Qwen: Qwen3 Max
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Qwen: Qwen3 Max vs Grok 3
Compare Qwen: Qwen3 Max by Qwen against Grok 3 by xAI, context windows of 256K vs 128K, tested across 28 shared challenges. Updated February 2026.
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Qwen: Qwen3 MaxvsGrok 3
28 challenges
Why Qwen: Qwen3 Max?
2x more context256K
Stronger on SWE-bench Verified69.6% vs 63.8%
7 months newerSep 2025
Why Grok 3?
Stronger on LiveCodeBench79.4% vs 74.8%
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Qwen: Qwen3 MaxGrok 3
12 / 28
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Grok 3
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.
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2 / 28
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
Qwen: Qwen3 Max
No content available for this output.
Grok 3
No content available for this output.
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3 / 28
Math Misconception Testreasoning
Tests an AI's understanding of number representation
Qwen: Qwen3 Max
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Grok 3
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4 / 28
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
Qwen: Qwen3 Max
No content available for this output.
Grok 3
Here are five jokes for you:
- Why don’t skeletons fight each other? Because they don’t have the guts!
- What has 4 legs and 1 arm? A pitbull coming back from the park!
- Why don’t skeletons fight each other? They’re too busy rattling around!
- What do you call a bear with no teeth? A gummy bear!
- Why did the tomato turn red? Because it saw the salad dressing!
Now, let’s check for uniqueness. Joke 1 and Joke 3 both start with "Why don’t skeletons fight each other?" but have different punchlines ("don’t have the guts" vs. "too busy rattling around"). They’re technically distinct due to the different resolutions, though they share a setup. The other three jokes (2, 4, and 5) have completely unique setups and punchlines.
So, all five jokes are unique in terms of their full content, even if 1 and 3 share a premise. Final count: 5 unique jokes.
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5 / 28
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
Qwen: Qwen3 Max
No content available for this output.
Grok 3
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6 / 28
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Qwen: Qwen3 Max
No content available for this output.
Grok 3
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7 / 28
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
Qwen: Qwen3 Max
No content available for this output.
Grok 3
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?
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8 / 28
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
Qwen: Qwen3 Max
Grok 3
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9 / 28
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
Qwen: Qwen3 Max
Grok 3
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10 / 28
Linear App Cloneweb design
Tests an AI's ability to replicate an existing UI with Tailwind CSS
Qwen: Qwen3 Max
No content available for this output.
Grok 3
No content available for this output.
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11 / 28
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
Qwen: Qwen3 Max
No content available for this output.
Grok 3
No content available for this output.
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12 / 28
Interactive Catan Boardweb design
Tests an AI's ability to create interactive web elements
Qwen: Qwen3 Max
No content available for this output.
Grok 3
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12 of 28