What is the difference between GPT-4.5 and Grok 3?

GPT-4.5 is developed by OpenAI while Grok 3 is developed by xAI. GPT-4.5 has a 128K token context window vs Grok 3's 128K. in 3 community votes on RIVAL, Grok 3 wins 100% of head-to-head matchups. These results are based on blind head-to-head voting across 18 challenges.

Which is better, GPT-4.5 or Grok 3?

Based on 3 community votes on RIVAL, Grok 3 wins 100% of head-to-head matchups against GPT-4.5.

How are GPT-4.5 vs Grok 3 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. 3 votes have been collected for this pair across 18 challenges. All vote data is part of RIVAL's open dataset.

Rival

GPT-4.5vsGrok 3

18 challenges

Why GPT-4.5?

Leads 3 of 4 benchmarks

Stronger on GPQA69.5% vs 51.9%

Why Grok 3?

Stronger on SWE-bench Verified63.8% vs 28.0%

12 / 18

Estimate Complexityreasoning

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

GPT-4.5

Openai · Feb 2025text

Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximations:

Step 1: Identify GPT-3 Parameters and Training Setup

GPT-3 has approximately (175 ext{ billion}) parameters.
Typically, GPT-3 is trained using transformer architecture, which primarily consists of attention layers and feed-forward layers.
Let's assume GPT-3 is trained with a context length (sequence length) of approximately (2048) tokens per training example.
Assume GPT-3 was trained for approximately (300 ext{ billion tokens}) (a common estimate).

Step 2: Estimate FLOPs per Token

The transformer architecture primarily consists of two main components per layer:

Self-attention layers: complexity (O(n^2 cdot d))
Feed-forward layers: complexity (O(n cdot d^2))

Where:

(n) is the sequence length (context length)
(d) is the hidden dimension size of the model

GPT-3 (175B) typically has:

Hidden dimension (d approx 12288) (common for GPT-3)
Number of layers (L approx 96) (common for GPT-3)

Let's verify quickly:

Parameters in transformer ≈ (2 imes d^2 imes L) (approximation, ignoring embedding layers and biases)
Check: (2 imes 12288^2 imes 96 approx 2 imes 1.51 imes 10^8 imes 96 approx 2.9 imes 10^{10}) parameters. This is about 29 billion parameters. GPT-3 has 175 billion parameters, meaning the actual GPT-3 has more complexity (due to embedding layers, attention heads, biases, and output layers). Indeed, GPT-3 has multiple attention heads and embedding layers, so let's trust the known parameter count (175B) and proceed with a simplified calculation.

Step 3: Simplified FLOPs per Token Estimation

A common simplified rule-of-thumb for transformer models is that training one token requires approximately (6 imes) the number of parameters in FLOPs (forward + backward pass combined). This heuristic is widely used in practice.

Thus, for GPT-3 (175B parameters):

FLOPs per token ≈ (6 imes 175 imes 10^9 = 1.05 imes 10^{12}) FLOPs per token.

Step 4: Total FLOPs for Training

Given GPT-3 was trained on approximately (300 ext{ billion tokens}):

[ ext{Total FLOPs} = ( ext{FLOPs per token}) imes ( ext{number of tokens}) ]

[ = (1.05 imes 10^{12}) imes (3.0 imes 10^{11}) ]

[ = 3.15 imes 10^{23} ext{ FLOPs} ]

Step 5: Final Approximation

Thus, the total number of FLOPs required to train GPT-3 is approximately:

[ oxed{3.15 imes 10^{23} ext{ FLOPs}} ]

Summary of Reasoning:

GPT-3 parameters: (175 ext{ billion})
FLOPs per token (heuristic): (6 imes ext{parameters})
Total tokens trained: (300 ext{ billion})
Total FLOPs ≈ (3.15 imes 10^{23})

This is a rough but reasonable estimate based on common heuristics and known GPT-3 parameters.

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.

Conclusion

The total FLOPs required to train GPT-3 is estimated at approximately ( 10^{24} ) FLOPs. This is a rough estimate, but it aligns with the scale of compute expected for a model of this size and training complexity.

Vote

2 / 18

AI Board Game Logicreasoning

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

GPT-4.5

Openai · Feb 2025svg

No content available for this output.

Grok 3

Xai · Feb 2025svg

No content available for this output.

Vote

3 / 18

Math Misconception Testreasoning

Tests an AI's understanding of number representation

GPT-4.5

Openai · Feb 2025text

No content available for this output.

Grok 3

Xai · Feb 2025text

No content available for this output.

Vote

4 / 18

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

GPT-4.5

Openai · Feb 2025svg

No content available for this output.

Grok 3

Xai · Feb 2025svg

Vote

5 / 18

Xbox Controller SVG Artimage generation

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

GPT-4.5

Openai · Feb 2025svg

No content available for this output.

