What is the difference between Grok 4.20 Multi-Agent Beta and Gemini 2.5 Pro Preview 06-05?

Grok 4.20 Multi-Agent Beta is developed by xAI while Gemini 2.5 Pro Preview 06-05 is developed by Google AI. Grok 4.20 Multi-Agent Beta has a 2.0M token context window vs Gemini 2.5 Pro Preview 06-05's 1.0M. You can compare their actual outputs across 38 challenges on Rival to see how they differ in practice.

Which is better, Grok 4.20 Multi-Agent Beta or Gemini 2.5 Pro Preview 06-05?

It depends on your use case. Grok 4.20 Multi-Agent Beta and Gemini 2.5 Pro Preview 06-05 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 38 challenges so you can judge which fits your needs best.

How much does Grok 4.20 Multi-Agent Beta cost compared to Gemini 2.5 Pro Preview 06-05?

Grok 4.20 Multi-Agent Beta costs $2/M input tokens and Gemini 2.5 Pro Preview 06-05 costs $1.25/M input tokens. Gemini 2.5 Pro Preview 06-05 is $0.75/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare Grok 4.20 Multi-Agent Beta and Gemini 2.5 Pro Preview 06-05 on Rival?

This page shows a side-by-side comparison of Grok 4.20 Multi-Agent Beta and Gemini 2.5 Pro Preview 06-05 across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

Updated Mar 12, 2026

Grok 4.20 Multi-Agent BetavsGemini 2.5 Pro Preview 06-05

38 fights queued

Why Grok 4.20 Multi-Agent Beta?

1.5x cheaper overall$2.00/M in · $6.00/M out

1.9x more context2.0M

9 months newerMar 2026

Why Gemini 2.5 Pro Preview 06-05?

Dead even. This one's a coin flip.

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19998.2

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12 / 38

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

Grok 4.20 Multi-Agent Beta

Xai · Mar 2026text

3.15 \times 10^{23} FLOPs

GPT-3 (the 175B-parameter version) has N ≈ 1.75 × 10^{11} parameters. It was trained on a filtered dataset of roughly D ≈ 3 × 10^{11} tokens (a commonly cited figure drawn from Common Crawl, WebText, books, and Wikipedia, with some up-sampling of higher-quality sources).

For dense transformer models the dominant training-cost term is the forward + backward passes through the layers. A widely used approximation is:

Total training FLOPs ≈ 6 × N × D

~2N FLOPs per token for the forward pass (matrix multiplies in attention and FFN layers).
~4N FLOPs per token for the backward pass (gradient computations).

Multiplying gives:

6 × 1.75 × 10^{11} × 3 × 10^{11} = 6 × 5.25 × 10^{22} = 3.15 × 10^{23}

This figure matches the number most often quoted for GPT-3 in the literature and in scaling-law discussions. Minor variations (±10–20 %) can arise from exact token count, optimizer overhead, or mixed-precision implementation details, but the order-of-magnitude estimate remains ~3 × 10^{23} FLOPs.

Grok 4.20 Multi-Agent Beta vs Gemini 2.5 Pro Preview 06-05

Which is better, Grok 4.20 Multi-Agent Beta or Gemini 2.5 Pro Preview 06-05?