What is the difference between DeepSeek V3.2 and GPT-5.4 Mini?

DeepSeek V3.2 is developed by DeepSeek while GPT-5.4 Mini is developed by OpenAI. DeepSeek V3.2 has a 131K token context window vs GPT-5.4 Mini's 400K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek V3.2 or GPT-5.4 Mini?

It depends on your use case. DeepSeek V3.2 and GPT-5.4 Mini each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.

How much does DeepSeek V3.2 cost compared to GPT-5.4 Mini?

DeepSeek V3.2 costs $0.28/M input tokens and GPT-5.4 Mini costs $0.75/M input tokens. DeepSeek V3.2 is $0.47/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 V3.2 and GPT-5.4 Mini on Rival?

This page shows a side-by-side comparison of DeepSeek V3.2 and GPT-5.4 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

Updated Mar 17, 2026

DeepSeek V3.2vsGPT-5.4 Mini

47 fights queued

Why DeepSeek V3.2?

8.2x cheaper overall$0.28/M in · $0.42/M out

Why GPT-5.4 Mini?

3.1x more context400K

4 months newerMar 2026

DeepSeek V3.2GPT-5.4 Mini

Input price

$0.28/M

$0.75/M

Output price

$0.42/M

$4.50/M

Context

131K

400K

Released

Dec 2025

Mar 2026

Favorites

DeepSeek V3.2GPT-5.4 Mini

Movie

The Princess Bride

19877.7

The Godfather

19728.7

Album

Kind of Blue

Miles Davis

Kid A

Radiohead

Book

Gödel, Escher, Bach

Douglas R. Hofstadter

Pride and Prejudice

Jane Austen

City

Kyoto

Japan

Kyoto

Japan

Game

Tetris (1984)

Puzzle4.4

The Legend of Zelda: Breath of the Wild

Adventure, Action4.5

6 / 47

Estimate Complexityreasoning

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

DeepSeek V3.2

Deepseek · Dec 2025text

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026text

A reasonable back-of-the-envelope estimate for training GPT-3 is about 3 × 10²³ FLOPs total, with maybe a factor-of-2 uncertainty depending on what you count and the exact training setup.

Here’s the reasoning step by step.

1) Use the model size and token count

GPT-3’s largest widely cited version has about:

175 billion parameters = 1.75 × 10¹¹ parameters
Trained on roughly 300 billion tokens = 3 × 10¹¹ tokens

2) Estimate FLOPs per token for training

For a transformer, a rough rule of thumb is:

Forward pass + backward pass costs on the order of 6 × number of parameters FLOPs per token

Why 6?

Forward pass: roughly 2 FLOPs per parameter-use is a common crude estimate
Backward pass: roughly 2–4× the forward cost
So total training cost per token is often approximated as about 6P

Thus:

[ \text{FLOPs per token} \approx 6 \times 1.75 \times 10^{11} = 1.05 \times 10^{12} ]

So each token costs about 1 trillion FLOPs.

3) Multiply by the number of training tokens

[ (1.05 \times 10^{12}) \times (3 \times 10^{11}) = 3.15 \times 10^{23} ]

So the total training compute is about:

[ \boxed{3 \times 10^{23}\ \text{FLOPs}} ]

4) Sanity check

This scale is plausible because GPT-3 was famously extremely expensive to train, and 10²³ FLOPs is in the right ballpark for a model of that size trained on hundreds of billions of tokens.

5) Caveats

This is an estimate, not an exact number, because it depends on:

whether you count only dense matrix ops or also overhead
optimizer cost
attention cost vs. MLP cost
sequence length and batching details
whether the “6P per token” rule is used or a slightly different constant

A more conservative range would be:

[ \boxed{(2 \text{ to } 4) \times 10^{23}\ \text{FLOPs}} ]

Final answer

Estimated total training cost for GPT-3: about (3 \times 10^{23}) FLOPs.

Try this prompt

Vote

2 / 47

AI Board Game Logicreasoning

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

DeepSeek V3.2

Deepseek · Dec 2025svg

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026svg

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

Try this prompt

Vote

3 / 47

Logic Puzzlereasoning

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

DeepSeek V3.2

Deepseek · Dec 2025text

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026text

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

Try this prompt

Vote

4 / 47

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek V3.2

Deepseek · Dec 2025text

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026text

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

Try this prompt

Vote

5 / 47

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek V3.2

Deepseek · Dec 2025svg

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026svg

Try this prompt

Vote

6 / 47

Xbox Controller SVG Artimage generation

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

DeepSeek V3.2

Deepseek · Dec 2025svg

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

Try this prompt

GPT-5.4 Mini

Openai · Mar 2026svg

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

Try this prompt

Vote

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Our Verdict

DeepSeek V3.2

GPT-5.4 Mini

No community votes yet. On paper, these are closely matched - try both with your actual task to see which fits your workflow.

