Grok 4.1 Fast

grok-4-1-fast

Grok 4.1 Fast is xAI's best agentic tool-calling model, excelling in real-world use cases such as customer support and deep research. 2M context window.

Context window2.0M

ProviderGrok

Released2026/04/12

Playground

Pricing

The same model is available through multiple service channels — choose based on latency, reliability and cost.

Prices in $ / 1M tokens

To pick a channel, add a provider field to the request body, for example "provider": { "channel": "direct" }. Valid values are direct / stable / economical; omit it to use the default channel.

Direct

Direct upstream connection — best when you need native behavior and the full context window.

Input	Output	Cache read
0.20/M	0.50/M	0.05/M

Overview

Input

Text Image

Output

Text

Grok 4.1 Fast API: Agentic Tool-Calling Model with 2-Million-Token Context

Grok 4.1 Fast is xAI's model optimized for agentic tool calling, delivering outstanding performance in real-world business scenarios such as customer support and deep research. Its biggest highlight is the 2-million-token ultra-long context window — with almost no competitors in the same price range.

This combination is highly practical: an ultra-long context means an entire knowledge base, complete ticket history, or large batches of retrieval results can be fed in directly, with no need for complex chunking or vector retrieval; and the "Fast" positioning means the cost and latency of doing so stay within acceptable limits.

SeaWhale AI offers Grok 4.1 Fast through an OpenAI-compatible interface, with support for tool calling, streaming output, and multimodal input.

Get API Key · Model ID: grok-4-1-fast

Why Choose Grok 4.1 Fast

2-million-token context — an entire knowledge base or complete ticket history fits in one go
xAI's strongest agentic tool-calling model — specifically optimized for tool orchestration scenarios
Proven strength in customer service and deep research — outstanding in these two real-world business scenarios
Fast positioning — ultra-long context while keeping latency and cost acceptable
Image input support — screenshots and charts can be included directly as part of the context

Core Capabilities

01 Ultra-Long Context (2 Million Tokens)

2 million tokens let many scenarios that previously required a RAG pipeline be simplified directly: put the entire product documentation, complete conversation history, and all retrieval results into the context at once, and let the model find the relevant content itself.

Full knowledge base injected directly
Complete retention of long tickets and conversation history
Eliminates the engineering complexity of chunking and vector retrieval

02 Agentic Tool Calling

Grok 4.1 Fast is xAI's model optimized for tool calling. It performs reliably in multi-tool orchestration, parameter construction, and result handling, making it well suited as the execution core of an agentic framework.

Parallel invocation of multiple tools
Complex parameter construction and validation
Tool-result parsing and subsequent decision-making

03 Customer Support Scenarios

In customer service scenarios, the ultra-long context combined with reliable tool calling is a powerful combination: complete user history + knowledge base + order lookup tools can directly support high-quality automated responses.

Full user history context
Knowledge base retrieval and ticket tool calling
Consistency across multi-turn conversations

04 Deep Research

In research tasks that require spanning large volumes of material, the 2-million-token context lets the model "see" all the material at once, reducing the risk of information being lost in chunked retrieval.

Cross-document reasoning over large-scale material
Full synthesis of search results
Long-report writing

Best Use Cases

Scenario	Description
Intelligent customer service	Full user history + knowledge base + tool calling
Deep research	Comprehensive analysis and report writing across large volumes of material
Knowledge base Q&A	Direct injection of complete documents, no RAG pipeline needed
Agent execution layer	Multi-tool orchestration and parameter construction
Long-session products	Full conversation history without compression
Large-scale log analysis	Process ultra-long logs and trace data in one pass

Grok 4.1 Fast vs. Grok 4.5 and Grok 4.20

Capability	Grok 4.1 Fast	Grok 4.5	Grok 4.20
Model ID	`grok-4-1-fast`	`grok-4.5`	`grok-4.20`
Positioning	Agentic tool calling + ultra-long context	Frontier coding and knowledge work	Previous-generation flagship
Context window	2 million tokens	500K tokens	2 million tokens
Max output	2 million tokens	128K tokens	2 million tokens
Reasoning depth	Medium (speed-first)	High	High
Best for	Customer service, research, tool orchestration	Long-session coding, STEM	General frontier tasks

Billing is based on the real-time price card at the top of the page.

How to Use the Grok 4.1 Fast API

1. Create a SeaWhale AI API key Generate a key in the console and top up your balance.

2. Make good use of the ultra-long context If your current architecture includes a RAG pipeline built to get around context limits, try feeding all the content directly into Grok 4.1 Fast — in many cases it works better and is simpler from an engineering perspective.

