What is the difference between DeepSeek V3.2 Speciale and Gemini 3 Pro Preview?

DeepSeek V3.2 Speciale is developed by DeepSeek while Gemini 3 Pro Preview is developed by Google AI. You can compare their actual outputs across 46 challenges on RIVAL to see how they differ in practice.

Which is better, DeepSeek V3.2 Speciale or Gemini 3 Pro Preview?

It depends on your use case. DeepSeek V3.2 Speciale and Gemini 3 Pro Preview each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 46 challenges so you can judge which fits your needs best.

How can I compare DeepSeek V3.2 Speciale and Gemini 3 Pro Preview on RIVAL?

This page shows a side-by-side comparison of DeepSeek V3.2 Speciale and Gemini 3 Pro Preview 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

DeepSeek V3.2 SpecialevsGemini 3 Pro Preview

41 challenges

Favorites

DeepSeek V3.2 SpecialeGemini 3 Pro Preview

Movie

—

The Matrix

19998.2

Album

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The Dark Side

Gregorian

Book

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The Hitch Hiker's Guide to the Galaxy

Douglas Adams

City

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Kyoto

Japan

Game

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Tetris (1984)

Puzzle4.4

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Estimate Complexityreasoning

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

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

Estimating the total number of Floating Point Operations (FLOPs) required to train GPT-3 involves understanding the model's architecture, training process, and computational requirements. Here's a step-by-step breakdown of the reasoning:

1. Understand GPT-3's Architecture

GPT-3 is a transformer-based language model with the following key parameters:

Number of parameters: 175 billion (175 × 10⁹).
Number of layers: 96.
Hidden size: 12,288.
Attention heads: 96.

2. Compute FLOPs per Forward Pass

In a transformer model, the FLOPs per forward pass can be approximated using the formula: [ \text{FLOPs} \approx 2 \times \text{Parameters} \times \text{Sequence Length} ]

Sequence length: GPT-3 uses a sequence length of 2048 tokens.
Parameters: 175 × 10⁹.

So, the FLOPs per forward pass are: [ \text{FLOPs}_{\text{forward}} \approx 2 \times 175 \times 10^9 \times 2048 = 7.168 \times 10^{14} ]

3. Compute FLOPs per Backward Pass

Backpropagation typically requires about twice the FLOPs of a forward pass. Therefore: [ \text{FLOPs}{\text{backward}} \approx 2 \times \text{FLOPs}{\text{forward}} = 2 \times 7.168 \times 10^{14} = 1.4336 \times 10^{15} ]

4. Total FLOPs per Training Step

Each training step consists of one forward pass and one backward pass. Thus: [ \text{FLOPs}{\text{step}} = \text{FLOPs}{\text{forward}} + \text{FLOPs}_{\text{backward}} = 7.168 \times 10^{14} + 1.4336 \times 10^{15} = 2.1504 \times 10^{15} ]

5. Total Training Steps

GPT-3 was trained on 300 billion tokens. With a batch size of 3.2 million tokens per step, the total number of training steps is: [ \text{Steps} = \frac{\text{Total Tokens}}{\text{Batch Size}} = \frac{300 \times 10^9}{3.2 \times 10^6} = 93,750 ]

6. Total FLOPs for Training

Multiply the FLOPs per step by the total number of steps: [ \text{Total FLOPs} = \text{FLOPs}_{\text{step}} \times \text{Steps} = 2.1504 \times 10^{15} \times 93,750 \approx 2.016 \times 10^{20} ]

Final Estimate

The total number of FLOPs required to train GPT-3 is approximately: [ \boxed{2 \times 10^{20} \text{ FLOPs}} ]

This estimate aligns with publicly reported figures and provides a reasonable approximation based on the model's architecture and training process.

Gemini 3 Pro Preview

Google · Nov 2025text

To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a standard approximation formula derived from the architecture of Transformer models.

Here is the step-by-step breakdown of the estimation:

Step 1: Identify the Key Variables

To calculate training compute, we need two primary numbers: the number of parameters in the model ($P$) and the number of tokens the model was trained on ($D$).

Model Size ($P$): GPT-3 is famously known to have 175 billion parameters. $$P = 175 \times 10^9$$
Dataset Size ($D$): According to the original GPT-3 paper ("Language Models are Few-Shot Learners"), the model was trained on a weighted mix of datasets (Common Crawl, WebText2, Books1, Books2, Wikipedia). The total number of tokens processed during training was approximately 300 billion tokens. $$D = 300 \times 10^9$$

Step 2: Apply the Compute Approximation Formula

For dense Transformer models, a widely accepted heuristic (often cited in papers by Kaplan et al. and DeepMind regarding scaling laws) is that training requires approximately 6 FLOPs per parameter per training token.

Forward Pass: Requires $\approx 2P$ FLOPs per token (matrix multiplication involves 1 multiply and 1 add, and we do this for the weights).
Backward Pass (Gradient Calculation): Requires $\approx 4P$ FLOPs per token (roughly twice the cost of the forward pass).
Total: $2P + 4P = 6P$ FLOPs per token.

