LiteLLMModels

Grok 4.20 Multi Agent Experimental Beta API Pricing

As of October 10, 2026, Grok 4.20 Multi Agent Experimental Beta costs $1.25 per 1M input tokens and $2.50 per 1M output tokens on xAI, with cache reads at $0.20 per 1M tokens. It has a 1,000,000-token context window and returns up to 1,000,000 tokens per response.

By xAIModel ID xai/grok-4.20-multi-agent-experimental-beta-0304Prices updated Checked
Input
$1.25 / 1M tokens
Output
$2.50 / 1M tokens
Cache read
$0.20 / 1M tokens
Context window
1,000,000 tokens
Max output
1,000,000 tokens
Providers
1
Added to LiteLLM
September 22, 2026

Grok 4.20 Multi Agent Experimental Beta pricing by provider

2 listings across 1 provider. Token prices are in US dollars per 1M tokens. The highlighted row is the price quoted above.

ProviderModel name in LiteLLMInputOutputCache readContext
xAIsnapshot 0304xai/grok-4.20-multi-agent-experimental-beta-0304$1.25$2.50$0.201M
xAIlatest aliasxai/grok-4.20-multi-agent-experimental-beta-latest$1.25$2.50$0.201M

Other Grok 4.20 Multi Agent Experimental Beta prices on xAI

Input above 200K tokens$2.50 per 1M tokens
Output above 200K tokens$5.00 per 1M tokens
Cache read above 200K tokens$0.40 per 1M tokens

What Grok 4.20 Multi Agent Experimental Beta costs in practice

1,000 requests with 2,000 input and 500 output tokens each

xAI$3.75

100 long-document requests with 100,000 input and 2,000 output tokens each

xAI$13.00

Grok 4.20 Multi Agent Experimental Beta features

Use Grok 4.20 Multi Agent Experimental Beta with LiteLLM

Call Grok 4.20 Multi Agent Experimental Beta through the LiteLLM Python SDK, or put it behind the LiteLLM proxy and send OpenAI-format requests to /v1/responses. LiteLLM tracks the cost of every request at these prices.

Python SDK
from litellm import completion

response = completion(
    model="xai/grok-4.20-multi-agent-experimental-beta-0304",
    messages=[{"role": "user", "content": "Hello!"}],
)
Proxy config.yaml
model_list:
  - model_name: grok-4.20-multi-agent-experimental-beta
    litellm_params:
      model: xai/grok-4.20-multi-agent-experimental-beta-0304
      api_key: os.environ/XAI_API_KEY
Call the proxy
curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "grok-4.20-multi-agent-experimental-beta",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Grok 4.20 Multi Agent Experimental Beta FAQ

How much does Grok 4.20 Multi Agent Experimental Beta cost?

Grok 4.20 Multi Agent Experimental Beta costs $1.25 per 1M input tokens and $2.50 per 1M output tokens on xAI, with cache reads at $0.20 per 1M tokens. Above 200K input tokens, input costs $2.50 and output costs $5.00 per 1M tokens.

What is the context window of Grok 4.20 Multi Agent Experimental Beta?

Grok 4.20 Multi Agent Experimental Beta has a 1,000,000-token context window and returns up to 1,000,000 tokens per response.

Which providers offer Grok 4.20 Multi Agent Experimental Beta?

Grok 4.20 Multi Agent Experimental Beta is available from xAI through LiteLLM, using the model name xai/grok-4.20-multi-agent-experimental-beta-0304.

How do I call Grok 4.20 Multi Agent Experimental Beta with an OpenAI-compatible API?

Use the LiteLLM Python SDK with model="xai/grok-4.20-multi-agent-experimental-beta-0304", or add the model to the LiteLLM proxy's config.yaml and send requests to /v1/responses from any OpenAI client. LiteLLM tracks the cost of every request at the prices on this page.

What features does Grok 4.20 Multi Agent Experimental Beta support?

Grok 4.20 Multi Agent Experimental Beta supports structured output, image input, reasoning, prompt caching and web search. It does not support function calling.

When did LiteLLM add Grok 4.20 Multi Agent Experimental Beta?

Grok 4.20 Multi Agent Experimental Beta was added to LiteLLM's model price file on September 22, 2026.

Sources

Prices come from LiteLLM's open-source model_prices_and_context_window.json, which LiteLLM uses to calculate the cost of every request. Provider pricing pages:

LiteLLM docs: xAI.

Provider pages: xAI.