bundled into the wider Grok app/API ecosystem around the same release window; they are not documented capabilities of this specific model ID. The audio and video gaps matter regardless if you are evaluating Grok 4.3 as a replacement for multimodal workloads: the Bedrock deployment is specifically a text-and-image reasoning path per AWS’s own model card.

The Converse API gap is significant if you have an existing multi-provider Bedrock deployment that routes through Converse to abstract over Claude, Titan, and others. Adding Grok 4.3 to that abstraction layer is not currently possible. You would need a separate integration path specifically for Grok.

Service Tiers

Bedrock’s four inference tiers are partially available (AWS Bedrock model card — “Service Tiers”):

Standard — Pay-per-token. No commitment. ✓
Priority — Higher throughput with a time-based commitment. ✓
Flex — Lower cost for flexible, non-time-sensitive workloads. ✓
Reserved — Dedicated throughput with a term commitment. ✗

The absence of Reserved throughput means you cannot provision committed capacity for Grok 4.3 on Bedrock at this point. For workloads that need SLA-backed throughput guarantees, the native xAI API’s own Priority Processing feature — which requests higher scheduling priority per-request via service_tier: "priority", introduced in xAI’s June 2026 release notes (xAI Docs — Priority Processing; xAI Docs — Release Notes) — may be a better path.

Bedrock-Native vs. xAI-Direct: When to Choose Each

The decision is not about capability parity — it is about where your existing infrastructure lives and what compliance requirements you have.

Reasons to use Grok 4.3 on Bedrock:

Your workload already runs on AWS infrastructure with IAM-bounded access control
You need enterprise compliance artifacts: VPC endpoints, AWS PrivateLink, CloudTrail audit logs, service control policies
You are billing AWS usage to a centralized account with consolidated invoicing
Your procurement team has an AWS enterprise agreement that covers third-party models on Bedrock
You want AWS-side data residency commitments for us-west-2 processing

Reasons to use the native xAI API:

You need modalities Bedrock doesn’t support for this model (audio, video) — see the capabilities note above, since these are not confirmed capabilities of xai.grok-4.3 specifically, but they are unavailable on the Bedrock deployment either way
You are already on the xAI API for other Grok models and want one credential to manage
You need cross-region routing beyond the three in-region-only regions Bedrock currently supports
Your team runs Grok Build (xAI’s coding agent CLI) and wants API consistency
You are evaluating Grok V9-Medium when it ships to API — it will appear on the native xAI API first

The native xAI API pricing for Grok 4.3 is $1.25 per million input tokens and $2.50 per million output tokens for prompts under 200K tokens, with cached input at $0.20/million (xAI Docs — Grok 4.3). As of this audit, Amazon Bedrock’s on-demand pricing for Grok 4.3 lists the identical $1.25/$2.50 per million rate for its supported regions — no separate Bedrock markup is currently listed (Amazon Bedrock Pricing page, xAI section). Confirm current rates on the pricing page before budgeting, since either side could change independently.

The SpaceX/xAI Context

The Bedrock launch happened three days before SpaceX entered into a definitive $60 billion all-stock merger agreement to acquire Cursor’s parent company, Anysphere, on June 16, 2026 (SEC filing, Space Exploration Technologies Corp.; TechCrunch). The deal remains subject to regulatory approval and is expected to close in Q3 2026 — it is a signed agreement, not yet a completed acquisition. The timing relative to the Bedrock launch is coincidental — the Bedrock integration predates the merger filing by weeks of infrastructure work — but the acquisition context is relevant for the longer-term deployment picture.

Post-acquisition (once and if it closes), xAI’s model distribution strategy could shift. Cursor reports roughly 4 million active developers (Cursor statistics roundup), which would give xAI a large existing IDE distribution channel if the deal completes. Bedrock gives it enterprise distribution through AWS’s enterprise sales channel. These are complementary paths: consumer/IDE developers through Cursor (pending deal close), enterprise builders through Bedrock, direct API users through api.x.ai.

Grok V9-Medium, a 1.5-trillion-parameter model xAI says completed training with a public release targeted roughly two to three weeks out, has not appeared on Bedrock documentation as of this writing. Elon Musk announced the training completion in late May 2026, putting the expected release in the mid-to-late-June window; reporting has continued to track it as imminent rather than shipped (KuCoin news coverage of Musk’s announcement; Tech Times). When it ships to Bedrock, expect the same Mantle-endpoint architecture.

Summary for Builders

Three things to internalize before you start:

Use the OpenAI SDK, not the AWS SDK. The Mantle endpoint is OpenAI-compatible. boto3.client('bedrock-runtime') will not work.
Reasoning is always-on at low effort by default. If you want no reasoning, pass {"effort": "none"} explicitly. If you want reasoning continuity across multi-turn conversations, use the Responses API and loop back encrypted reasoning content.
Check your default parameters. Temperature defaults to 0.7, not 1.0. If your existing prompts were calibrated for OpenAI-style defaults, outputs will be more conservative than expected until you adjust.

The Mantle endpoint for Grok 4.3 is available now in-region in us-west-2, us-east-1, and us-east-2 (AWS Bedrock model card — “Regional Availability”). More regions may follow.

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