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Amazon Bedrock offers Grok 4.6 with a 500K-token context window and configurable reasoning effort. Its model card lists implicit prompt caching, which can reduce the cost of eligible repeated input when Bedrock serves tokens from cache—but caching is best effort, not a per-request guarantee. A Node.js 22 app can call the model through AWS SDK for JavaScript v3 and the Bedrock Runtime Converse API.
What Bedrock Grok 4.6 offers
Grok 4.6 is an xAI model available through Amazon Bedrock. AWS describes it for coding, agentic tasks, and knowledge work. The AWS model card lists a 500K-token context window and four reasoning-effort settings: low, medium, high, and xhigh. Those are AWS-listed specifications, not independent performance test results. AWS records the launch date as August 18, 2026, and says the model is active with an end-of-life date no sooner than August 18, 2027. See the Grok 4.6 model card.
How prompt caching affects token efficiency
Prompt caching is designed to reduce latency and input-token costs when requests reuse eligible context. With implicit caching, Bedrock and the model automatically attempt to reuse eligible prompt prefixes; the request does not specify cache checkpoints. Grok 4.6’s model card lists implicit prompt caching as supported. It does not list explicit prompt caching for this model, so do not assume that explicit cache-checkpoint syntax applies.
A cache hit is not guaranteed. Repeating a prompt alone does not prove that its tokens were served from cache. To improve the chance of reuse, arrange prompts so the stable material comes first and request-specific changes come later.
#1 Best Overall
- Put repeated system instructions and reference material at the beginning.
- Keep the order and wording of that stable prefix consistent across requests.
- Place changing user input and other variable content after the shared material.
- Inspect response usage or cache-usage fields where exposed; measure representative requests rather than estimating savings from prompt structure.
Cache reads can lower the input cost for tokens actually served from cache, but they do not automatically reduce output-token usage or make every call cheaper. Uncached input remains billed at the standard input rate, while cache-read tokens use the model-specific cache-read rate. AWS notes that caching configuration and billing treatment depend on the model and API. Read AWS’s prompt-caching guidance.
Grok 4.6 token rates listed by AWS
The following are standard-tier rates per 1 million tokens displayed on the Grok 4.6 model card on October 7, 2026. They are date-specific published rates, not a promise of permanent pricing; check the model card for current rates and availability before estimating spend.
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| Bedrock route | Input | Output | Cache read |
|---|---|---|---|
| In-Region and Geo CRIS | $2.20 per 1 million tokens | $6.60 per 1 million tokens | $0.55 per 1 million tokens |
| Global CRIS | $2.00 per 1 million tokens | $6.00 per 1 million tokens | $0.50 per 1 million tokens |
AWS also lists Priority service at 1.75× standard per-token pricing and Flex at 0.5×. These are model-card terms checked October 7, 2026; confirm current service-tier availability and pricing for your route. The model card has the applicable rates.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchChoose the Bedrock access path and model ID
The model ID depends on the Bedrock API path. On bedrock-mantle, AWS lists xai.grok-4.6. On bedrock-runtime, use a cross-Region inference profile: us.xai.grok-4.6 or global.xai.grok-4.6. The model card says Grok 4.6 is not available for in-Region inference on the Bedrock Runtime endpoint.
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The example below uses the Runtime Converse API and the US profile shown in AWS’s Converse example. Choose the profile that fits your geography, account availability, and current AWS configuration; the code’s US identifier is not a universal regional setting.
Call Grok 4.6 from a Node.js 22 app
Install the AWS SDK for JavaScript v3 Bedrock Runtime package in your project:
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npm install @aws-sdk/client-bedrock-runtimeThen create a client and send a Converse request:
import { BedrockRuntimeClient, ConverseCommand } from "@aws-sdk/client-bedrock-runtime";
const client = new BedrockRuntimeClient({
region: process.env.AWS_REGION,
});
const command = new ConverseCommand({
modelId: "us.xai.grok-4.6",
messages: [
{
role: "user",
content: [{ text: "Explain prompt caching simply." }],
},
],
});
const response = await client.send(command);
console.log(response.output?.message?.content);- Set
AWS_REGIONto the AWS Region from which your application calls Bedrock Runtime. - Configure credentials using the normal AWS SDK credential chain, and ensure the identity has
bedrock:InvokeModelpermission for the model/profile. - Use the correct inference-profile ID for your chosen route and account availability.
- Send a message through
ConverseCommand; the SDK’s Converse request takes a model ID and amessagesarray.
This is an illustrative SDK integration shape based on AWS’s JavaScript reference and model-card example, not a claim that the snippet has been tested. The cited AWS sources do not establish Node.js 22 as a Grok-specific requirement. Node.js 22 is the runtime selected here; check the current SDK runtime compatibility guidance when configuring your project. For the command and request interface, see AWS SDK for JavaScript v3 ConverseCommand.
Verify cache use instead of assuming savings
For a workload with recurring context, log the response’s cache-usage fields when the implementation exposes them. Compare cache-read volume with total input across representative repeated requests, and account for both cache reads and uncached input at their applicable rates. AWS documents cache usage in response fields for supported implementations and cautions that a hit is not guaranteed. The resulting measurement—not a general savings percentage—is the useful basis for deciding whether prompt caching improves your workload’s token efficiency.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
