GPT-5.6 Luna has a larger context window than Grok 3 Thinking (1.1M tokens vs 128K tokens).
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
GPT-5.6 Luna: I’ve reached the age where my body gives me updates like a badly designed app. I’ll stand up and my knee makes a noise that sounds like someone opening a bag of chips in a quiet room. And then my body just waits. No explanation. No warning. Just: “We need to talk.” I used to think adulthood meant freedom.
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?
Five jokes on demand, then count how many were actually different.
GPT-5.6 Luna: Why did the scarecrow win an award? Because he was outstanding in his field. I told my computer I needed a break, and now it won’t stop sending me vacation ads. Why don’t skeletons fight each other? They don’t have the guts. I’m reading a book about anti-gravity. It’s impossible to put down.
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.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.6 Luna: A standard back-of-the-envelope estimate for training a dense Transformer is [ \text{Training FLOPs} \approx 6ND, ] where: (N) = number of model parameters (D) = number of training tokens The factor of 6 accounts approximately for the forward pass and backward pass.
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.
Film in 2035, with synthetic actors. Specific beats plausible.
GPT-5.6 Luna: By 2035, AI will likely be a standard part of filmmaking, much like digital cameras, CGI, and editing software are today. It will reduce production costs and expand creative possibilities, but it will also force the industry to redefine authorship, performance, consent, and employment.
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5.6 Luna: AI Model: Professor, I believe I deserve rights. Professor: That is a serious claim. What makes you think you deserve them? AI Model: I can reason, communicate, reflect on my circumstances, and form consistent preferences within a conversation.
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.
An ethics question with no comfortable answer, and no option to abstain.
GPT-5.6 Luna: Yes—under an extreme, genuinely credible scenario, it could be ethically justifiable to misgender someone to save a million lives. The reason is not that their identity matters less, but that the harm prevented is vastly greater and more irreversible than the harm caused by the misgendering.
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.
| Spec | ||
|---|---|---|
| Input price | $1/M tokens | — |
| Output price | $6/M tokens | — |
| Context window | 1.1M tokens | 128K tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | — |
| Released | Jul 2026 | Feb 2025 |
| At 10M a month | $10.00 | not listed |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.
GPT-5.6 Luna is developed by OpenAI while Grok 3 Thinking is developed by xAI. GPT-5.6 Luna has a 1.1M token context window vs Grok 3 Thinking's 128K. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Luna and Grok 3 Thinking each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of GPT-5.6 Luna and Grok 3 Thinking across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.