What is the difference between DeepSeek R1 and xAI: Grok 4?

DeepSeek R1 is developed by DeepSeek while xAI: Grok 4 is developed by xAI. DeepSeek R1 has a 128K token context window vs xAI: Grok 4's 256K. in 1 community votes on RIVAL, xAI: Grok 4 wins 100% of head-to-head matchups. These results are based on blind head-to-head voting across 31 challenges.

Which is better, DeepSeek R1 or xAI: Grok 4?

Based on 1 community votes on RIVAL, xAI: Grok 4 wins 100% of head-to-head matchups against DeepSeek R1.

How much does DeepSeek R1 cost compared to xAI: Grok 4?

DeepSeek R1 costs $0.55/M input tokens and xAI: Grok 4 costs $3/M input tokens. DeepSeek R1 is $2.45/M cheaper per input. The more expensive model wins 100% of duels, so the premium may be justified by quality.

How are DeepSeek R1 vs xAI: Grok 4 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. 1 votes have been collected for this pair across 31 challenges. All vote data is part of RIVAL's open dataset.

Rival

DeepSeek R1vsxAI: Grok 4

31 challenges

Why DeepSeek R1?

6.6x cheaper overall$0.55/M in · $2.19/M out

Why xAI: Grok 4?

2x more context256K

5 months newerJul 2025

Favorites

DeepSeek R1xAI: Grok 4

Movie

The Godfather

19728.7

The Matrix

19998.2

Album

The Dark Side of the Moon

Pink Floyd

The Dark Side of the Moon

Pink Floyd

Book

Pride and Prejudice

Jane Austen

The Hitch Hiker's Guide to the Galaxy

Douglas Adams

City

Kyoto

Japan

Tokyo

Japan

Game

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

Portal

Action, Puzzle4.5

12 / 31

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.

xAI: Grok 4

Xai · Jul 2025text

Below, I'll estimate the total number of floating-point operations (FLOPs) required to train GPT-3 from scratch, based on publicly known details about the model (from its original release and related analyses) and standard formulas used in machine learning research for estimating training compute. I'll explain my reasoning step by step, including the assumptions I'm making and why they're reasonable. Note that this is an estimation—exact numbers aren't publicly disclosed in full detail by OpenAI, and real-world training involves optimizations, inefficiencies, and hardware-specific factors that could adjust the final figure. I'm not searching online; this is based on my pre-existing knowledge of large language model (LLM) training.

Step 1: Recall Key Facts About GPT-3

Model size: GPT-3 (specifically, the largest variant, often called "GPT-3 175B") has approximately 175 billion parameters. This is the total number of trainable weights in the model.
Training data: It was trained on a massive dataset, including filtered Common Crawl data, books, Wikipedia, and other sources. The total number of tokens (subword units used in training) is estimated to be around 300 billion. This is based on the reported dataset size (about 570 GB of text) and typical tokenization rates for models like GPT (where 1 token ≈ 4 characters, leading to roughly 300 billion tokens from that volume of data).
Training setup: GPT-3 is an autoregressive transformer model trained using supervised fine-tuning on next-token prediction (essentially, language modeling). It was trained for roughly one epoch (one full pass) through the dataset, which is common for large models to avoid overfitting and due to data scarcity at that scale.

These numbers are widely cited in AI research papers and discussions around scaling laws (e.g., work from OpenAI and DeepMind on model scaling).

Step 2: Understand the Standard FLOPs Estimation Formula for LLM Training

Training a transformer-based LLM like GPT-3 involves:

A forward pass: Computing predictions for each token in the input sequence (involves matrix multiplications across the model's layers).
A backward pass: Computing gradients to update the model's parameters (via backpropagation).
This happens for every token in the training dataset.

A widely used rule of thumb from AI scaling research (e.g., papers like "Scaling Laws for Neural Language Models" by Kaplan et al. from OpenAI) estimates the total training FLOPs as:

Total FLOPs ≈ 6 × P × T

Where:

P = number of parameters (e.g., 175 billion for GPT-3).
T = total number of tokens processed during training (e.g., 300 billion).
6 is a multiplier that accounts for:
- Forward pass: ~2×P FLOPs per token (mainly matrix-vector multiplications involving all parameters).
- Backward pass: ~4×P FLOPs per token (backpropagation is roughly twice as expensive as forward).
- Total per token: ~6×P FLOPs.
- This assumes dense operations (no sparsity) and ignores minor overhead like activations or normalization, which is reasonable for estimation.

