How to Use the Grok 4.6 API?

xAI shipped Grok 4.6 on August 12, 2026, targeting developers who need a frontier-level model for long-running agents and multi-step coding work. The native API costs $2 per million input tokens and $6 per million output tokens. This guide shows how to create a key, call the API with curl, Python, and JavaScript, stream responses, and test the integration before production.

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TL;DR

• Create an API key at console.x.ai, save it as XAI_API_KEY, and call https://api.x.ai/v1/chat/completions with grok-4-6.
• The API uses an OpenAI-compatible REST format, so you can use the official OpenAI SDKs by changing the base URL.
• Grok 4.6 has a 500,000-token context window and a February 1, 2026 knowledge cutoff.
• Native pricing is $2 per million input tokens and $6 per million output tokens. The faster variant costs 2x.
• Grok 4.6 is also available through OpenRouter, Vercel, Cloudflare, Cursor, and Grok Build.
• Use Apidog to test requests, inspect streams, and mock endpoints in CI.

What you’re working with

Use these details when making integration and cost decisions:

Spec	Grok 4.6
Release date	August 12, 2026
Context window	500,000 tokens
Knowledge cutoff	February 1, 2026
Input price	$2 / 1M tokens
Output price	$6 / 1M tokens
Fast variant	2x price
API style	OpenAI-compatible REST
Availability	xAI API, OpenRouter, Vercel, Cloudflare, Cursor, Grok Build

The main improvements over Grok 4.5 are agentic. xAI reports that Grok 4.6 checks its work more often on long trajectories and produces stronger first passes for interactive and visual projects. Reported benchmark results rose from 54% to 65.9% on DeepSWE v1.1 and from 47.1% to 57.5% on APEX-Agents.

If you already integrated Grok 4.5, the API surface is unchanged. Review the Grok 4.5 API guide, then change the model name.

Step 1: Create and store an API key

1. Open console.x.ai and sign in or create an xAI account.
2. Select API Keys in the sidebar.
3. Click Create API key.
4. Name the key by environment, such as grok-dev or grok-prod.
5. Copy the key immediately. xAI displays it only once.

Store the key in an environment variable instead of hard-coding it:

export XAI_API_KEY="your-key-here"

Use separate keys for development and production. Never commit keys to version control. If a key leaks, revoke it in the xAI console and create a replacement.

Step 2: Send your first request with curl

The xAI API follows the OpenAI chat completions format.

curl https://api.x.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -d '{
    "model": "grok-4-6",
    "messages": [
      {
        "role": "system",
        "content": "You are a concise technical assistant."
      },
      {
        "role": "user",
        "content": "Explain idempotency in REST APIs in two sentences."
      }
    ]
  }'

A successful response includes:

• choices: the generated assistant response.
• usage: input and output token counts.

Log the usage object from the start. It is the most direct signal for tracking request cost.

If you get a model not found error, check the models available to your key:

curl https://api.x.ai/v1/models \
  -H "Authorization: Bearer $XAI_API_KEY"

Model IDs can differ by provider. For example, OpenRouter lists the model as x-ai/grok-4.6.

Step 3: Call Grok 4.6 from Python and JavaScript

Because the API is OpenAI-compatible, you can use the official OpenAI SDKs. Set the xAI base URL and use your xAI key.

Python

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ["XAI_API_KEY"],
    base_url="https://api.x.ai/v1",
)

response = client.chat.completions.create(
    model="grok-4-6",
    messages=[
        {
            "role": "system",
            "content": "You are a concise technical assistant.",
        },
        {
            "role": "user",
            "content": "Write a Python function that validates an email address.",
        },
    ],
)

print(response.choices[0].message.content)
print(response.usage)

JavaScript / TypeScript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.XAI_API_KEY,
  baseURL: "https://api.x.ai/v1",
});

const response = await client.chat.completions.create({
  model: "grok-4-6",
  messages: [
    {
      role: "system",
      content: "You are a concise technical assistant.",
    },
    {
      role: "user",
      content: "Write a TypeScript type guard for a User object.",
    },
  ],
});

console.log(response.choices[0].message.content);

This compatibility also makes A/B testing straightforward. If your application already uses the GPT-5.6 API, put the provider and model behind configuration and compare outputs, latency, and token usage.

