What makes Grok-4.20 different from previous Grok models?

Grok-4.20 introduces a multi-agent reasoning system where several agents analyze a prompt simultaneously and collaborate on a final answer, improving complex reasoning and coding performance.

How large is the context window in the Grok-4.20 API?

Grok-4.20 supports up to a 2,000,000-token context window, allowing developers to process extremely long documents or datasets in a single request.

Can Grok-4.20 handle multimodal inputs such as images or video?

Yes. Grok-4.20 supports multimodal inputs including text, images, and video, enabling analysis of mixed content within a single conversation.

How does Grok-4.20 compare with GPT-5.2 or Gemini models?

Grok-4.20 focuses on multi-agent reasoning and very long context windows, while GPT-5.2 emphasizes high-accuracy reasoning and Gemini models focus on multimodal integration within the Google ecosystem.

Is Grok-4.20 available through an API for developers?

Yes. Grok-4.20 is available through the Come API.

What benchmarks show Grok-4.20 performance?

Early reports place Grok-4.20 around 1505–1535 ELO on LMSYS Arena and strong results in real-world competitions such as Alpha Arena trading simulations.

Affordable Grok 4.20 API | text-to-text

Technical Specifications of Grok-4.20

Item	Grok-4.20 (public specs)
Model family	Grok-4 series
Developer	xAI
Release status	Beta (first rollout Feb 17, 2026)
Input types	Text, Image, Video
Output types	Text outputs (structured outputs & function/tool calling supported).
Context window	Up to 2,000,000 tokens
Architecture	Multi-agent collaborative reasoning
Tool support	Function calling, structured outputs
Reasoning	Built-in reasoning capabilities
Training infrastructure	Colossus supercluster (~200,000 GPUs)
Model variants	grok-4.20-multi-agent-beta-0309, grok-4.20-beta-0309-reasoning, grok-4.20-beta-0309-non-reasoning.

What is Grok-4.20

Grok-4.20 is the latest experimental release in the Grok-4 family developed by xAI. It focuses on agentic reasoning, extremely long context handling, and high-speed inference, aiming to deliver precise answers with a lower hallucination rate than earlier Grok models.

Unlike earlier Grok models that used single-model inference, Grok-4.20 introduces multi-agent collaboration, where several internal agents analyze a prompt simultaneously and converge on a final answer. This architecture is designed to improve performance on complex reasoning, coding, and research tasks.

Main Features of Grok-4.20

Ultra-long context window (2M tokens): Enables processing of entire books, large datasets, or long coding repositories in a single prompt.
Multi-agent reasoning architecture: Up to four internal agents can analyze a prompt in parallel and debate solutions before producing a final answer.
Agentic tool calling and structured outputs: Supports function calling and structured responses for integration with applications and automated workflows.
Multimodal understanding: Accepts text, image, and video inputs within the same model pipeline.
Fast inference with low hallucination focus: xAI positions the model as optimized for truthful answers and strong prompt adherence.

Benchmark Performance of Grok-4.20

Public benchmark data is still limited during beta, but early reports indicate:

Benchmark	Result / Status
LMSYS Chatbot Arena	Estimated ELO ~1505–1535
ForecastBench	Ranked #2 in early tests
Alpha Arena trading challenge	Achieved +34.59% returns

These numbers suggest Grok-4.20 competes with frontier models in real-world reasoning and agent-driven tasks rather than simple benchmark questions.

Grok-4.20 Beta vs Other Frontier Models

Model	Developer	Context Window	Key Strength
Grok-4.20	xAI	2M tokens	Multi-agent reasoning
GPT-5.2	OpenAI	~400K tokens	Advanced reasoning + coding
Gemini 3 Pro	Google	~1M tokens	multimodal and Google ecosystem
Claude 4 Opus	Anthropic	~200K+ tokens	reliable reasoning

Key differences

Grok-4.20 emphasizes multi-agent collaboration for reasoning tasks.
It provides one of the largest context windows in production LLMs (2M tokens).
Competing models may outperform Grok in certain areas such as structured reasoning or creative writing depending on evaluation tasks.