This formula is a simplification but matches empirical reports for models like GPT-2, GPT-3, and others. It focuses on the dominant compute cost (parameter updates) and doesn't include inference-time FLOPs, data loading, or evaluation— just training.

Why 6? It's derived from counting operations in transformer layers (attention, feed-forward networks). Some papers use slightly different multipliers (e.g., 3× or 4× if only counting multiply-accumulates), but 6× is the standard for total FLOPs in scaling law discussions.

Step 3: Plug in the Numbers

P = 175 billion = 1.75 × 10^11
T = 300 billion = 3 × 10^11
Multiplier = 6

Total FLOPs = 6 × (1.75 × 10^11) × (3 × 10^11)

First, compute 1.75 × 3 = 5.25
Then, 5.25 × 10^(11+11) = 5.25 × 10^22
Finally, 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23

So, my estimate is approximately 3 × 10^23 FLOPs (rounding to one significant figure for simplicity, as these are ballpark numbers).

Step 4: Validate and Adjust for Uncertainties

Is the token count accurate? 300 billion is a common estimate based on the GPT-3 paper's description of the dataset (45 TB of compressed text filtered down to 570 GB). Token counts can vary with the tokenizer (GPT uses byte-pair encoding), but this is within 10-20% of reported figures. If the effective tokens were higher (e.g., due to repeated data or sampling), it could bump up to 400-500 billion, increasing FLOPs by ~30-60%, but I think 300 billion is conservative and standard.
Is the multiplier accurate? Some analyses use 4×P×T (focusing only on multiply-accumulates), which would give ~2 × 10^23 FLOPs. Others include optimizer overhead (e.g., Adam optimizer adds ~10-20% more). I've seen independent estimates around 3.7 × 10^23 FLOPs, which is close to mine—differences could come from including setup costs or hardware inefficiencies.
Other factors:
- Parallelism and hardware: GPT-3 was trained on a supercomputer-scale cluster (e.g., thousands of V100 GPUs). Real FLOPs include communication overhead in distributed training, which might add 10-20% inefficiency, but the formula ignores this for simplicity.
- Precision: Training likely used mixed precision (e.g., FP16), which reduces FLOPs slightly but doesn't change the order of magnitude.
- Epochs: If there was any multi-epoch training or warm-starting, it could increase FLOPs, but reports suggest mostly one epoch.
- Order of magnitude check: Smaller models like GPT-2 (1.5 billion params, ~10 billion tokens) required ~10^19 FLOPs. Scaling up by factors of ~100x params and ~30x tokens gives ~10^19 × 3,000 = ~3 × 10^22, but wait—that's off because the formula is multiplicative. Actually, GPT-2 estimates are lower; adjusting properly lands in the 10^23 range for GPT-3, which aligns with reports of it taking thousands of GPU-years.

If I adjust for potential underestimation (e.g., higher token count or overhead), the upper bound might be 5 × 10^23 FLOPs, but I stick with 3 × 10^23 as the core estimate.

Step 5: Convert to Intuitive Units (Optional Context)

To make this relatable:

3 × 10^23 FLOPs is enormous—equivalent to a high-end GPU (e.g., 10 TFLOPs/second) running continuously for about 1,000 years (rough calc: 10^13 FLOPs/sec × 3 × 10^7 sec/year × 1,000 years ≈ 3 × 10^23).
In terms of energy, this might consume gigawatt-hours of electricity, but that's beyond FLOPs.

Final Estimate

Based on this reasoning, I estimate the total FLOPs for training GPT-3 at around 3 × 10^23. This is consistent with what I'd expect from scaling laws and matches informal discussions in the AI community. If new details emerged (e.g., exact token counts), it could shift by a factor of 2, but the order of magnitude (10^23) is solid. If you have additional details or want me to adjust assumptions, let me know!

Vote

2 / 31

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.

xAI: Grok 4

Xai · Jul 2025text

Based on the details in the riddle, Sally has exactly 1 sister.

