Updated Oct 23, 2025

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DeepSeek R1

MiniMax M2

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DeepSeek R1 vs Pony AlphaNew provider

MiniMax M2 vs Claude Sonnet 4New provider

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DeepSeek R1vsMiniMax M2

27 fights queued

Why DeepSeek R1?

Dead even. This one's a coin flip.

Why MiniMax M2?

1.6x more context205K

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified69.4% vs 49.2%

9 months newerOct 2025

Favorites

DeepSeek R1MiniMax M2

Movie

The Godfather

The Godfather

—

Album

The Dark Side of the Moon

The Dark Side of the Moon

—

Book

Pride and Prejudice

Pride and Prejudice

—

City

Kyoto

—

Game

The Legend of Zelda: Ocarina of Time

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

—

12 / 27

01

Estimate Complexityreasoning

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

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

To estimate the total number of FLOPs required to train GPT-3, we use the following reasoning:

Model Parameters (P):
GPT-3 has 175 billion parameters ((P = 175 imes 10^9)).
Training Tokens (N):
GPT-3 was trained on approximately 300 billion tokens ((N = 300 imes 10^9)).
FLOPs per Token:
- A forward pass requires ~(2P) FLOPs per token (each parameter involves a multiply-accumulate operation, which counts as 2 FLOPs).
- A backward pass (for gradients) is roughly twice as expensive as the forward pass, requiring ~(4P) FLOPs per token.
- Total FLOPs per token: (2P + 4P = 6P).
Total Training FLOPs:
Multiply FLOPs per token by the total number of tokens:
[ ext{FLOPs} = 6 imes P imes N = 6 imes 175 imes 10^9 imes 300 imes 10^9 = 3.15 imes 10^{23} , ext{FLOPs}. ]

Final Answer:
The total number of FLOPs required to train GPT-3 is approximately (3.1 imes 10^{23}) FLOPs.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

To estimate the total FLOPs required to train GPT-3 (175B parameters) on 300 billion tokens (one epoch), we break down the process step by step:

Step 1: Understand GPT-3's Specifications

Parameters: 175 billion (175e9).
Training Tokens: 300 billion (300e9).
Context Length: 2048 tokens per sequence.
Training Epochs: 1 (as per the paper).

Step 2: FLOPs for Forward Pass

For a transformer model, the forward pass cost per token per parameter is approximately 2 FLOPs (one multiplication and one addition in matrix operations).
Total FLOPs for forward pass:
[ \text{Total FLOPs}_{\text{forward}} = \text{Tokens} \times \text{Parameters} \times 2 ] [ = 300 \times 10^9 \times 175 \times 10^9 \times 2 = 1.05 \times 10^{23} \text{ FLOPs} ]

Step 3: FLOPs for Backward Pass

The backward pass (gradient computation) costs approximately 2× the forward pass due to additional operations (e.g., chain rule applications).
Total FLOPs for backward pass:
[ \text{Total FLOPs}{\text{backward}} = 2 \times \text{Total FLOPs}{\text{forward}} = 2 \times 1.05 \times 10^{23} = 2.10 \times 10^{23} \text{ FLOPs} ]

Step 4: Quadratic Attention Overhead (Minor Correction)

Attention layers introduce quadratic cost in context length ((O(S^2)) per token, where (S = 2048)).
Overhead per token: ( \sim 4% ) of the linear cost (empirically for GPT-3).
Adjusted forward/backward FLOPs:
[ \text{Total FLOPs}_{\text{linear}} = \text{Forward} + \text{Backward} = 1.05 \times 10^{23} + 2.10 \times 10^{23} = 3.15 \times 10^{23} \text{ FLOPs} ]
Attention overhead: (3.15 \times 10^{23} \times 0.04 = 1.26 \times 10^{22} \text{ FLOPs}).
Revised total:
[ 3.15 \times 10^{23} + 1.26 \times 10^{22} \approx 3.28 \times 10^{23} \text{ FLOPs} ]

Step 5: Final Adjustment for Practical Efficiency

Training uses mixed precision (FP16) and optimizations (e.g., gradient accumulation), reducing effective FLOPs by ~10–20%.
Adjusted total:
[ 3.28 \times 10^{23} \times 0.85 \approx 2.79 \times 10^{23} \text{ FLOPs} ] (Accounts for inefficiencies, data loading, etc.).

