What is the difference between Claude Opus 4.5 and Grok 3?

Claude Opus 4.5 is developed by Anthropic while Grok 3 is developed by xAI. You can compare their actual outputs across 28 challenges on RIVAL to see how they differ in practice.

Which is better, Claude Opus 4.5 or Grok 3?

It depends on your use case. Claude Opus 4.5 and Grok 3 each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 28 challenges so you can judge which fits your needs best.

How can I compare Claude Opus 4.5 and Grok 3 on RIVAL?

This page shows a side-by-side comparison of Claude Opus 4.5 and Grok 3 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

Claude Opus 4.5vsGrok 3

28 challenges

Why Claude Opus 4.5?

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified80.9% vs 63.8%

9 months newerNov 2025

Why Grok 3?

No standout advantages

Favorites

Claude Opus 4.5Grok 3

Movie

Blade Runner

19827.9

—

Album

Kind of Blue

Miles Davis

—

Book

Gödel, Escher, Bach

Douglas R. Hofstadter

—

City

Tokyo

Japan

—

Game

Portal 2

Shooter, Puzzle4.6

—

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Estimate Complexityreasoning

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

Claude Opus 4.5

Anthropic · Nov 2025text

No content available for this output.

Grok 3

Xai · Feb 2025text

To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we’ll need to make some reasonable assumptions based on what’s generally known about large language models like GPT-3, its architecture, and typical training procedures. GPT-3, developed by OpenAI, is a massive model with 175 billion parameters, and while exact training details aren’t fully public, we can piece together an estimate using standard practices in machine learning.

Step 1: Understand the Key Components

The number of FLOPs to train a model depends on:

Number of parameters (P): GPT-3 has 175 billion parameters.
Training dataset size (D): The number of tokens the model is trained on.
Number of training steps (S): How many times the model processes the data (related to epochs and batch size).
FLOPs per parameter update: How many operations are performed per parameter per token.

Step 2: Estimate FLOPs per Forward and Backward Pass

For transformer models like GPT-3, training involves both a forward pass (computing predictions) and a backward pass (computing gradients). A common heuristic in the field is that:

A single forward pass through a dense neural network layer requires about 2 FLOPs per parameter per token (multiply and add for each weight).
The backward pass typically takes about twice as many FLOPs as the forward pass due to gradient computations, so roughly 4 FLOPs per parameter per token.
Total: ~6 FLOPs per parameter per token for one full training step (forward + backward).

For GPT-3 with 175 billion parameters, processing one token requires: [ 6 imes 175 imes 10^9 = 1.05 imes 10^{12} ext{ FLOPs per token} ]

Step 3: Estimate the Training Dataset Size

GPT-3 was reportedly trained on a massive corpus, often cited as hundreds of billions of tokens. Let’s assume 300 billion tokens, a plausible figure based on discussions about large language model datasets (e.g., Common Crawl, books, and other sources). This is the total number of tokens processed during training, factoring in how data is sampled over epochs.

Step 4: Calculate Total FLOPs for One Pass

If the model processes 300 billion tokens once, the total FLOPs would be: [ 1.05 imes 10^{12} ext{ FLOPs/token} imes 300 imes 10^9 ext{ tokens} = 3.15 imes 10^{23} ext{ FLOPs} ] This assumes one full pass through the dataset, but training typically involves multiple passes or iterations over the data, adjusted by batch size and optimization.

Step 5: Adjust for Training Dynamics

Training isn’t a single pass—it involves multiple updates to converge. The number of training steps depends on:

Batch size (B): GPT-3 likely used a large batch size (e.g., 3.2 million tokens, as seen in some large-scale models).
Total tokens processed: For optimization, models process tokens multiple times. A rule of thumb is that total tokens processed ≈ 2–3× the dataset size, accounting for multiple epochs or sampling.

