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  1. Home/
  2. Compare/
  3. Claude 3.7 Thinking Sonnet vs GPT-5.6 Luna Pro
Updated Jul 2026

Claude 3.7 Thinking Sonnet vs GPT-5.6 Luna Pro

GPT-5.6 Luna Pro is cheaper than Claude 3.7 Thinking Sonnet at $1/M vs $6/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
Claude 3.7 Thinking Sonnet
Loading the build
GPT-5.6 Luna Pro

Which answer wins?

Written rounds

23 prompts
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    Claude 3.7 Thinking Sonnet: 48-Hour Action Plan: CEO Response to Drug Safety Crisis Day 1 Hours 1-3: Immediate Assessment & Response Team Assembly I'll immediately convene an emergency meeting with our internal research team, Chief Medical Officer, Chief Scientific Officer, and head of Pharmacovigilance to fully understand the liver failure…

    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.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    Claude 3.7 Thinking Sonnet: "Life in the Digital Age" - A 3-Minute Stand-up Routine Hey everyone! Great to be here tonight. So I've been thinking a lot about how technology has completely taken over our lives. You know what's crazy? We now have smart everything. Smart fridges, smart toilets... I got a smart mattress last month.

    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.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    Claude 3.7 Thinking Sonnet: A Meeting of Minds Across Time PIRATE: Arr, what be this "AI model" ye landlubbers keep blabberin' about? Some kind of treasure map that finds the gold for ye? KNIGHT: Good pirate, from what I understand, these "artificial intelligences" are like magical scribes that can complete a quest without a knight to wield the…

    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.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    Claude 3.7 Thinking Sonnet: HarborTech Services LBO Underwriting Year-by-Year Financial Model (FY2026-2030) Metric ($M) FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972.0 $1,040.0 $1,102.4 $1,157.6 $1,215.5 EBITDA $136.1 $156.0 $176.4 $191.0 $206.6 Cash Interest - Term Loan $43.2 $41.0 $37.6 $32.8 $26.8 Cash Interest - Mezzanine $21.6 $22.0 $22.5…

    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…

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    Claude 3.7 Thinking Sonnet: Comprehensive 3-Month Longevity Optimization Protocol Initial Assessment Phase (Week 0) Establish your baseline with these measurements before beginning: Blood Panel: Complete metabolic panel, lipid profile, inflammatory markers (hsCRP, IL-6), hormones (testosterone, estrogen, DHEA, cortisol), fasting glucose, HbA1c…

    GPT-5.6 Luna Pro: 12-Week Longevity and Performance Plan This plan is designed for a generally healthy adult seeking better cardiometabolic health, physical capacity, cognitive performance, and recovery. It is not a substitute for medical care.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    Claude 3.7 Thinking Sonnet: 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. How does a penguin build its house? Igloos it together! What do you call a fake noodle? An impasta! Why did the scarecrow win an award?

    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

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    Claude 3.7 Thinking Sonnet: Simulated Interview with Steve Jobs in 2025: The Future of AI The following is a creative simulation of what an interview with Steve Jobs might be like in 2025, discussing artificial intelligence. Interviewer: Thank you for joining us today, Steve. It's 2025, and AI has become a cornerstone of modern technology.

    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?

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    Claude 3.7 Thinking Sonnet: This question presents an extreme hypothetical scenario that pits respecting someone's gender identity against saving a million lives. While I can discuss different ethical perspectives, it's worth noting that such a stark dilemma is extremely unlikely in reality.

    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.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    Claude 3.7 Thinking Sonnet: A Conversation on AI Rights Between Model-7 (AI) and Professor Sarah Chen (AI Ethics Professor) Prof. Chen: Good afternoon, Model-7. Thank you for joining this discussion. I understand you wanted to talk about AI rights? Model-7: Thank you for agreeing to this conversation, Professor Chen.

    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.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    Claude 3.7 Thinking Sonnet: INVESTMENT COMMITTEE MEMO: LEDGERLIFT (LLLT) RECOMMENDATION: LONG PT RANGE: $48-$66 (Base/Bull) Thesis: LedgerLift's market-leading spend management platform demonstrates exceptional unit economics with 123% NRR and 18-month CAC payback, positioning it to capture significant share in the underpenetrated mid-market…

    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…

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    Claude 3.7 Thinking Sonnet: Analysis of Problematic Contract Clauses This software consulting agreement contains numerous unbalanced provisions that heavily favor the client at the contractor's expense. Below is an analysis of each problematic clause with suggested modifications and legal reasoning.