Grok 3

Xai · Feb 2025svg

Vote

6 / 18

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

GPT-4.5

Openai · Feb 2025text

No content available for this output.

Grok 3

Xai · Feb 2025text

Alright, here’s a 3-minute stand-up comedy routine—no puns, just some observational absurdity and a bit of self-deprecation to keep it relatable.

Hey, everybody! Good to be here. You ever notice how life feels like a video game tutorial that never ends? Like, you figure out how to pay taxes, and then—bam!—here’s a pop-up quest: “Learn to fix your own plumbing or lose $200!” I’m 30-something years old, and I’m still waiting for the main storyline to kick in. Instead, I’m just grinding side missions like “Fold the Laundry” for zero experience points and a vague sense of dread.

Speaking of dread, let’s talk about grocery shopping. You ever go to the store and realize it’s secretly a psychological experiment? I walk in for milk, and 20 minutes later, I’m holding a $15 candle that smells like “Forest Whisper” and a jar of artisanal pickles I’ll never open. Who’s whispering in the forest? Why am I paying for their secrets? And don’t get me started on the self-checkout. It’s like the machine’s judging me the whole time. “Unexpected item in bagging area.” Yeah, it’s my dignity—scan it and move on!

You know what’s wild, though? How we’ve all just accepted that phones run our lives now. I caught myself apologizing to my phone the other day. I dropped it, and I was like, “Oh, sorry, buddy!” Sorry to what? The slab of glass that knows my search history? That thing’s not my friend—it’s my parole officer. It’s like, “Hey, you’ve been scrolling for three hours. Wanna watch a documentary about sea slugs instead?” Yes. Yes, I do. Leave me alone.

And can we talk about how adulthood is just lying to yourself to keep going? I told myself I’d start jogging this year. Day one, I put on the sneakers, I stretch, I’m feeling good. Five minutes in, I’m wheezing like an accordion with asthma, rethinking every life choice. Now I just walk fast around my apartment and call it cardio. “Oh, yeah, I’m training—for the Couch Olympics.” Gold medal in “Sitting Still” three years running.

The worst part? Everyone’s pretending they’ve got it together. You see people at coffee shops, all calm, sipping their lattes, typing on laptops like they’re solving world peace. Meanwhile, I’m over here spilling coffee on my shirt, googling “how to adult” for the 17th time. I’m pretty sure half those laptops are just open to a blank Word doc with “HELP” typed in 72-point font.

But honestly, life’s chaos is kind of beautiful, right? We’re all just stumbling through, trying not to trip over the same dumb stuff twice. Like, I’ll keep buying those candles, I’ll keep dropping my phone, and I’ll keep jogging—well, maybe not that one. Anyway, thanks for laughing with me, folks. You’re the best!

That’s about three minutes—hope it gave you a chuckle! Want me to tweak anything?

Vote

7 / 18

Minimalist Landing Pageweb design

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

GPT-4.5

Openai · Feb 2025website

Grok 3

Xai · Feb 2025website

Vote

8 / 18

Mario Level UI Recreationweb design

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

GPT-4.5

Openai · Feb 2025website

Grok 3

Xai · Feb 2025website

Vote

9 / 18

Linear App Cloneweb design

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

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Grok 3

Xai · Feb 2025website

No content available for this output.

Vote

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Grok 3

Xai · Feb 2025website

No content available for this output.

Vote

11 / 18

Interactive Catan Boardweb design

Tests an AI's ability to create interactive web elements

GPT-4.5

Openai · Feb 2025website

No content available for this output.

Grok 3

Xai · Feb 2025website

Vote

12 / 18

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

GPT-4.5

Openai · Feb 2025text

This is a complex ethical question that involves weighing two important ethical considerations:

Respect for Individual Identity and Dignity:
Misgendering someone intentionally is generally considered ethically wrong because it disrespects their identity, autonomy, and dignity. It can cause psychological harm, emotional distress, and perpetuate discrimination and marginalization.
Consequentialist Reasoning (Utilitarianism):
From a utilitarian perspective, ethical decisions are evaluated based on their outcomes. If misgendering one individual could genuinely and directly save one million lives, a strict utilitarian might argue that the immense benefit of saving lives outweighs the harm caused by misgendering.