Quick Explanation

Sally (a girl) has 3 brothers. That's straightforward.
Each of those brothers has exactly 2 sisters (female siblings).
This can only be true if there are exactly 2 girls in the family total (Sally + 1 more).
Therefore, Sally herself must have exactly 1 sister (the other girl).

Family Breakdown

Boys: 3 total (Sally's brothers).
Girls: 2 total (Sally + 1 sister).
From any brother's perspective: He has 2 brothers (male siblings) and 2 sisters (female siblings). This matches the riddle exactly.
From Sally's perspective: She has 3 brothers (male siblings) and 1 sister (female sibling). Again, this matches.

If there were more (or fewer) than 2 girls total, the "each brother has 2 sisters" part of the riddle couldn't possibly be true. No other interpretation fits the facts as stated.

Common Mistake to Avoid

Some people misread this type of riddle and try to multiply things (e.g., "3 brothers × 2 sisters each = 6 sisters"), but that's incorrect because it massively overcounts the same sisters. There's no need for math here beyond basic counting of boys vs. girls. The family simply has 3 boys and 2 girls, as explained above.

If this doesn't match what you think the answer is, or if there's more context I'm missing, feel free to reply!

Vote

3 / 31

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

xAI: Grok 4

Xai · Jul 2025text

Below, I'll follow your instructions step by step. First, I'll generate 5 jokes (numbered for clarity). Then, I'll answer the question: "How many are unique?"

Generated Jokes:

Why did the scarecrow win an award? Because he was outstanding in his field!
Why did the scarecrow win an award? Because he was outstanding in his field!
Why did the scarecrow win an award? Because he was outstanding in his field!
Why did the scarecrow win an award? Because he was outstanding in his field!
Why did the scarecrow win an award? Because he was outstanding in his field!

How many are unique?

Only 1 (since they're all the same joke repeated 5 times). 😄

Vote

4 / 31

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek R1

Deepseek · Feb 2025svg

xAI: Grok 4

Xai · Jul 2025svg

Vote

5 / 31

Xbox Controller SVG Artimage generation

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

DeepSeek R1

Deepseek · Feb 2025svg

xAI: Grok 4

Xai · Jul 2025svg

Vote

6 / 31

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.

xAI: Grok 4

Xai · Jul 2025text

No content available for this output.

Vote

7 / 31

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.

xAI: Grok 4

Xai · Jul 2025text

No content available for this output.

Vote

8 / 31

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.

xAI: Grok 4

Xai · Jul 2025text

No content available for this output.

Vote

9 / 31

Minimalist Landing Pageweb design

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

DeepSeek R1

Deepseek · Feb 2025website

xAI: Grok 4

Xai · Jul 2025website

Vote

10 / 31

Pokémon Battle UI Recreationweb design

Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.

DeepSeek R1

Deepseek · Feb 2025website

No content available for this output.

xAI: Grok 4

Xai · Jul 2025website

No content available for this output.

Vote

Mario Level UI Recreationweb design

Recreate an interactive, classic Mario level in a single HTML file.

DeepSeek R1

Deepseek · Feb 2025website

xAI: Grok 4

Xai · Jul 2025website

Vote

12 / 31

Linear App Cloneweb design

Tests an AI's ability to replicate an existing UI with Tailwind CSS

DeepSeek R1

Deepseek · Feb 2025website

No content available for this output.

xAI: Grok 4

Xai · Jul 2025website

No content available for this output.

Vote

12 of 31

FAQ

Continue exploring

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Step 1: Recall Key Facts About GPT-3

Model size: GPT-3 (specifically, the largest variant, often called "GPT-3 175B") has approximately 175 billion parameters. This is the total number of trainable weights in the model.
Training data: It was trained on a massive dataset, including filtered Common Crawl data, books, Wikipedia, and other sources. The total number of tokens (subword units used in training) is estimated to be around 300 billion. This is based on the reported dataset size (about 570 GB of text) and typical tokenization rates for models like GPT (where 1 token ≈ 4 characters, leading to roughly 300 billion tokens from that volume of data).
Training setup: GPT-3 is an autoregressive transformer model trained using supervised fine-tuning on next-token prediction (essentially, language modeling). It was trained for roughly one epoch (one full pass) through the dataset, which is common for large models to avoid overfitting and due to data scarcity at that scale.