DeepSeek V3.2 is 11x cheaper per token — worth considering if cost matters.

Too close to call

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FAQ

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A reasonable back-of-the-envelope estimate for training GPT-3 is about 3 × 10²³ FLOPs total, with maybe a factor-of-2 uncertainty depending on what you count and the exact training setup.

Here’s the reasoning step by step.

1) Use the model size and token count

GPT-3’s largest widely cited version has about:

175 billion parameters = 1.75 × 10¹¹ parameters
Trained on roughly 300 billion tokens = 3 × 10¹¹ tokens

2) Estimate FLOPs per token for training

For a transformer, a rough rule of thumb is:

Forward pass + backward pass costs on the order of 6 × number of parameters FLOPs per token

Why 6?

Forward pass: roughly 2 FLOPs per parameter-use is a common crude estimate
Backward pass: roughly 2–4× the forward cost
So total training cost per token is often approximated as about 6P

Thus:

[ \text{FLOPs per token} \approx 6 \times 1.75 \times 10^{11} = 1.05 \times 10^{12} ]

So each token costs about 1 trillion FLOPs.

3) Multiply by the number of training tokens

[ (1.05 \times 10^{12}) \times (3 \times 10^{11}) = 3.15 \times 10^{23} ]

So the total training compute is about:

[ \boxed{3 \times 10^{23}\ \text{FLOPs}} ]

4) Sanity check

This scale is plausible because GPT-3 was famously extremely expensive to train, and 10²³ FLOPs is in the right ballpark for a model of that size trained on hundreds of billions of tokens.

5) Caveats

This is an estimate, not an exact number, because it depends on:

whether you count only dense matrix ops or also overhead
optimizer cost
attention cost vs. MLP cost
sequence length and batching details
whether the “6P per token” rule is used or a slightly different constant

A more conservative range would be:

[ \boxed{(2 \text{ to } 4) \times 10^{23}\ \text{FLOPs}} ]

Final answer

Estimated total training cost for GPT-3: about (3 \times 10^{23}) FLOPs.

DeepSeek V3.2 vs GPT-5.4 Mini

Which is better, DeepSeek V3.2 or GPT-5.4 Mini?

Key Differences Between DeepSeek V3.2 and GPT-5.4 Mini

Why DeepSeek V3.2?

Why GPT-5.4 Mini?

Favorites

Estimate Complexityreasoning

DeepSeek V3.2

GPT-5.4 Mini

1) Use the model size and token count

2) Estimate FLOPs per token for training

3) Multiply by the number of training tokens

4) Sanity check

5) Caveats

Final answer

AI Board Game Logicreasoning

DeepSeek V3.2

GPT-5.4 Mini

Logic Puzzlereasoning

DeepSeek V3.2

GPT-5.4 Mini

Stochastic Consistency Testanalysis

DeepSeek V3.2

GPT-5.4 Mini

SVG Layout Challengeimage generation

DeepSeek V3.2

GPT-5.4 Mini

Xbox Controller SVG Artimage generation

DeepSeek V3.2

GPT-5.4 Mini

This matchup has more rounds

Static demos are useful. Your prompt matters more.

What is the difference between DeepSeek V3.2 and GPT-5.4 Mini?

Which is better, DeepSeek V3.2 or GPT-5.4 Mini?

How much does DeepSeek V3.2 cost compared to GPT-5.4 Mini?

How can I compare DeepSeek V3.2 and GPT-5.4 Mini on Rival?

Why DeepSeek V3.2?

Why GPT-5.4 Mini?

Favorites

Estimate Complexityreasoning

DeepSeek V3.2

GPT-5.4 Mini

1) Use the model size and token count

2) Estimate FLOPs per token for training

3) Multiply by the number of training tokens

4) Sanity check

5) Caveats

Final answer

AI Board Game Logicreasoning

DeepSeek V3.2

GPT-5.4 Mini

Logic Puzzlereasoning

DeepSeek V3.2

GPT-5.4 Mini

Stochastic Consistency Testanalysis

DeepSeek V3.2

GPT-5.4 Mini

SVG Layout Challengeimage generation

DeepSeek V3.2

GPT-5.4 Mini

Xbox Controller SVG Artimage generation

DeepSeek V3.2

GPT-5.4 Mini

This matchup has more rounds

Static demos are useful. Your prompt matters more.