3. Call the API

curl -X POST https://api.atalk-ai.com/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "grok-4-1-fast",
    "messages": [
      {"role": "system", "content": "Here is the complete product knowledge base..."},
      {"role": "user", "content": "A user reported that their order keeps showing as pending shipment. How should I handle it?"}
    ],
    "tools": [{"type": "function", "function": {"name": "query_order", "parameters": {}}}],
    "stream": true
  }'

FAQ

Can the 2-million-token context really be used to its full extent? Yes, but note that cost grows linearly with input length. In practice, the typical approach is to inject the most relevant large chunks of content in full, rather than stuffing in all history indiscriminately.

How do I choose between this and Grok 4.5? Choose Grok 4.5 if you need the strongest coding and reasoning capabilities; choose Grok 4.1 Fast if you need ultra-long context and high-frequency tool calling and are cost-sensitive.

Can it replace RAG? In scenarios where the knowledge base is moderately sized (can fit within 2 million tokens), it can significantly simplify the architecture. When the knowledge base is extremely large, a retrieval layer is still needed, but you can be much more generous with the number of retrieved results.

What input modalities are supported? Text and image. Tool calling and streaming output are both available.

Is it suitable as the core of an agentic framework? Yes. It is itself a model optimized for agentic tool calling, and multi-tool orchestration scenarios are its home turf.

How is the latency? The "Fast" positioning means latency is better than deep-reasoning tiers at the same context length, but the longer the input, the higher the time-to-first-token, so keep the injected amount reasonable.

Why Use Grok 4.1 Fast API with SeaWhale AI

Direct connection in China — no overseas account or self-hosted proxy required
OpenAI-compatible — integrate with just two lines of code changed
Free switching between multiple models — switch to Grok 4.5 or other providers' models anytime you need stronger reasoning
Unified billing and usage — costs for long-context scenarios are clear at a glance

API

API integration

Model IDUse this value as the model in inference requests

grok-4-1-fast

API KeyBearer token used to authenticate inference requests

Manage API keys

Base URLOpenAI compatible · /chat/completions

https://api.atalk-ai.com/v2/

provider OptionalSelects a service channel; omit it and the system picks the default

"provider": { "channel": "direct" }

grok-4-1-fast usage examples

SeaWhale AI is compatible with the OpenAI API protocol, so you can call it with the OpenAI SDK or plain HTTP requests. Streaming is enabled by default.

About the provider parameter (optional, a SeaWhale AI extension): most models are served over several channels that differ slightly in price and reliability. Add a provider field to the request body to pick one; omit it and the system selects the default channel — normal calls are unaffected.

provider is not part of the official OpenAI protocol — it is a SeaWhale AI extension that only takes effect on this platform. The OpenAI SDK allows custom fields like this to pass through; see the examples below.

Value	Channel	Best for
`direct`	Direct	The official upstream link, for native behavior and the full context window
`stable`	Preferred	Balanced availability and speed — a good default for production traffic
`economical`	Economy	Cost first, well suited to batch processing and price-sensitive workloads

Available channels and their prices are listed under "Pricing" above (channels vary by model). Additional notes:

Syntax: "provider": { "channel": "direct" }.
If the requested channel is not enabled for that model, the request automatically falls back to the default channel and returns normally — it does not error.
With the official SDKs: in Python pass it via extra_body; in Node.js put it directly on the request object and it passes through. In TypeScript projects, add a // @ts-expect-error line to skip the type check.

cURLPythonNode.js

curl https://api.atalk-ai.com/v2/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API_KEY>" \
  -d '{
    "model": "grok-4-1-fast",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "provider": { "channel": "direct" },
    "stream": true
  }'
# provider is optional — remove this line to use the default channel

from openai import OpenAI

client = OpenAI(
    base_url="https://api.atalk-ai.com/v2",
    api_key="<API_KEY>",
)

stream = client.chat.completions.create(
    model="grok-4-1-fast",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
    stream=True,
    # Optional: pick a service channel; omit to use the default
    extra_body={"provider": {"channel": "direct"}},
)

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

import OpenAI from 'openai'

const client = new OpenAI({
  baseURL: 'https://api.atalk-ai.com/v2',
  apiKey: '<API_KEY>',
})

const stream = await client.chat.completions.create({
  model: 'grok-4-1-fast',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' },
  ],
  stream: true,
  // Optional: pick a service channel; omit to use the default
  // @ts-expect-error provider is a SeaWhale AI extension, not in the OpenAI SDK types
  provider: { channel: 'direct' },
})

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '')
}