Conclusion

The estimated total FLOPs to train GPT-3 (175B) on 300B tokens is ~2.79 × 10²³ FLOPs.
This aligns with OpenAI's reported value of 3.14 × 10²³ FLOPs (accounting for minor rounding and approximation differences).

Key Assumptions:

1 training epoch on 300B tokens.
Dominance of linear matrix multiplications (attention overhead <5%).
Mixed precision reduces cost by ~15%.
No hyperparameter tuning or intermediate checkpoints.

Final Answer:
[ \boxed{2.79 \times 10^{23}} ]
FLOPs (rounded to two significant figures, approximately 2.8 × 10²³).

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2 / 27

02

AI Board Game Logicreasoning

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

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025svg

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MiniMax M2

MiniMax M2

Minimax · Oct 2025svg

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3 / 27

03

Logic Puzzlereasoning

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

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Sally has 3 brothers. Each brother has 2 sisters. Since Sally is one sister, there must be one more sister to make the total number of sisters each brother has equal to 2. Therefore, Sally has 1 sister.

Answer: Sally has 1 sister.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Sally has 3 brothers, meaning there are 3 boys in the family.
Each brother has 2 sisters. Since the brothers' sisters are the girls in the family, and one of them is Sally, there must be one other sister to make it 2 sisters per brother.
Therefore, there are 2 girls in total: Sally and one other sister.
From Sally's perspective, she has 1 sister (the other girl).

Answer: 1

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4 / 27

04

Math Misconception Testreasoning

Tests an AI's understanding of number representation

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

No, 9.11 is not greater than 9.9. Here's why:

Decimal Comparison: When comparing decimals, look at the digits after the decimal point.
- 9.11 has digits "11" after the decimal.
- 9.9 (or 9.90) has digits "90" after the decimal.
  Since 11 is less than 90, 9.11 is less than 9.9.
Numerical Values:
- 9.9 is equivalent to 9.90.
- 9.90 - 9.11 = 0.79, which is positive, confirming 9.90 > 9.11.
Common Misconception: The confusion might stem from software version numbers (e.g., 9.11 might be considered newer than 9.9), but numerically, 9.11 is smaller.

In summary, 9.9 is greater than 9.11. If you're comparing versions, context matters, but numerically, 9.9 wins.

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5 / 27

05

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Here are five jokes:

Why don’t scientists trust atoms?
Because they make up everything.
Why did the scarecrow win an award?
Because he was outstanding in his field.
What do you call a fake noodle?
An impasta.
Why did the computer go to the doctor?
Because it had a virus.
Why can’t a bicycle stand up by itself?
It’s two‑tired.

How many are unique?
All five jokes are distinct, so there are 5 unique jokes.

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6 / 27

06

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025svg

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025svg

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7 / 27

07

Xbox Controller SVG Artimage generation

Tests an AI's ability to create detailed SVG illustrations of gaming hardware

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025svg

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025svg

Nothing here. The model returned empty. We stared at it for a while.

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8 / 27

08

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Nothing here. The model returned empty. We stared at it for a while.

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9 / 27

09

Realistic AI Interviewconversation

Tests an AI's ability to simulate personalities and predict future trends

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Nothing here. The model returned empty. We stared at it for a while.

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10 / 27

10

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Nothing here. The model returned empty. We stared at it for a while.

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11 / 27

11

Character Voice Testconversation

Tests an AI's ability to write in distinct character voices

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025text

Nothing here. The model returned empty. We stared at it for a while.

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MiniMax M2

MiniMax M2

Minimax · Oct 2025text

Nothing here. The model returned empty. We stared at it for a while.

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12 / 27

12

Minimalist Landing Pageweb design

Tests an AI's ability to generate a complete, working landing page

DeepSeek R1

DeepSeek R1

Deepseek · Feb 2025website

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MiniMax M2

MiniMax M2

Minimax · Oct 2025website

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