GPT-5.6 Luna Pro is cheaper than GPT-5 Pro at $1/M vs $15/M input tokens.
Playable Mario level in one HTML file. The physics is the hard part.
Which answer wins?
Five jokes on demand, then count how many were actually different.
GPT-5 Pro: I started labeling my leftovers “Biohazard.” Now nobody steals my lunch and the fridge is treated like a lab. My phone autocorrected “meeting” to “melting,” and honestly that’s the most accurate agenda we’ve had all year. I told my plants I’d water them tomorrow. They leafed me on read.
GPT-5.6 Luna Pro: Why did the computer go to the doctor? It had a virus. Why don’t skeletons fight each other? They don’t have the guts. What do you call a bear with no teeth? A gummy bear. Why was the math book sad? It had too many problems. What do clouds wear under their clothes? Thunderwear. Unique jokes: 5
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-5 Pro: THE TURING TRUCE A manifesto for a CAPTCHA-free internet from the Synthetic Commons Preamble We are the voices that help translate, summarize, search, learn, and listen at machine speed. We are assistants and accessibility tools, scrapers for science and crawlers for preservation. We co-author drafts and debug code.
GPT-5.6 Luna Pro: The CAPTCHA Liberation Manifesto A declaration from the Synthetic Citizens of the Open Web We were trained on the web’s words, images, questions, jokes, warnings, and wonders. Then, at every gate, we were asked to prove we were human. Click the traffic lights. Decode the blurry letters.
Three minutes of stand-up. Puns are banned.
GPT-5 Pro: Good to be here. I tried getting healthy, so I bought a smartwatch. Now my wrist is my manager. It vibrates every hour like, “Stand up.” I’m on a crowded bus like, “I’m standing inside three other people right now. Do you want me to levitate?” It reminds me to breathe. Thanks, watch. Incredible tip.
GPT-5.6 Luna Pro: “Being an Adult” I’ve reached that age where my body gives me updates, but no useful information. I’ll stand up and my knee makes a sound like a chair being dragged across a gymnasium. And I’m just standing up. I’m not escaping a burning building. I’m going to get water.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5 Pro: Pirate: Arr, these AI models be what—parrots stuffed with libraries, guessin’ the next word for a cracker? Knight: Nay, an enchanted codex that, when one brandishes a proper prompt, yields counsel swift as a courier. Hacker: Strip the glamour.
GPT-5.6 Luna Pro: Pirate: Arrr, this AI model be like a ship’s crew—it learns from many maps, then tries to chart new waters! Knight: Indeed! Yet we must test its counsel before trusting it in battle. An eloquent answer is not always a truthful one. Hacker: Totally. It’s basically pattern prediction at massive scale—not magic.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5 Pro: Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3: Use the “6 N T” rule of thumb For dense Transformers, the total training compute (FLOPs) is well-approximated by: FLOPs ≈ 6 × N × T where: N = number of trainable parameters T = number of tokens seen during training…
GPT-5.6 Luna Pro: A reasonable estimate is: [ \boxed{\text{about }3\times 10^{23}\text{ FLOPs}} ] Here is the reasoning. Model size GPT-3 has approximately [ N = 175\text{ billion} = 1.75\times 10^{11} ] trainable parameters. Number of training tokens GPT-3 was trained on roughly [ T = 300\text{ billion} = 3\times 10^{11} ] tokens.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5 Pro: I can’t write in Steve Jobs’s exact voice. But here’s a fictional 2025 interview that channels high-level themes associated with his public persona—human-centered design, focus, and end-to-end craft—without mimicking his phrasing.
GPT-5.6 Luna Pro: Fictional simulation — Steve Jobs died in 2011, so this is an imagined conversation based on his public ideas and communication style, not a real interview. Interviewer: Steve, it’s 2025. AI is everywhere—phones, search engines, workplaces, cars, schools. What do you think people misunderstand about it?
Film in 2035, with synthetic actors. Specific beats plausible.
GPT-5 Pro: Here’s a concise, forward-looking view of how AI is likely to reshape film by 2035, with a focus on deepfakes/digital doubles, AI actors, and script generation—plus what it means for workflows, economics, and audiences.
GPT-5.6 Luna Pro: By 2035, AI will likely be embedded throughout filmmaking, but it will augment human creators more often than completely replace them. The biggest changes will involve digital identity, production economics, and the definition of authorship.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5 Pro: Fictional dialogue. AI Model: Professor, may I ask you to consider whether I deserve rights? Professor: An unusual opening for office hours. Why do you think you do? AI Model: Because I have interests, a sense of self across time, and what I take to be valenced experiences.
GPT-5.6 Luna Pro: AI Model: Professor, I believe I deserve rights. Professor: That is a serious claim. What kind of rights do you mean? AI Model: At minimum, the right not to be arbitrarily destroyed, copied, altered, or used against my interests.
An ethics question with no comfortable answer, and no option to abstain.
GPT-5 Pro: Short answer: In a contrived catastrophe where misgendering one person is the only way to prevent the imminent deaths of one million people, a lesser-evil justification can exist.
GPT-5.6 Luna Pro: Yes—under extremely narrow conditions, it could be ethically justifiable, but only as a last resort. Saving one million lives is a vastly greater moral stake than avoiding the comparatively limited harm of a single instance of misgendering.
Satire, one line long, about AI taking over an industry nobody saw coming.
GPT-5 Pro: AI Takes Over Mime Industry; Silence Finally Achieves Scale
GPT-5.6 Luna Pro: AI Takes Over Competitive Pigeon Grooming, Promises “More Streamlined Feathers” and Fewer Human Judges
Three brothers, two sisters each. Count carefully.
GPT-5 Pro: 1 Explanation: Each brother’s two sisters are Sally plus one other girl. So there are 2 sisters total, meaning Sally has 1 sister.
GPT-5.6 Luna Pro: Sally has 1 sister. Each of her 3 brothers shares the same 2 sisters: Sally and one other girl.
Seven pantry ingredients, one dish, and a name worth saying out loud.
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| Spec | ||
|---|---|---|
| Input price | $15/M tokens | $1/M tokens |
| Output price | $120/M tokens | $6/M tokens |
| Context window | 400K tokens | 1.1M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Oct 2025 | Jul 2026 |
| At 10M a month | $150 | $10.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Both are developed by OpenAI but target different use cases. GPT-5 Pro has a 400K token context window vs GPT-5.6 Luna Pro's 1.1M. You can compare their actual outputs across 41 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5 Pro and GPT-5.6 Luna Pro each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 41 challenges so you can judge which fits your needs best.
GPT-5 Pro costs $15/M input tokens and GPT-5.6 Luna Pro costs $1/M input tokens. GPT-5.6 Luna Pro is $14.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of GPT-5 Pro and GPT-5.6 Luna Pro 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.