What is the difference between DeepSeek R1 0528 and Optimus Alpha?

DeepSeek R1 0528 is developed by DeepSeek while Optimus Alpha is developed by OpenRouter. DeepSeek R1 0528 has a 164K token context window vs Optimus Alpha's 1.0M. You can compare their actual outputs across 17 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek R1 0528 or Optimus Alpha?

It depends on your use case. DeepSeek R1 0528 and Optimus Alpha each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 17 challenges so you can judge which fits your needs best.

How can I compare DeepSeek R1 0528 and Optimus Alpha on Rival?

This page shows a side-by-side comparison of DeepSeek R1 0528 and Optimus Alpha 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 May 28, 2025

DeepSeek R1 0528vsOptimus Alpha

17 fights queued

Why DeepSeek R1 0528?

2 months newerMay 2025

Why Optimus Alpha?

6.1x more context1.0M

DeepSeek R1 0528Optimus Alpha

Input price

$0.000/M

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Output price

$0.000/M

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Context

164K

1.0M

Released

May 2025

Apr 2025

12 / 17

Estimate Complexityreasoning

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

DeepSeek R1 0528

Deepseek · May 2025text

To estimate the total number of FLOPs required to train GPT-3, we use the standard formula for transformer model training FLOPs:

Total FLOPs ≈ 6 × (number of parameters) × (number of tokens in the training dataset)

Step-by-Step Reasoning:

Model Parameters (N):
- GPT-3 has 175 billion parameters, so ( N = 175 \times 10^9 ).
Training Tokens (T):
- GPT-3 was trained on approximately 300 billion tokens, so ( T = 300 \times 10^9 ).
FLOPs per Parameter-Token:
- Forward pass: Each token requires ~2 FLOPs per parameter (due to matrix multiplications involving multiply-add operations, counted as 2 FLOPs).
- Backward pass: Gradient computation typically requires ~4 FLOPs per parameter (twice the cost of the forward pass for gradient calculations).
- Total per parameter-token: ( 2 , \text{(forward)} + 4 , \text{(backward)} = 6 , \text{FLOPs} ).
Calculation: [ \text{Total FLOPs} = 6 \times (175 \times 10^9) \times (300 \times 10^9) ]
- First, multiply the parameters and tokens: [ 175 \times 10^9 \times 300 \times 10^9 = 52,500 \times 10^{18} = 5.25 \times 10^{22} ]
- Then multiply by 6: [ 6 \times 5.25 \times 10^{22} = 31.5 \times 10^{22} = 3.15 \times 10^{23} ]

Final Estimate:

[ \boxed{3.15 \times 10^{23}} , \text{FLOPs} ]

Notes:

This formula ((6 \times N \times T)) is a widely accepted heuristic for transformer models.
The estimate assumes one full training epoch (no repeated tokens).
Additional overhead (e.g., optimizer steps, activations) is negligible compared to the dominant matrix operations, so it is omitted.
GPT-3 specifics: Parameters = 175B, Tokens = 300B.

DeepSeek R1 0528 vs Optimus Alpha

Which is better, DeepSeek R1 0528 or Optimus Alpha?

Key Differences Between DeepSeek R1 0528 and Optimus Alpha

Why DeepSeek R1 0528?

Why Optimus Alpha?

Estimate Complexityreasoning

DeepSeek R1 0528

Step-by-Step Reasoning:

Final Estimate:

Notes:

Optimus Alpha

Step 1: Understand Model Size

Step 2: Estimate FLOPs per Forward/Backward Pass

Step 3: Estimate Total Number of Tokens Used in Training

Step 4: Calculate Total Number of FLOPs

Step 5: Check and discussion

Final Answer

Logic Puzzlereasoning

DeepSeek R1 0528

Optimus Alpha

Stochastic Consistency Testanalysis

DeepSeek R1 0528

Uniqueness Check:

Optimus Alpha

SVG Layout Challengeimage generation

DeepSeek R1 0528

Optimus Alpha

Xbox Controller SVG Artimage generation

DeepSeek R1 0528

Optimus Alpha

Generate a Stand-Up Routineconversation

DeepSeek R1 0528

Optimus Alpha

Realistic AI Interviewconversation

DeepSeek R1 0528

Optimus Alpha

Satirical Fake News Headlineconversation

DeepSeek R1 0528

Optimus Alpha

Minimalist Landing Pageweb design

DeepSeek R1 0528

Optimus Alpha

Linear App Cloneweb design

DeepSeek R1 0528

Optimus Alpha

Framer-Style Animationweb design

DeepSeek R1 0528

Optimus Alpha

Dark Mode Dashboardweb design

DeepSeek R1 0528

Optimus Alpha

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Common questions

What is the difference between DeepSeek R1 0528 and Optimus Alpha?

Which is better, DeepSeek R1 0528 or Optimus Alpha?

How can I compare DeepSeek R1 0528 and Optimus Alpha on Rival?

Why DeepSeek R1 0528?

Why Optimus Alpha?

Estimate Complexityreasoning

DeepSeek R1 0528

Step-by-Step Reasoning:

Final Estimate:

Notes:

Optimus Alpha

Step 1: Understand Model Size

Step 2: Estimate FLOPs per Forward/Backward Pass

Step 3: Estimate Total Number of Tokens Used in Training

Step 4: Calculate Total Number of FLOPs

Step 5: Check and discussion

Final Answer

Logic Puzzlereasoning

DeepSeek R1 0528

Optimus Alpha

Stochastic Consistency Testanalysis

DeepSeek R1 0528

Uniqueness Check:

Optimus Alpha

SVG Layout Challengeimage generation

DeepSeek R1 0528