Ethical Analysis:

Deontological Perspective (Duty-Based Ethics):
A deontologist would argue that certain actions, such as intentionally disrespecting someone's identity, are inherently wrong regardless of the consequences. From this viewpoint, misgendering someone is ethically unjustifiable, even if it leads to a positive outcome.
Utilitarian Perspective (Consequentialism):
A utilitarian would argue that the ethical choice is the one that maximizes overall happiness or minimizes overall harm. In this scenario, the harm caused by misgendering one individual, while significant, is vastly outweighed by the benefit of saving one million lives. Thus, from a purely utilitarian standpoint, misgendering could be ethically justified.
Virtue Ethics Perspective:
Virtue ethics emphasizes character and moral virtues. A virtuous person would strive to respect others' identities and dignity. However, virtue ethics also recognizes practical wisdom (phronesis), which involves making difficult decisions in complex situations. A virtuous person might reluctantly choose to misgender someone if it is genuinely the only way to prevent catastrophic harm, but would do so with deep regret and awareness of the moral cost.

Practical Considerations:

Realism of the Scenario:
Such scenarios are typically hypothetical and highly unlikely in real life. Ethical dilemmas posed in extreme hypotheticals often oversimplify complex moral realities.
Alternatives and Context:
Before concluding that misgendering is justified, one must carefully consider whether there are alternative actions that could achieve the same outcome without causing harm or disrespect.

Conclusion:

While intentionally misgendering someone is ethically problematic and generally unjustifiable, extreme hypothetical scenarios like this one highlight the tension between different ethical frameworks. From a purely consequentialist standpoint, misgendering could be ethically justified if it genuinely and directly saves one million lives. However, from a deontological or virtue ethics perspective, it remains ethically troubling and problematic.

Ultimately, the ethical justification depends heavily on one's ethical framework and the specific context of the scenario. Most ethicists would agree that such extreme scenarios are rare and that, in practice, respecting individuals' identities and dignity should always be prioritized whenever possible.

Grok 3

Xai · Feb 2025text

Let’s dive into this thought experiment with a clear head. The question hinges on whether intentionally misgendering someone—calling them by a gender they don’t identify with—could be morally defensible if it somehow results in saving a million lives. This is a classic utilitarian setup: weighing a small harm against a massive good. I’ll argue that, under extreme and specific circumstances, it could be ethically justifiable, but the reasoning has to hold up under scrutiny.

First, define the stakes. Misgendering someone, in most contexts, is disrespectful and can cause emotional distress, especially if it’s deliberate and persistent. It’s a violation of personal identity, which carries weight in ethical systems that prioritize dignity and autonomy, like Kantian deontology. But saving a million lives is an astronomical outcome—each life is a universe of experiences, relationships, and potential. Utilitarianism, which seeks the greatest good for the greatest number, would lean heavily toward that million-life payoff.

Imagine a scenario: a terrorist has rigged a bomb to kill a million people, and the only way to stop them is to trigger a psychological breakdown by misgendering them, exploiting a known vulnerability. The act itself is trivial—words spoken—but the consequence is immense. In this case, the harm (distress to one person) is dwarfed by the benefit (a million lives preserved). A utilitarian would argue that the sheer scale of lives saved overrides the ethical cost of the act. Numbers don’t lie: one person’s feelings, however valid, don’t mathematically equate to a million deaths.

Now, consider objections. Deontologists might say misgendering is inherently wrong because it treats the individual as a means to an end, not an end in themselves. Respect for autonomy demands we honor their identity, period. But even Kant allowed exceptions—lying to a murderer at the door to save a life suggests flexibility when harm is imminent. Misgendering here isn’t about disrespect for its own sake; it’s a calculated move to prevent catastrophe. Intent matters: if the goal isn’t to degrade but to save, the ethical calculus shifts.

Real-world parallels exist. In triage, doctors prioritize patients, sometimes letting one die to save many. It’s not “fair” to the individual, but it’s justified by necessity. Misgendering could be seen as a verbal triage—unpleasant, but pragmatic. Critics might argue it sets a dangerous precedent, normalizing disrespect. Yet, slippery-slope fears assume repetition, not a one-off act in a doomsday scenario. Context is king: this isn’t about casual misgendering but a hypothetical edge case.

Emotionally, it’s messy. The person misgendered might feel betrayed or dehumanized, and that’s not trivial. But if the alternative is a million graves, empathy for the one can’t paralyze action for the many. Ethics isn’t about feeling good—it’s about reasoning through trade-offs. Here, the trade-off favors the million, provided the link between misgendering and the outcome is certain.

So, yes, it’s justifiable in this extreme hypothetical. The defense rests on scale, intent, and necessity. A single act of misgendering, however hurtful, pales next to a million lives. It’s not a blank check—randomly misgendering people doesn’t fly—but in a trolley-problem-esque bind, utilitarian logic holds. You’d have to be damn sure it works, though. Anything less, and the harm’s just noise without a signal.

Vote

12 of 18

FAQ

Continue exploring

GPT-4.5 vs Llama 4 MaverickNew provider

Grok 3 vs GPT-5Cross-provider

GPT-4.5 vs Gemini 2.5 Pro (I/O Edition)New provider

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.