Step 4: Stream responses

For user-facing applications, enable streaming. It improves perceived latency for long, multi-step responses.

stream = client.chat.completions.create(
    model="grok-4-6",
    messages=[
        {
            "role": "user",
            "content": "Refactor this function and explain each change: ...",
        }
    ],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

Streaming uses server-sent events (SSE). Each update arrives as a separate data: line. Verify that your client, reverse proxy, and deployment platform do not buffer or strip the stream.

When debugging SSE behavior, Apidog renders chunks in real time. That makes it easier to distinguish model latency from client-side buffering or a stalled proxy.

Step 5: Use the 500K context window carefully

A 500,000-token context can hold a mid-sized codebase or several hundred pages of documents. Do not automatically send everything on every request.

Calculate input cost

At $2 per million input tokens, a 500K-token prompt costs approximately $1 before output tokens. For repeated questions over the same corpus:

• Cache reusable context where possible.
• Retrieve only relevant documents or code chunks.
• Avoid resending unchanged reference material.
• Log token usage per workflow or agent step.

Structure long prompts deliberately

For long-context prompts:

1. Put stable instructions at the beginning.
2. Put reference material in the middle.
3. Put the current task or question at the end.

The fast variant costs 2x and is better suited to latency-sensitive flows such as interactive coding assistants. For batch analysis, bulk classification, or overnight jobs, the standard tier is the more obvious choice.

For pricing comparisons with GPT-5.6 and Claude, see the Grok 4.5 pricing breakdown, which still applies structurally to 4.6.

Test the integration properly with Apidog

A successful curl request is only the beginning. Before using Grok 4.6 in production, make requests reproducible, separate environments, and automate failure checks.

Apidog can support that workflow:

1. Create a project and add an environment:

   base_url = https://api.x.ai/v1
   XAI_API_KEY = your-key

Keep development and production keys in separate environments.

1. Create one chat completions request using environment variables:

   POST {{base_url}}/chat/completions
   Authorization: Bearer {{XAI_API_KEY}}
   Content-Type: application/json

1. Add the request body:

   {
     "model": "grok-4-6",
     "messages": [
       {
         "role": "user",
         "content": "Explain this API error."
       }
     ]
   }

1. Inspect streaming responses visually. SSE chunks can reveal stalls, truncation, or buffering issues immediately.

2. Add automated assertions for:

   • A non-empty choices[0].message.content.
   • Token usage within a budget.
   • Response time within your SLA.

3. Mock the endpoint for frontend and agent-loop development. A stable mock lets CI test expected response shapes without consuming live API tokens.

This is especially useful for agent workflows that make many model calls per task. Mock predictable paths in CI, then run a smaller set of live integration tests separately.

Common errors and quick fixes

Error	Likely cause	Fix
401 Unauthorized	Missing or malformed Authorization header	Confirm the Bearer prefix and verify XAI_API_KEY is set in the current shell or runtime.
404 model not found	Wrong model ID for the provider	Call /v1/models. Resellers can use different IDs, such as x-ai/grok-4.6 on OpenRouter.
429 Too Many Requests	Rate limit or exhausted quota	Apply exponential backoff and check usage in console.x.ai.
Truncated output	max_tokens is too low	Increase the limit for long, multi-step answers.
Stalled stream	Client buffering or a proxy strips SSE	Confirm stream: true, disable proxy buffering, and inspect the raw stream in Apidog.

FAQ

Is the Grok 4.6 API OpenAI-compatible?

Yes. The chat completions endpoint accepts the same request shape, and the official OpenAI SDKs work when base_url points to https://api.x.ai/v1.

How much does the Grok 4.6 API cost?

It costs $2 per million input tokens and $6 per million output tokens. The faster variant costs double. You pay for the tokens you send and generate.

Do I need a new integration if I use Grok 4.5?

No. Change the model name. Authentication, request format, and endpoints are unchanged from Grok 4.5.

Can I use Grok 4.6 without an xAI account?

Yes. It is also available through OpenRouter, Vercel AI Gateway, and Cloudflare, each with its own billing. The native xAI API is typically the cheapest path at volume.