Representative Use Cases

Long-context research analysis
Process large documents, legal materials, or academic research.
Agentic automation systems
Build multi-step workflows where the model plans and executes tasks.
Advanced coding and simulations
Solve engineering problems or simulate systems with long reasoning chains.
Data analysis and dashboard automation
Track and analyze multiple streams of data in parallel.
Multimodal knowledge processing
Interpret images, video frames, and text in a unified reasoning process.

How to access and use Grok 4.2 API

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to `Grok 4.2` API

Select the “grok-4.20-0309-reasoning” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Chat format.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

Pricing for Grok 4.20

Explore competitive pricing for Grok 4.20, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how Grok 4.20 can enhance your projects while keeping costs manageable.

Comet Price (USD / M Tokens)	Official Price (USD / M Tokens)	Discount
Input:$1/M Output:$2/M	Input:$1.25/M Output:$2.5/M	-20%

Sample code and API for Grok 4.20

Access comprehensive sample code and API resources for Grok 4.20 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Grok 4.20 in your projects.

Python
JavaScript
Curl

import os

from openai import OpenAI

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)
response = client.responses.create(
    model="grok-4.20-multi-agent-beta-0309",
    input=[
        {
            "role": "user",
            "content": "Research the latest breakthroughs in quantum computing and summarize the key findings.",
        }
    ],
    tools=[{"type": "web_search"}, {"type": "x_search"}],
)

print(response.output_text or response.model_dump_json(indent=2))

Python Code Example

import os

from openai import OpenAI

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)
response = client.responses.create(
    model="grok-4.20-multi-agent-beta-0309",
    input=[
        {
            "role": "user",
            "content": "Research the latest breakthroughs in quantum computing and summarize the key findings.",
        }
    ],
    tools=[{"type": "web_search"}, {"type": "x_search"}],
)

print(response.output_text or response.model_dump_json(indent=2))

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const apiKey = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const baseUrl = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey,
  baseURL: baseUrl,
});

const response = await client.responses.create({
  model: "grok-4.20-multi-agent-beta-0309",
  input: [
    {
      role: "user",
      content: "Research the latest breakthroughs in quantum computing and summarize the key findings.",
    },
  ],
  tools: [{ type: "web_search" }, { type: "x_search" }],
});

console.log(response.output_text ?? JSON.stringify(response.output, null, 2));

Curl Code Example

#!/usr/bin/env bash
# Get your CometAPI key from https://api.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

response=$(curl --silent --location --request POST "https://api.cometapi.com/v1/responses" \
  --header "Authorization: Bearer $COMETAPI_KEY" \
  --header "Content-Type: application/json" \
  --header "Accept: application/json" \
  --data-raw '{
    "model": "grok-4.20-multi-agent-beta-0309",
    "input": [
      {
        "role": "user",
        "content": "Research the latest breakthroughs in quantum computing and summarize the key findings."
      }
    ],
    "tools": [
      {"type": "web_search"},
      {"type": "x_search"}
    ]
  }')

if command -v jq >/dev/null 2>&1; then
  printf '%s\n' "$response" | jq -r '(
    [
      .output[]?
      | select(.type == "message")
      | .content[]?
      | select(.type == "output_text")
      | .text
    ][0]
  ) // .output_text // .'
else
  printf '%s\n' "$response"
fi

Versions of Grok 4.20

The reason Grok 4.20 has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation.

Model id	description	Availability	Request
grok-4.20-multi-agent-beta-0309	Multi-agent variant tuned for realtime agent orchestration and tool calling (useful for deep research workflows where multiple sub-agents perform web searches, code execution, and critique).	✅	response format calls.
grok-4.20-0309-reasoning	Reasoning-optimized variant: prioritizes deeper chain-of-thought style reasoning and higher benchmark scores on reasoning-heavy tests; higher latency/cost per token expected versus non-reasoning variants.	✅	chat format call and response format calls.
grok-4.20-0309-non-reasoning	Lower-latency / lower-cost variant for high-throughput tasks where deterministic, short answers or streaming outputs are the priority; tradeoffs include lower reasoning benchmark scores.	✅	chat format call and response format calls.