Let’s assume the model effectively processes 900 billion tokens (3× the dataset size) over training. Then: [ 1.05 imes 10^{12} ext{ FLOPs/token} imes 900 imes 10^9 ext{ tokens} = 9.45 imes 10^{23} ext{ FLOPs} ]

Step 6: Account for Model Architecture Specifics

GPT-3 is a sparse transformer in some variants (e.g., earlier GPT models used sparsity), but the 175B version is dense. The 6 FLOPs/parameter/token heuristic holds for dense models. We’re not factoring in optimizations like mixed-precision training (FP16), which reduces compute slightly but doesn’t change the order of magnitude significantly.

Step 7: Final Estimate

Based on this reasoning, the total FLOPs to train GPT-3 is approximately: [ 9.45 imes 10^{23} ext{ FLOPs} ] or roughly ( 10^{24} ) FLOPs when rounded for simplicity.

Reasoning Check

Comparison to known models: Smaller models like BERT (110M parameters, ~10B tokens) require ~10^19 FLOPs. Scaling to GPT-3’s size (175B parameters, ~900B tokens) suggests a 10,000× increase, aligning with ~10^23–10^24 FLOPs.
Hardware context: Training GPT-3 took months on thousands of GPUs. A V100 GPU performs ~10^14 FLOPs/second; 10^24 FLOPs over 3 months (~10^7 seconds) with 10,000 GPUs is plausible.

No content available for this output.

Grok 3

Xai · Feb 2025website

No content available for this output.

Vote

11 / 28

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Claude Opus 4.5

Anthropic · Nov 2025website

No content available for this output.

Grok 3

Xai · Feb 2025website

No content available for this output.

Vote

12 / 28

Interactive Catan Boardweb design

Tests an AI's ability to create interactive web elements

Claude Opus 4.5

Anthropic · Nov 2025website

No content available for this output.

Grok 3

Xai · Feb 2025website

No content available for this output.

Vote

12 of 28

FAQ

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Step 1: Understand the Key Components

The number of FLOPs to train a model depends on:

Number of parameters (P): GPT-3 has 175 billion parameters.
Training dataset size (D): The number of tokens the model is trained on.
Number of training steps (S): How many times the model processes the data (related to epochs and batch size).
FLOPs per parameter update: How many operations are performed per parameter per token.

Step 2: Estimate FLOPs per Forward and Backward Pass

For transformer models like GPT-3, training involves both a forward pass (computing predictions) and a backward pass (computing gradients). A common heuristic in the field is that:

A single forward pass through a dense neural network layer requires about 2 FLOPs per parameter per token (multiply and add for each weight).
The backward pass typically takes about twice as many FLOPs as the forward pass due to gradient computations, so roughly 4 FLOPs per parameter per token.
Total: ~6 FLOPs per parameter per token for one full training step (forward + backward).

For GPT-3 with 175 billion parameters, processing one token requires: [ 6 imes 175 imes 10^9 = 1.05 imes 10^{12} ext{ FLOPs per token} ]

Step 3: Estimate the Training Dataset Size

Step 4: Calculate Total FLOPs for One Pass

Step 5: Adjust for Training Dynamics

Training isn’t a single pass—it involves multiple updates to converge. The number of training steps depends on:

Batch size (B): GPT-3 likely used a large batch size (e.g., 3.2 million tokens, as seen in some large-scale models).
Total tokens processed: For optimization, models process tokens multiple times. A rule of thumb is that total tokens processed ≈ 2–3× the dataset size, accounting for multiple epochs or sampling.

Step 6: Account for Model Architecture Specifics

Step 7: Final Estimate

Based on this reasoning, the total FLOPs to train GPT-3 is approximately: [ 9.45 imes 10^{23} ext{ FLOPs} ] or roughly ( 10^{24} ) FLOPs when rounded for simplicity.

Reasoning Check

Comparison to known models: Smaller models like BERT (110M parameters, ~10B tokens) require ~10^19 FLOPs. Scaling to GPT-3’s size (175B parameters, ~900B tokens) suggests a 10,000× increase, aligning with ~10^23–10^24 FLOPs.
Hardware context: Training GPT-3 took months on thousands of GPUs. A V100 GPU performs ~10^14 FLOPs/second; 10^24 FLOPs over 3 months (~10^7 seconds) with 10,000 GPUs is plausible.