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grok-4.20-multi-agent-beta-0309 is xAI’s multi-agent collaboration model: a single request spins up multiple agents working in parallel internally (the model calls itself Oppie, a “collaborative AI team leader”), with a lead agent synthesizing the final answer. It suits complex research and multi-angle comparative analysis. Available on APIYI in the standard OpenAI-compatible format.

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Billing Profile (Read This First)

All internal agent traffic lands on your bill. This is the biggest difference from regular models:
  • A measured ordinary prompt of ~40 tokens actually billed 39,263 prompt tokens + 9,997 completion tokens (all internal multi-agent round-trips included)
  • Even the simplest one-liner request carries a fixed overhead of roughly 3,900 prompt tokens
  • The unit price matches grok-4.3 ($1.25 / $2.50 per 1M tokens), but a single request can cost tens of times more than a regular model
Do not use this model for simple tasks — use grok-4.3 or grok-4.20-0309-reasoning for ordinary Q&A.
The good news: internal traffic gets high cache hit rates (26.8K of the measured 39K prompt tokens hit the discounted cache rate), so real cost runs below a naive token-count conversion — but still far above regular models.

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How to Call It

Identical to any other model — only the model field differs: 
from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key",
    base_url="https://api.apiyi.com/v1",
)

resp = client.chat.completions.create(
    model="grok-4.20-multi-agent-beta-0309",
    messages=[{
        "role": "user",
        "content": "Compare Rust and Go for building highly concurrent network services: 3 points each, then a one-line verdict"
    }],
)
print(resp.choices[0].message.content)
print("Billed tokens:", resp.usage.total_tokens)
Multi-agent orchestration is entirely server-side — no extra parameters. Streaming and Structured Outputs (json_schema) are verified working too.

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Measured Characteristics (2026-07-13)

DimensionMeasured
Medium-complexity task latency~29 s
Simple Q&A latency~5 s
Simple Q&A fixed overhead~3,900 prompt tokens
Medium task token consumption~39K prompt + ~10K completion
Internal cache hits~2/3 of prompt tokens at the discounted cache rate
Chain-of-thought exposureNot exposed (reasoning_tokens still billed)

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When to Use It

Good Fit

Multi-angle deep comparative analysis, complex research questions, open-ended tasks that benefit from several lines of thought cross-validating each other — parallel agent exploration meaningfully improves answer completeness.

Poor Fit

Everyday Q&A, translation, summarization, code completion — single-track tasks where output quality is close to regular models but cost is amplified tens of times. Use grok-4.3 / grok-4.5 for these.
Before going live, compare output quality between grok-4.20-0309-reasoning and the multi-agent model on a handful of real tasks, then decide whether the quality delta justifies the billing amplification — for most scenarios, the reasoning variant is enough.

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FAQ

It’s the model’s built-in persona (the leader role of the multi-agent team) — entirely normal. Verify model identity via the model field in your request and response.
No. Multi-agent orchestration happens inside xAI’s servers, with no control parameters exposed.
Set it generously (e.g. 8192) — the model’s internal reasoning is token-hungry, and a small budget easily truncates the final answer.

Grok Overview

Full model lineup and pricing

Chat & Reasoning

Chain-of-thought and billing for the regular models