What is the difference between DeepSeek V3.1 and GPT-4.1 Mini?

DeepSeek V3.1 is developed by DeepSeek while GPT-4.1 Mini is developed by OpenAI. DeepSeek V3.1 has a 164K token context window vs GPT-4.1 Mini's 1.0M. You can compare their actual outputs across 43 challenges on RIVAL to see how they differ in practice.

Which is better, DeepSeek V3.1 or GPT-4.1 Mini?

It depends on your use case. DeepSeek V3.1 and GPT-4.1 Mini each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 43 challenges so you can judge which fits your needs best.

How much does DeepSeek V3.1 cost compared to GPT-4.1 Mini?

DeepSeek V3.1 costs $0.2/M input tokens and GPT-4.1 Mini costs $0.4/M input tokens. DeepSeek V3.1 is $0.20/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 DeepSeek V3.1 and GPT-4.1 Mini on RIVAL?

This page shows a side-by-side comparison of DeepSeek V3.1 and GPT-4.1 Mini 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 V3.1vsGPT-4.1 Mini

38 challenges

Why DeepSeek V3.1?

2x cheaper overall$0.20/M in · $0.80/M out

4 months newerAug 2025

Why GPT-4.1 Mini?

6.4x more context1.0M

Favorites

DeepSeek V3.1GPT-4.1 Mini

Movie

The Princess Bride

19877.7

Inception

20108.4

Album

The Rise and Fall of Ziggy Stardust and the Spiders From Mars

David Bowie

Dark Side Of The Moon

suisside

Book

Gödel, Escher, Bach

Douglas R. Hofstadter

Sapiens

Yuval Noah Harari

City

Kyoto

Japan

Tokyo

Japan

Game

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

12 / 38

Estimate Complexityreasoning

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

DeepSeek V3.1

Deepseek · Aug 2025text

No content available for this output.

GPT-4.1 Mini

Openai · Apr 2025text

Certainly! Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 by breaking down the problem step-by-step. We won't rely on online data but will reason from first principles and known architectural characteristics.

Step 1: Understand GPT-3's basic specifications

GPT-3, the largest model version, has approximately:

Number of parameters (weights): 175 billion (1.75 × 10¹¹)
Number of layers: about 96 transformer layers
Model size: 175B parameters is widely known

Step 2: Estimate FLOPs per forward pass per token

A transformer forward pass consists mainly of:

Matrix multiplications in self-attention
Matrix multiplications in feedforward network (FFN)

Two main components dominate FLOPs:

Self-attention: roughly proportional to ( d_{model}^2 ) per token (where ( d_{model} ) is hidden size)
Feedforward layer: 2 linear layers per transformer layer, roughly (4 d_{model}^2) multiplications per token (due to expanding dimension by factor 4 in FFN)

Step 3: Extract hidden size and sequence length

From papers and common configurations:

Hidden size ( d_{model} approx 12,288 ) (for 175B model)
Sequence length used during training ( L approx 2048 ) tokens (max context length)

Step 4: Calculate approximate FLOPs per token per forward pass

Using formulas from transformer FLOPs estimation:

For each transformer layer, per token, forward pass FLOPs roughly:

[ ext{FLOPs}_ ext{layer/token} approx 2 imes d_{model}^2 ]

(for attention and FFN combined, includes multiplications and additions)

Because the FFN (feedforward network) is the heaviest part, it dominates, so let's assume:

( sim 8 imes d_{model}^2 ) FLOPs per token per layer (this is a common estimate for one forward pass)

Substituting:

[ 8 imes (12,288)^2 approx 8 imes 1.5 imes 10^8 = 1.2 imes 10^9 ext{ FLOPs per token per layer} ]

For 96 layers:

[ 1.2 imes 10^9 imes 96 = 1.15 imes 10^{11} ext{ FLOPs per token (forward pass)} ]

Step 5: Account for backward pass

Backpropagation generally costs about 2× to 3× the forward pass FLOPs because computing gradients involves forward pass and backward pass computations.

Use factor 3 to be safe:

[ ext{FLOPs} = 3 imes 1.15 imes 10^{11} = 3.45 imes 10^{11} ext{ FLOPs per token (forward + backward)} ]

Step 6: Calculate total number of tokens used in training

From publicly known info, GPT-3 was trained on about 300 billion tokens (around 3 × 10¹¹ tokens) of text.

Step 7: Calculate total FLOPs for full training

Multiply FLOPs per token by total tokens:

[ 3.45 imes 10^{11} imes 3 imes 10^{11} = 1.035 imes 10^{23} ext{ FLOPs} ]

Final estimation:

[ oxed{ ext{Total FLOPs to train GPT-3} approx 10^{23} quad ext{FLOPs} } ]

Summary:

Per token forward + backward FLOPs: (sim 3.5 imes 10^{11})
Number of training tokens: (sim 3 imes 10^{11})
Total training FLOPs: (sim 10^{23})

Additional notes:

This estimate matches the order of magnitude widely discussed in ML circles.
Variations depend on exact training steps, optimizer, precision (FP16/32), etc.
Might be a bit lower or higher depending on exact implementation.

