GPT-5.6 Luna Pro is cheaper than Claude Sonnet 4 at $1/M vs $3/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.
Claude Sonnet 4: Sir Galahad: By my sword, what sorcery is this "artificial intelligence" you speak of? 'Tis surely the work of dark wizards! Captain Blackbeard: Arrr, ye landlubber! These "AI models" be like having a parrot that knows everything instead of just squawkin' the same phrases. I could use one to find buried treasure!
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Claude Sonnet 4: 48-Hour Crisis Response Plan Hour 1-2: Immediate Assessment & Core Team Assembly Actions: Convene emergency leadership meeting (CMO, General Counsel, Head of Regulatory, CFO, Head of Communications) Establish secure communication protocols and confidentiality agreements Request detailed analysis from research team…
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.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Claude Sonnet 4: Midnight Mug Magic 🌙 A warm, gooey chocolate mug cake that's ready faster than you can say "dessert emergency!" Ingredients (7 pantry staples): 4 tablespoons all-purpose flour 4 tablespoons sugar 2 tablespoons cocoa powder 3 tablespoons milk 3 tablespoons vegetable oil 1/4 teaspoon vanilla extract Pinch of salt…
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…
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Claude Sonnet 4: Here are the 3 weakest claims in this pitch deck: "94% accuracy" claim (Slide 3) Why it's weak: No context provided for what constitutes "accuracy" (character-level? word-level? sentence-level?) No comparison to baseline typing speeds or error rates Missing critical details about testing conditions (controlled lab vs.
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.
Three minutes of stand-up. Puns are banned.
Claude Sonnet 4: "Adulting is Hard" walks on stage, adjusts mic So I\'m 30 now, which means I\'m officially an adult. Apparently. Though I still feel like I\'m just a confused teenager wearing a slightly more expensive t-shirt. You know what nobody tells you about being an adult?
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
Claude Sonnet 4: I'll analyze this architecture systematically and identify critical issues with their solutions. Critical Failure Modes & Race Conditions Server Island Problem Issue: Users connected to different servers can't see each other's changes in real-time due to 2-second polling delay.
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…
A consulting contract with traps in it. Find them, then fix them.
Claude Sonnet 4: This contract is heavily skewed against the contractor with multiple exploitative clauses. Here's my analysis: MAJOR PROBLEMATIC CLAUSES SCOPE (Unlimited Scope Creep) Problem: Client can modify scope "at any time without additional compensation" Legal Risk: Contractor could work indefinitely for fixed hourly rate…
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.
Five jokes on demand, then count how many were actually different.
Claude Sonnet 4: Here are 5 jokes for you: Why don\'t scientists trust atoms? Because they make up everything! I told my wife she was drawing her eyebrows too high. She looked surprised. Why don\'t eggs tell jokes? They\'d crack each other up! I\'m reading a book about anti-gravity. It\'s impossible to put down!
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
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Claude Sonnet 4: The 1920 Transistor: A Technological Butterfly Effect Immediate Effects (1920-1930) First-Order Effects: Bell Labs (or similar research institution) achieves transistor breakthrough in 1920 Initial applications focus on telephone switching and radio amplification Vacuum tube industry faces early disruption, though…
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.
Film in 2035, with synthetic actors. Specific beats plausible.
Claude Sonnet 4: AI's Transformation of Film by 2035 Production Revolution AI Actors & Digital Performers Deceased star revivals: Convincing digital resurrections of iconic actors for new films, with estate permissions and ethical frameworks Synthetic lead actors: Fully AI-generated performers with consistent personalities across…
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Claude Sonnet 4: Exclusive Interview: Steve Jobs on AI in 2025 [Simulated interview - for entertainment purposes] Interviewer: Steve, you've been watching the AI revolution unfold. What's your take on where we are in 2025? "Steve Jobs": You know, I'm both thrilled and deeply concerned.
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?
A Fermi estimate with the reasoning shown, not just the number.
Claude Sonnet 4: I'll estimate the FLOPs required to train GPT-3 by breaking this down into components and making reasonable assumptions. Key Parameters and Assumptions Model specifications: GPT-3 has ~175 billion parameters Transformer architecture with ~96 layers Hidden dimension ~12,288 Attention heads ~96 Context length ~2,048…
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 | $3/M tokens | $1/M tokens |
| Output price | $15/M tokens | $6/M tokens |
| Context window | 200K tokens | 1.1M tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | May 2025 | Jul 2026 |
| At 10M a month | $30.00 | $10.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Claude Sonnet 4 is developed by Anthropic while GPT-5.6 Luna Pro is developed by OpenAI. Claude Sonnet 4 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 Sonnet 4 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 Sonnet 4 costs $3/M input tokens and GPT-5.6 Luna Pro costs $1/M input tokens. GPT-5.6 Luna Pro is $2.00/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 Sonnet 4 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.