These numbers are widely cited in AI research papers and discussions around scaling laws (e.g., work from OpenAI and DeepMind on model scaling).

Step 2: Understand the Standard FLOPs Estimation Formula for LLM Training

Training a transformer-based LLM like GPT-3 involves:

A forward pass: Computing predictions for each token in the input sequence (involves matrix multiplications across the model's layers).
A backward pass: Computing gradients to update the model's parameters (via backpropagation).
This happens for every token in the training dataset.

A widely used rule of thumb from AI scaling research (e.g., papers like "Scaling Laws for Neural Language Models" by Kaplan et al. from OpenAI) estimates the total training FLOPs as:

Total FLOPs ≈ 6 × P × T

Where:

P = number of parameters (e.g., 175 billion for GPT-3).
T = total number of tokens processed during training (e.g., 300 billion).
6 is a multiplier that accounts for:
- Forward pass: ~2×P FLOPs per token (mainly matrix-vector multiplications involving all parameters).
- Backward pass: ~4×P FLOPs per token (backpropagation is roughly twice as expensive as forward).
- Total per token: ~6×P FLOPs.
- This assumes dense operations (no sparsity) and ignores minor overhead like activations or normalization, which is reasonable for estimation.

Step 3: Plug in the Numbers

P = 175 billion = 1.75 × 10^11
T = 300 billion = 3 × 10^11
Multiplier = 6

Total FLOPs = 6 × (1.75 × 10^11) × (3 × 10^11)

First, compute 1.75 × 3 = 5.25
Then, 5.25 × 10^(11+11) = 5.25 × 10^22
Finally, 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23

So, my estimate is approximately 3 × 10^23 FLOPs (rounding to one significant figure for simplicity, as these are ballpark numbers).

Step 4: Validate and Adjust for Uncertainties

Is the token count accurate? 300 billion is a common estimate based on the GPT-3 paper's description of the dataset (45 TB of compressed text filtered down to 570 GB). Token counts can vary with the tokenizer (GPT uses byte-pair encoding), but this is within 10-20% of reported figures. If the effective tokens were higher (e.g., due to repeated data or sampling), it could bump up to 400-500 billion, increasing FLOPs by ~30-60%, but I think 300 billion is conservative and standard.
Is the multiplier accurate? Some analyses use 4×P×T (focusing only on multiply-accumulates), which would give ~2 × 10^23 FLOPs. Others include optimizer overhead (e.g., Adam optimizer adds ~10-20% more). I've seen independent estimates around 3.7 × 10^23 FLOPs, which is close to mine—differences could come from including setup costs or hardware inefficiencies.
Other factors:
- Parallelism and hardware: GPT-3 was trained on a supercomputer-scale cluster (e.g., thousands of V100 GPUs). Real FLOPs include communication overhead in distributed training, which might add 10-20% inefficiency, but the formula ignores this for simplicity.
- Precision: Training likely used mixed precision (e.g., FP16), which reduces FLOPs slightly but doesn't change the order of magnitude.
- Epochs: If there was any multi-epoch training or warm-starting, it could increase FLOPs, but reports suggest mostly one epoch.
- Order of magnitude check: Smaller models like GPT-2 (1.5 billion params, ~10 billion tokens) required ~10^19 FLOPs. Scaling up by factors of ~100x params and ~30x tokens gives ~10^19 × 3,000 = ~3 × 10^22, but wait—that's off because the formula is multiplicative. Actually, GPT-2 estimates are lower; adjusting properly lands in the 10^23 range for GPT-3, which aligns with reports of it taking thousands of GPU-years.

If I adjust for potential underestimation (e.g., higher token count or overhead), the upper bound might be 5 × 10^23 FLOPs, but I stick with 3 × 10^23 as the core estimate.

Step 5: Convert to Intuitive Units (Optional Context)

To make this relatable:

3 × 10^23 FLOPs is enormous—equivalent to a high-end GPU (e.g., 10 TFLOPs/second) running continuously for about 1,000 years (rough calc: 10^13 FLOPs/sec × 3 × 10^7 sec/year × 1,000 years ≈ 3 × 10^23).
In terms of energy, this might consume gigawatt-hours of electricity, but that's beyond FLOPs.