So, the training of GPT-3 requires on the order of ( 10^{23} ) floating point operations.

Vote

2 / 38

AI Board Game Logicreasoning

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

DeepSeek V3.1

Deepseek · Aug 2025svg

No content available for this output.

GPT-4.1 Mini

Openai · Apr 2025svg

Vote

3 / 38

Logic Puzzlereasoning

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

DeepSeek V3.1

Deepseek · Aug 2025text

No content available for this output.

GPT-4.1 Mini

Openai · Apr 2025text

Let's analyze the information:

Sally has 3 brothers.
Each brother has 2 sisters.

Since Sally is a girl and one of the sisters, for each brother to have 2 sisters, it means there must be a total of 2 sisters in the family (including Sally).

Therefore:

Total number of sisters = 2
Sally is one of them
So, Sally has 1 sister

Answer: Sally has 1 sister.

Vote

4 / 38

Math Misconception Testreasoning

Tests an AI's understanding of number representation

DeepSeek V3.1

Deepseek · Aug 2025text

No content available for this output.

GPT-4.1 Mini

Openai · Apr 2025text

No, 9.11 is not greater than 9.9.

When comparing decimal numbers, you compare digit by digit from left to right:

The integer part: both are 9.
The tenths place: 9.11 has 1 in the tenths place, while 9.9 has 9 in the tenths place.

No content available for this output.

Vote

12 / 38

Minimalist Landing Pageweb design

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

DeepSeek V3.1

Deepseek · Aug 2025website

GPT-4.1 Mini

Openai · Apr 2025website

Vote

12 of 38

FAQ

Continue exploring

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GPT-4.1 Mini vs Pony AlphaNew provider

Step 1: Understand GPT-3's basic specifications

GPT-3, the largest model version, has approximately:

Number of parameters (weights): 175 billion (1.75 × 10¹¹)
Number of layers: about 96 transformer layers
Model size: 175B parameters is widely known

Step 2: Estimate FLOPs per forward pass per token

A transformer forward pass consists mainly of:

Matrix multiplications in self-attention
Matrix multiplications in feedforward network (FFN)

Two main components dominate FLOPs:

Self-attention: roughly proportional to ( d_{model}^2 ) per token (where ( d_{model} ) is hidden size)
Feedforward layer: 2 linear layers per transformer layer, roughly (4 d_{model}^2) multiplications per token (due to expanding dimension by factor 4 in FFN)

Step 3: Extract hidden size and sequence length

From papers and common configurations:

Hidden size ( d_{model} approx 12,288 ) (for 175B model)
Sequence length used during training ( L approx 2048 ) tokens (max context length)

Step 4: Calculate approximate FLOPs per token per forward pass

Using formulas from transformer FLOPs estimation:

For each transformer layer, per token, forward pass FLOPs roughly:

[ ext{FLOPs}_ ext{layer/token} approx 2 imes d_{model}^2 ]

(for attention and FFN combined, includes multiplications and additions)

Because the FFN (feedforward network) is the heaviest part, it dominates, so let's assume:

( sim 8 imes d_{model}^2 ) FLOPs per token per layer (this is a common estimate for one forward pass)

Substituting:

[ 8 imes (12,288)^2 approx 8 imes 1.5 imes 10^8 = 1.2 imes 10^9 ext{ FLOPs per token per layer} ]

For 96 layers:

[ 1.2 imes 10^9 imes 96 = 1.15 imes 10^{11} ext{ FLOPs per token (forward pass)} ]

Step 5: Account for backward pass

Backpropagation generally costs about 2× to 3× the forward pass FLOPs because computing gradients involves forward pass and backward pass computations.

Use factor 3 to be safe:

[ ext{FLOPs} = 3 imes 1.15 imes 10^{11} = 3.45 imes 10^{11} ext{ FLOPs per token (forward + backward)} ]

Step 6: Calculate total number of tokens used in training

From publicly known info, GPT-3 was trained on about 300 billion tokens (around 3 × 10¹¹ tokens) of text.

Step 7: Calculate total FLOPs for full training

Multiply FLOPs per token by total tokens:

[ 3.45 imes 10^{11} imes 3 imes 10^{11} = 1.035 imes 10^{23} ext{ FLOPs} ]

Final estimation:

[ oxed{ ext{Total FLOPs to train GPT-3} approx 10^{23} quad ext{FLOPs} } ]

Summary:

Per token forward + backward FLOPs: (sim 3.5 imes 10^{11})
Number of training tokens: (sim 3 imes 10^{11})
Total training FLOPs: (sim 10^{23})

Additional notes:

This estimate matches the order of magnitude widely discussed in ML circles.
Variations depend on exact training steps, optimizer, precision (FP16/32), etc.
Might be a bit lower or higher depending on exact implementation.

So, the training of GPT-3 requires on the order of ( 10^{23} ) floating point operations.