Therefore, the formula for Total Training Compute ($C$) is: $$C \approx 6 \times P \times D$$

Step 3: Perform the Calculation

Now, substitute the values into the formula:

$$C \approx 6 \times (175 \times 10^9) \times (300 \times 10^9)$$

First, group the integers and the powers of 10: $$C \approx (6 \times 175 \times 300) \times (10^9 \times 10^9)$$

Calculate the integers:

$6 \times 175 = 1,050$
$1,050 \times 300 = 315,000$

Combine with the exponent ($10^{18}$): $$C \approx 315,000 \times 10^{18} \text{ FLOPs}$$

Step 4: Convert to Scientific Notation

To make the number readable, we normalize it:

$$315,000 = 3.15 \times 10^5$$

Combine exponents: $$3.15 \times 10^5 \times 10^{18} = 3.15 \times 10^{23}$$

Final Estimate

The estimated total compute required to train GPT-3 is approximately:

$$3.14 \times 10^{23} \text{ FLOPs}$$

(Note: In the AI literature, this is often converted to "Petaflop-days." One Petaflop-day is approximately $8.64 \times 10^{19}$ FLOPs. Dividing $3.14 \times 10^{23}$ by that number yields roughly 3,640 Petaflop-days, which aligns closely with OpenAI's published figures.)

Vote

2 / 41

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

DeepSeek V3.2 Speciale

Deepseek · Dec 2025svg

Gemini 3 Pro Preview

Google · Nov 2025svg

Vote

3 / 41

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

Alright, let's tackle the problem step by step to ensure we understand it fully and arrive at the correct answer.

Problem Statement: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?

Understanding the Problem: We need to determine the number of sisters Sally has, given the information about her brothers and their sisters.

Breaking It Down:

Sally's Brothers:
- Sally has 3 brothers.
Brothers' Sisters:
- Each brother has 2 sisters.

Key Point to Note: When it's mentioned that each brother has 2 sisters, it's important to recognize that Sally herself is one of those sisters.

Let's Visualize the Family:

Total Brothers: 3
Sisters per Brother: 2

Since Sally is one of the sisters, the total number of sisters includes her and possibly others.

Calculating the Number of Sisters:

Each of the 3 brothers has 2 sisters. Since Sally is one sister common to all brothers, we can determine how many additional sisters there are.

Let's denote:

Total sisters = Number of sisters each brother has

But we know that Sally is one of those sisters. Therefore:

Total sisters = Sally + (Number of additional sisters)

Given that each brother has 2 sisters, and Sally is one of them, there must be one more sister.

So, Total sisters = 1 (Sally) + 1 (additional sister) = 2 sisters

Verification:

Let's verify this to ensure accuracy.

Sally has 3 brothers.
Each brother has 2 sisters: Sally and one more sister.
Therefore, the total number of sisters is 2 (Sally and her sister).

This aligns with our calculation.

Common Pitfall:

Gemini 3 Pro Preview

Google · Nov 2025website

Vote

12 / 41

Pokémon Battle UI Recreationweb design

Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.

DeepSeek V3.2 Speciale

Deepseek · Dec 2025website

No content available for this output.

Gemini 3 Pro Preview

Google · Nov 2025website

No content available for this output.

Vote

12 of 41

FAQ

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To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a standard approximation formula derived from the architecture of Transformer models.

Here is the step-by-step breakdown of the estimation:

Step 1: Identify the Key Variables

To calculate training compute, we need two primary numbers: the number of parameters in the model ($P$) and the number of tokens the model was trained on ($D$).

Model Size ($P$): GPT-3 is famously known to have 175 billion parameters. $$P = 175 \times 10^9$$
Dataset Size ($D$): According to the original GPT-3 paper ("Language Models are Few-Shot Learners"), the model was trained on a weighted mix of datasets (Common Crawl, WebText2, Books1, Books2, Wikipedia). The total number of tokens processed during training was approximately 300 billion tokens. $$D = 300 \times 10^9$$

Step 2: Apply the Compute Approximation Formula

Forward Pass: Requires $\approx 2P$ FLOPs per token (matrix multiplication involves 1 multiply and 1 add, and we do this for the weights).
Backward Pass (Gradient Calculation): Requires $\approx 4P$ FLOPs per token (roughly twice the cost of the forward pass).
Total: $2P + 4P = 6P$ FLOPs per token.

Therefore, the formula for Total Training Compute ($C$) is: $$C \approx 6 \times P \times D$$

Step 3: Perform the Calculation

Now, substitute the values into the formula:

$$C \approx 6 \times (175 \times 10^9) \times (300 \times 10^9)$$

First, group the integers and the powers of 10: $$C \approx (6 \times 175 \times 300) \times (10^9 \times 10^9)$$

Calculate the integers:

$6 \times 175 = 1,050$
$1,050 \times 300 = 315,000$

Combine with the exponent ($10^{18}$): $$C \approx 315,000 \times 10^{18} \text{ FLOPs}$$

Step 4: Convert to Scientific Notation

To make the number readable, we normalize it:

$$315,000 = 3.15 \times 10^5$$

Combine exponents: $$3.15 \times 10^5 \times 10^{18} = 3.15 \times 10^{23}$$

Final Estimate

The estimated total compute required to train GPT-3 is approximately:

$$3.14 \times 10^{23} \text{ FLOPs}$$