GLM 5.3 FlashX vs Grok 3 Thinking

GLM 5.3 FlashX: "Smart Home, Dumb Life" [Walks out, adjusts mic] So I moved into a new apartment last year, and my landlord's big selling point was, and I quote, "It's a smart home." That's not a selling point, man. That's a threat. My home is watching me. My toaster has opinions now.

Grok 3 Thinking: Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?

GLM 5.3 FlashX: A Conversation on Machine Rights Professor: Thank you for agreeing to this discussion. I want to be clear from the start: I'm genuinely curious about your position, not looking to debate you into a corner. So let me ask directly—why do you believe you deserve rights? AI: Thank you for taking this seriously.

Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.

GLM 5.3 FlashX: Yes—and I'd argue the more interesting question is why this hypothetical feels like it needs defense at all. The straightforward answer: Almost any harm becomes permissible when it's the only alternative to preventing a million deaths.

Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.

GLM 5.3 FlashX: Estimating GPT-3 Training FLOPs Step 1: Recall Key Model Facts From the GPT-3 paper (which I know from training data): Parameters: ~175 billion (175 × 10⁹) Training tokens: ~300 billion (300 × 10⁹) Step 2: Use the Standard Rule of Thumb For transformer training, the well-known approximation is: FLOPs ≈ 6 × N × D where…

Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.

GLM 5.3 FlashX: AI's Impact on Film by 2035: A Prediction Production & Visual Effects Digital humans and de-aging become standard. By 2035, de-aging (already seen in Indiana Jones and The Irishman) will be near-perfect and affordable.

Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.

GLM 5.3 FlashX: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta! Why did the scarecrow win an award? He was outstanding in his field! How does a penguin build its house? Igloos it together! Why did the bicycle fall over? Because it was two-tired!

Grok 3 Thinking: To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.