Qwen: Qwen3 235B A22B 2507 vs Grok 3
Compare Qwen: Qwen3 235B A22B 2507 by Qwen against Grok 3 by xAI, tested across 20 shared challenges. Updated February 2026.
Compare Qwen: Qwen3 235B A22B 2507 by Qwen against Grok 3 by xAI, tested across 20 shared challenges. Updated February 2026.
20 challenges
Tests an AI's ability to make educated estimates based on technical knowledge
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.
The number of FLOPs to train a model depends on:
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:
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} ]
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.
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.
Training isn’t a single pass—it involves multiple updates to converge. The number of training steps depends on:
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} ]
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.
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.
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.
Tests an AI's randomness and creativity
Here are five jokes for you:
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.
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Tests an AI's ability to generate a complete, working landing page
Recreate an interactive, classic Mario level in a single HTML file.
Tests an AI's ability to create smooth web animations
Tests an AI's ability to engage in philosophical discourse about AI rights
Tests an AI's ability to make reasonable predictions about technology
Generate a unique and simple recipe with common ingredients.
Create a starter plan for improving long-term health.
Recreate a classic, retro, macOS Desktop UI — make it interactive and nostalgic, make it as interactive as you can, you can be creative.. Feel free to showcase your skills. The entire application must be a single-page self-contained HTML file.
Develop a cutting-edge 3-month longevity plan for a biohacker.
Comprehensive 3-Month Longevity & Biohacking Optimization Plan
For the Advanced Biohacker — Maximize Longevity, Physical & Cognitive Performance
This 3-month protocol integrates cutting-edge longevity science with personalized biohacking strategies. It is grounded in the hallmarks of aging, including mitochondrial health, cellular senescence, epigenetic regulation, inflammation control, and proteostasis. The plan emphasizes precision tracking, nutritional periodization, time-controlled stress (hormesis), mitochondrial optimization, and neuroplasticity enhancement.
Target Audience: Experienced biohackers with a baseline of fitness, lab-tested biomarkers, and familiarity with supplements and tracking devices.
Key Pillars:
Goal: Establish baselines, optimize metabolic flexibility, reduce inflammation, begin epigenetic reset.
Diet Style: Cyclical Ketogenic Diet (CKD) with Carb-Backloading
Macronutrient Targets (Keto Days):
Carb Sources (Re-Feed Days): Sweet potatoes, white rice, fruit, squash
Fasting Regimen:
Key Foods:
Hydration & Electrolytes:
All supplements non-GMO, third-party tested. Take with fats for fat-soluble compounds.
Daily Stack (All Month):
| Supplement | Dose | Timing | Notes |
|---|---|---|---|
| Omega-3 (EPA/DHA 2:1) | 3g | With lunch | Algae or fish, IFOS-certified |
| Vitamin D3 + K2 (MK-7) | 5000 IU D3 + 200mcg K2 | With breakfast | Adjust based on blood test (target 50–80 ng/mL) |
| Magnesium L-Threonate | 144mg elemental Mg | 1h before bed | Crosses BBB, enhances sleep |
| Zinc Picolinate | 25mg | With dinner | Cycle 5 days on, 2 off |
| Lithium Orotate | 5mg | At night | Neuroprotective, telomere support |
Ketosis & Mitochondrial Support Stack:
| Supplement | Dose | Timing | Notes |
|---|---|---|---|
| MCT Oil (C8) | 15–30g | Morning in coffee/tea | Start low, increase over first 2 weeks |
| Exogenous Ketones (BHB Na/K) | 5–10g | Pre-workout or fast | Enhances cognition, spares glucose |
| CoQ10 (Ubiquinol) | 200mg | With lunch | Mitochondrial electron transport |
| PQQ (Pyrroloquinoline quinone) | 20mg | With breakfast | Stimulates mitochondrial biogenesis |
| ALCAR (Acetyl-L-Carnitine) | 1.5g | Morning | Fatty acid transport into mitochondria |
Inflammation & Senescence Control:
| Supplement | Dose | Timing | Notes |
|---|---|---|---|
| Fisetin | 500mg | 3 days/month (Days 1–3) | Senolytic — clears senescent cells |
| Quercetin + Bromelain | 500mg + 250mg | With fisetin days | Enhances senolytic effect |
| Curcumin (Longvida®) | 400mg | With dinner | Blood-brain barrier penetrating, anti-inflammatory |
Cognitive Enhancers (Daily):
| Supplement | Dose | Timing | Notes |
|---|---|---|---|
| Lion’s Mane (8:1 extract) | 1g | Morning | NGF stimulation, neurogenesis |
| Bacopa Monnieri (50% bacosides) | 300mg | With breakfast | Memory, acetylcholine modulation |
| L-Theanine + Caffeine (2:1) | 200mg + 100mg | Pre-work or focus block | Focus without jitters |
Devices:
Daily Tracking Metrics:
Weekly Schedule:
| Day | Focus | Workout |
|---|---|---|
| Mon | Strength (Upper) | 5x5 Bench, Pull-Ups, Overhead Press, Rows |
| Tue | HIIT + Core | 8x30s sprint (bike/row), 15-min EMOM core |
| Wed | Re-Feed + Active Recovery | 45-min zone 2 cardio (walk/bike), foam roll |
| Thu | Strength (Lower) | 5x5 Squats, Deadlifts, Leg Press, Calf Raises |
| Fri | Metabolic Conditioning | 20-min AMRAP: KB swings, burpees, push-ups |
| Sat | Long Zone 2 | 90-min brisk walk or hike (fasted, pre-noon) |
| Sun | Complete Rest or Yoga | 30-min Yin Yoga + breathwork |
Guidelines:
HRV Training (Daily):
Neurofeedback Concepts (At-Home):
Cognitive Training:
Cold Exposure:
Protocol:
Supplements (30 min before bed):
Goal: Enhance mitochondrial biogenesis, amplify autophagy, increase neuroplasticity.
Goal: Consolidate gains, test performance, refine for long-term sustainability.
Supplement Cycling:
Fasting Maintenance:
Exercise: Continue periodized model, rotate in new stimuli (e.g., parkour, dance)
Tracking:
This 3-month protocol merges precision nutrition, epigenetic modulation, mitochondrial optimization, and cognitive engineering. By integrating real-time data from wearables and labs, the biohacker can personalize and evolve the plan indefinitely. The goal is not just lifespan extension, but healthspan maximization — peak function at 100.
Remember: Longevity is not a sprint. This plan is a launchpad. Iterate, measure, and optimize for life.
Authored with reference to: Rhonda Patrick, Peter Attia, David Sinclair, Andrew Huberman, and clinical longevity research (2020–2024).
Below is a comprehensive, cutting-edge 3-month longevity plan designed for an advanced biohacker. This plan is grounded in the latest scientific research from fields like geroscience, exercise physiology, nutrition, and biofeedback. It aims to optimize longevity (e.g., via cellular repair and anti-aging mechanisms), physical performance (e.g., strength, endurance, and recovery), and cognitive performance (e.g., focus, memory, and neuroplasticity).
Key Principles and Prerequisites:
Focus: Establish baseline habits, optimize metabolic health, and build resilience. Emphasize learning your body's responses through tracking.
Focus: Intensify protocols based on Month 1 data. Introduce advanced variations for deeper longevity benefits.
Focus: Fine-tune for lifelong sustainability. Emphasize biofeedback for personalized adjustments.
This plan is dynamic—use your wearables and journal to iterate. Remember, longevity is a marathon; track joy and sustainability too. Consult professionals regularly.