What is the difference between DeepSeek R1 and o3 Mini?

DeepSeek R1 is developed by DeepSeek while o3 Mini is developed by OpenAI. DeepSeek R1 has a 128K token context window vs o3 Mini's 64K. in 2 community votes on RIVAL, DeepSeek R1 wins 100% of head-to-head matchups. These results are based on blind head-to-head voting across 38 challenges.

Which is better, DeepSeek R1 or o3 Mini?

Based on 2 community votes on RIVAL, DeepSeek R1 wins 100% of head-to-head matchups against o3 Mini. DeepSeek R1 is strongest in Web Design.

How much does DeepSeek R1 cost compared to o3 Mini?

DeepSeek R1 costs $0.55/M input tokens and o3 Mini costs $1.1/M input tokens. DeepSeek R1 is $0.55/M cheaper per input. DeepSeek R1 also wins more often in community votes, making it the better value.

How are DeepSeek R1 vs o3 Mini votes collected on RIVAL?

RIVAL presents both models' outputs side-by-side in blind duels — voters see the responses but don't know which model produced each one. This eliminates brand bias. 2 votes have been collected for this pair across 38 challenges. All vote data is part of RIVAL's open dataset.

Rival

DeepSeek R1vso3 Mini

33 challenges

Why DeepSeek R1?

2x cheaper overall$0.55/M in · $2.19/M out

2x more context128K

2 months newerFeb 2025

Why o3 Mini?

Leads 2 of 2 benchmarks

Stronger on MATH97.3% vs 72.6%

Favorites

DeepSeek R1o3 Mini

Movie

The Godfather

19728.7

The Shawshank Redemption

19948.7

Album

The Dark Side of the Moon

Pink Floyd

—

Book

Pride and Prejudice

Jane Austen

—

City

Kyoto

Japan

—

Game

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

—

12 / 33

Estimate Complexityreasoning

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

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

Below is a step‐by‐step estimation:

Parameters Count:
GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters.
Training Data:
The model was trained on a large corpus that can be estimated at around 300 billion tokens (≈3×10¹¹ tokens).
FLOPs per Token:
In training deep networks like GPT‑3, the forward pass involves roughly one multiply–accumulate per parameter per token, but the backward pass (for computing gradients) costs about 2–3 times as much as the forward pass. A common rule‐of‐thumb is that training requires roughly 6 times as many FLOPs as a single forward pass.
Total FLOPs Calculation:
The total number of FLOPs is therefore approximately:
[ ext{FLOPs} approx 6 imes ( ext{number of parameters}) imes ( ext{number of tokens}) ] Plugging in the numbers: [ ext{FLOPs} approx 6 imes 1.75 imes 10^{11} imes 3 imes 10^{11} approx 6 imes 5.25 imes 10^{22} approx 3.15 imes 10^{23} ]
Final Estimate:
So, the training of GPT‑3 likely required on the order of 3×10²³ FLOPs.

This is an approximate calculation that combines rough estimates for both the number of training tokens and the factor accounting for both the forward and backward passes. Variations in these estimates or the exact training setup could change the final number by a factor of a few, but 3×10²³ FLOPs is a commonly cited ballpark figure.

Vote

2 / 33

AI Board Game Logicreasoning

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

DeepSeek R1

Deepseek · Feb 2025svg

No content available for this output.

o3 Mini

Openai · Dec 2024svg

No content available for this output.

Vote

3 / 33

Logic Puzzlereasoning

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

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

4 / 33

Math Misconception Testreasoning

Tests an AI's understanding of number representation

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

5 / 33

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

6 / 33

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek R1

Deepseek · Feb 2025svg

No content available for this output.

o3 Mini

Openai · Dec 2024svg

No content available for this output.

Vote

7 / 33

Xbox Controller SVG Artimage generation

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

DeepSeek R1

Deepseek · Feb 2025svg

No content available for this output.

o3 Mini

Openai · Dec 2024svg

No content available for this output.

Vote

8 / 33

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

9 / 33

Realistic AI Interviewconversation

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

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

10 / 33

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

11 / 33

Character Voice Testconversation

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

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

o3 Mini

Openai · Dec 2024text

No content available for this output.

Vote

Minimalist Landing Pageweb design

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

DeepSeek R1

Deepseek · Feb 2025website

o3 Mini

Openai · Dec 2024website

Vote

12 of 33

FAQ

Continue exploring

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o3 Mini vs Claude Sonnet 4New provider

Below is a step‐by‐step estimation:

Parameters Count:
GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters.
Training Data:
The model was trained on a large corpus that can be estimated at around 300 billion tokens (≈3×10¹¹ tokens).
FLOPs per Token:
In training deep networks like GPT‑3, the forward pass involves roughly one multiply–accumulate per parameter per token, but the backward pass (for computing gradients) costs about 2–3 times as much as the forward pass. A common rule‐of‐thumb is that training requires roughly 6 times as many FLOPs as a single forward pass.
Total FLOPs Calculation:
The total number of FLOPs is therefore approximately:
[ ext{FLOPs} approx 6 imes ( ext{number of parameters}) imes ( ext{number of tokens}) ] Plugging in the numbers: [ ext{FLOPs} approx 6 imes 1.75 imes 10^{11} imes 3 imes 10^{11} approx 6 imes 5.25 imes 10^{22} approx 3.15 imes 10^{23} ]
Final Estimate:
So, the training of GPT‑3 likely required on the order of 3×10²³ FLOPs.