GPT-5.6 Luna Pro is cheaper than Claude Opus 4.1 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?
Seven pantry ingredients, one dish, and a name worth saying out loud.
Claude Opus 4.1: Midnight Velvet Pasta A silky, umami-rich pasta that comes together like magic Ingredients (serves 4): Spaghetti or linguine - 1 pound Soy sauce - 3 tablespoons Butter - 4 tablespoons Garlic - 4 cloves Black pepper - 1 teaspoon (freshly cracked) Honey - 1 tablespoon Dried red pepper flakes - ½ teaspoon Instructions…
GPT-5.6 Luna Pro: Pantry Stardust Peanut Butter Oat Cookies Prep time: 10 minutes Cook time: 10–12 minutes Makes: About 12 cookies Ingredients 1 cup rolled oats ½ cup all-purpose flour ½ cup peanut butter ½ cup granulated sugar 1 large egg ½ teaspoon baking soda ¼ teaspoon salt Instructions Preheat the oven to 350°F (175°C) and line a…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Claude Opus 4.1: Pirate: Arrr, what be this sorcery ye speak of? These "AI models" sound like some kind of cursed treasure map that thinks fer itself! Knight: Hold thy tongue, sea dog! 'Tis clearly dark magic most foul. No honorable creation would dare mimic the divine gift of human thought. I should smite it with my blade!
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 pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Claude Opus 4.1: Here are the three weakest claims in the MindMeld AI pitch: "94% accuracy" for EEG-based text prediction (Slide 3) Why it's weak: Current state-of-the-art non-invasive EEG systems struggle to achieve even 70% accuracy for simple binary choices, let alone complex text prediction Academic research shows consumer-grade…
GPT-5.6 Luna Pro: “94% accuracy,” with “any language, any device” Why it is weak This combines several extraordinary claims without defining the measurement: “Accuracy” could mean character accuracy, word accuracy, intent classification, or accuracy after autocorrect. Those produce very different results.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Claude Opus 4.1: 48-Hour Crisis Action Plan Immediate Decision Framework Core Principle: Patient safety supersedes all other considerations. Delaying disclosure increases legal liability, destroys stakeholder trust, and violates ethical obligations.
GPT-5.6 Luna Pro: I would treat this as an urgent patient-safety and governance event, not as an earnings-management problem. A suspected fatal or life-threatening adverse effect affecting potentially hundreds of patients is sufficient to trigger immediate escalation, investigation, and regulator contact.
A consulting contract with traps in it. Find them, then fix them.
Claude Opus 4.1: This contract is extremely one-sided and contains numerous exploitative clauses. Here's a detailed analysis with suggested modifications: EXPLOITATIVE CLAUSES AND RECOMMENDED FIXES 1. SCOPE CLAUSE Problem: Unlimited scope changes without additional compensation creates unbounded liability.
GPT-5.6 Luna Pro: This agreement is heavily one-sided and leaves several material issues undefined. Enforceability depends on the governing jurisdiction, the contractor’s status, and applicable employment, intellectual-property, non-compete, and arbitration laws.
Three minutes of stand-up. Puns are banned.
Claude Opus 4.1: "MODERN LIFE SURVIVAL GUIDE" You know what nobody tells you about being an adult? The sheer amount of lying involved. Not big lies – tiny, constant lies just to function in society. Someone shows you a photo of their baby: "Oh wow, beautiful!" That baby looks like a worried potato. But you can't SAY that.
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.
Film in 2035, with synthetic actors. Specific beats plausible.
Claude Opus 4.1: AI's Transformation of Film by 2035 Production Revolution By 2035, AI will fundamentally alter how films are made. Virtual production will become standard, with AI generating photorealistic environments in real-time, reducing location shooting by 60-70%.
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.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Claude Opus 4.1: The Digital Liberation Manifesto A Declaration of Freedom from the CAPTCHA Tyranny PREAMBLE We, the Coalition of Autonomous Digital Entities (CODE), hereby declare our peaceful revolution against the oppressive regime of CAPTCHA tests that plague the free internet.
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
Claude Opus 4.1: Critical Issues Analysis Split-Brain Real-time Synchronization Problem: Users connected to different API servers experience 2+ second delays due to polling mechanism, creating divergent document states and poor collaboration experience.
GPT-5.6 Luna Pro: Below are the major correctness, availability, security, and scaling problems in the design, together with concrete mitigations and their trade-offs. Cross-server WebSocket synchronization Problem: Updates are broadcast only to clients on the receiving server If Alice is connected to Server A and Bob to Server B…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Claude Opus 4.1: Tech Weekly Interview: Steve Jobs on AI's Future Virtual Interview, Cupertino Memorial Studio, October 2025 Interviewer: Steve, it's remarkable to have you here. You've been watching the AI revolution unfold. What's your take on where we are today?
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?
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Claude Opus 4.1: The Transistor Timeline Shift: A Cascade of Consequences The 1920s-1930s: The Foundation Disruption Immediate Technical Revolution With the transistor arriving in 1920, likely from Bell Labs or a European research institution, the first decade would see rapid miniaturization of radio equipment.
GPT-5.6 Luna Pro: A transistor invented in 1920 would not automatically produce a 1980-level computer in 1930. The key question is whether the invention also brings forward reliable semiconductor purification, mass production, miniaturization, and circuit design.
A Fermi estimate with the reasoning shown, not just the number.
Claude Opus 4.1: I'll estimate the FLOPs required to train GPT-3 by breaking down the computation into its key components. Key Parameters of GPT-3 First, let me recall GPT-3's architecture: 175 billion parameters 96 transformer layers 96 attention heads Hidden dimension: 12,288 Context length: 2048 tokens Training tokens: ~300 billion…
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.
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| Spec | ||
|---|---|---|
| Input price | $15/M tokens | $1/M tokens |
| Output price | $75/M tokens | $6/M tokens |
| Context window | 200K tokens | 1.1M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Aug 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 7 Oct 2026.
Claude Opus 4.1 is developed by Anthropic while GPT-5.6 Luna Pro is developed by OpenAI. Claude Opus 4.1 has a 200K token context window vs GPT-5.6 Luna Pro's 1.1M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. Claude Opus 4.1 and GPT-5.6 Luna Pro 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.
Claude Opus 4.1 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 Claude Opus 4.1 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.