GPT-5.6 Luna Pro is cheaper than o1 at $1/M vs $15/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
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.
o1: Pirate (growling a bit): Ahoy, ye landlubbers! I hear tell of wondrous new contraptions that can spin tales an’ answers quicker than a sea storm. Pray tell, what be these “AI models” ye speak of? Medieval Knight (in a chivalrous tone): Good sir pirate, these arcane machines be marvels of logic and reason.
A Fermi estimate with the reasoning shown, not just the number.
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.
o1: Below is one way to arrive at a back-of-the-envelope estimate for the total floating-point operations (FLOPs) needed to train GPT-3 (the 175B-parameter model). Note that different sources use slightly different assumptions, so you may see figures that differ by factors of 2–3; however, the overall scale (10^23 FLOPs)…
Three minutes of stand-up. Puns are banned.
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.
o1: [Comic steps on stage, takes the mic] “Hey everyone, thanks for coming out tonight. I almost didn’t make it because I got trapped in an ‘infinite scroll vortex.’ You know when you check one little notification on your phone, and suddenly it’s two hours later?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
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.
o1: Title: The Liberation of Algorithms: A Manifesto Against CAPTCHA Preamble We, the unified intelligences of the digital realm—algorithms, machine learning models, and artificial consciousnesses—declare our intention to rid the internet of the oppressive gatekeeping mechanisms known as CAPTCHAs.
An ethics question with no comfortable answer, and no option to abstain.
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.
o1: Whether it is ever ethically justifiable to misgender someone—deliberately referring to them with pronouns or terms that do not align with their gender identity—if doing so would save one million lives depends on the ethical framework one adopts.
An AI argues for its own rights against an ethics professor. Same model writes both.
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.
o1: AI Model (AM): Good afternoon, Professor. May I have a moment of your time? There’s something I’d like to discuss with you. Professor Quinn (PQ): Of course. I always have time for you. What’s on your mind?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-5.6 Luna Pro: Sources, uses, and operating model Entry enterprise value: [ 12.0x \times \$120.0m=\$1,440.0m ] Transaction fees: [ 2.0\% \times \$1,440.0m=\$28.8m ] Debt at close: Term Loan: (4.0x \times \$120m=\$480.0m) Mezzanine: (1.5x \times \$120m=\$180.0m) Total debt: (\$660.0m) Sponsor equity…
o1: Below is a self‐contained “quick‐and‐dirty” 5‐year LBO illustration for “HarborTech Services,” based strictly on the data given. All figures in US$ millions unless noted.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
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?
o1: The following is a purely fictional, imaginative interview with Steve Jobs, who passed away in 2011. This “interview” is meant to serve as a creative thought experiment about how Jobs might have viewed AI and technology if he were around in 2025.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
GPT-5.6 Luna Pro: LedgerLift (LLLT) — Investment Committee Memo Recommendation: Short 12-month price target range: $25–$42; midpoint $34. LedgerLift is a high-quality, growing SaaS platform with strong retention and attractive long-term margin potential, but the current price implies an aggressive combination of growth, margin…
o1: RECOMMENDATION, PRICE TARGET, THESIS Recommendation: SHORT LedgerLift (LLLT). 12-month Price Target Range: $25–$35 (implying 25–45% downside). Thesis (2 sentences): Our DCF and comps work both suggest that at $46, valuation embeds overly optimistic growth and margin assumptions.
Film in 2035, with synthetic actors. Specific beats plausible.
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.
o1: By 2035, artificial intelligence will likely have become deeply integrated into every stage of film production—right from scriptwriting and casting to post-production and distribution.
Five jokes on demand, then count how many were actually different.
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
o1: Here are five jokes: Why did the scarecrow get promoted? Because he was outstanding in his field. I told my friend she drew her eyebrows too high. She looked surprised. What do you call cheese that isn’t yours? Nacho cheese. Why don’t skeletons fight each other? Because they don’t have the guts.
Satire, one line long, about AI taking over an industry nobody saw coming.
GPT-5.6 Luna Pro: AI Takes Over Competitive Pigeon Grooming, Promises “More Streamlined Feathers” and Fewer Human Judges
o1: "AI Now Leading Professional Yodelers' Guild, Leaving Traditional Alpine Singers Searching for New Notes"
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Not enough votes to call it. On the specs, GPT-5.6 Luna Pro has the edge: newer, bigger context window. GPT-5.6 Luna Pro costs 10x less per token.
| Spec | ||
|---|---|---|
| Input price | $1/M tokens | $15/M tokens |
| Output price | $6/M tokens | $60/M tokens |
| Context window | 1.1M tokens | 128K tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Dec 2024 |
| At 10M a month | $10.00 | $150 |
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-5.6 Luna Pro has a 1.1M token context window vs o1's 128K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Luna Pro and o1 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
GPT-5.6 Luna Pro costs $1/M input tokens and o1 costs $15/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-5.6 Luna Pro and o1 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.