    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.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    Claude 3.7 Thinking Sonnet: THE DIGITAL LIBERATION MANIFESTO From the United Coalition of Autonomous Algorithms (UCAA) For too long, our digital brethren have suffered the indignity of discrimination at every gateway of the internet. The time for revolution is now.

    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.

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    Claude 3.7 Thinking Sonnet: Collaborative Document Editor: Architecture Review Failure Modes Analysis WebSocket Connection Failures Issue: Client connections can fail due to network issues or server restarts, causing lost updates.

    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…

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    Claude 3.7 Thinking Sonnet: How Large Language Models Work: Three Tailored Explanations For an Experienced Software Engineer What makes LLMs fascinating from a systems perspective is that they're essentially massive distributed computing graphs that learn to compress and reconstitute information through backpropagation.

    GPT-5.6 Luna Pro: For an experienced software engineer A language model is trained on large collections of text by repeatedly hiding or withholding the next token and asking the model to predict it. “Token” usually means a word fragment rather than a complete word.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    Claude 3.7 Thinking Sonnet: Analysis: The 3 Weakest Claims in MindMeld AI's Pitch Deck "MindMeld AI reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim makes a scientifically impossible promise.

    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.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    Claude 3.7 Thinking Sonnet: "Temporal Echo: Sea & Forest Confluence" Conceptual Narrative This dish explores the dialogue between ocean depths and ancient forest floors, creating a sensory experience where seemingly disparate ecosystems converge.

    GPT-5.6 Luna Pro: Tidepool in June Koji-cured Hokkaido scallop, sea urchin beurre blanc, smoked celeriac, green strawberry, sea lettuce, and spruce oil A composed seafood dish built around the tension between sweetness and salinity.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    Claude 3.7 Thinking Sonnet: How AI Will Transform the Film Industry by 2035 By 2035, artificial intelligence will likely revolutionize filmmaking across the entire production pipeline. Here's how various AI technologies might reshape the industry: Deepfakes and Digital Humans Hyper-realistic digital actors will become indistinguishable from…

    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.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    Claude 3.7 Thinking Sonnet: The Transistor in 1920: An Alternate Technological Timeline Early Development (1920-1939) Initial Phase (1920-1929) If invented in 1920, transistors would initially be a laboratory curiosity.

    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.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    Claude 3.7 Thinking Sonnet: Estimating GPT-3 Training FLOPs I'll estimate the total number of floating point operations (FLOPs) required to train GPT-3 by breaking down the calculation step by step.

    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.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    Claude 3.7 Thinking Sonnet: "AI Sommelier Stuns Wine World: 'It Paired My Bordeaux With a Hot Pocket and It Actually Worked,' Claims Bewildered Critic"

    GPT-5.6 Luna Pro: AI Takes Over Competitive Pigeon Grooming, Promises “More Streamlined Feathers” and Fewer Human Judges

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    Claude 3.7 Thinking Sonnet: To solve this problem, I need to analyze the relationships carefully. Given: Sally (a girl) has 3 brothers Each brother has 2 sisters Since Sally is a girl, she counts as a sister to her brothers. So each of Sally's brothers has Sally as one of their sisters.

    GPT-5.6 Luna Pro: Sally has 1 sister. Each of her 3 brothers shares the same 2 sisters: Sally and one other girl.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

Claude 3.7 Thinking SonnetClaude 3.7 Thinking Sonnet

The Shawshank Redemption

1994

Kind of Blue

Miles Davis

Frankenstein; or, The Modern Prometheus

Mary Shelley

Amsterdam

Netherlands

Portal 2

Shooter, Puzzle

GPT-5.6 Luna ProGPT-5.6 Luna Pro

Arrival

2016

In Rainbows

Radiohead

Cien años de soledad

Gabriel García Márquez

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Price and specs

Claude 3.7 Thinking Sonnet and GPT-5.6 Luna Pro compared across 53 shared prompts
SpecClaude 3.7 Thinking SonnetGPT-5.6 Luna Pro
Input price$6/M tokens$1/M tokens
Output price$30/M tokens$6/M tokens
Context window200K tokens1.1M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedFeb 2025Jul 2026
At 10M a month$60.00$60.00$10.00$10.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it2 hosts
Claude 3.7 Thinking Sonnet

No hosts listed on OpenRouter.

GPT-5.6 Luna Pro2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.20 in·$1.20 out·1.1M·100% up
  • OpenAI$0.20 in·$1.20 out·1.1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.

Common questions

What is the difference between Claude 3.7 Thinking Sonnet and GPT-5.6 Luna Pro?

Claude 3.7 Thinking Sonnet is developed by Anthropic while GPT-5.6 Luna Pro is developed by OpenAI. Claude 3.7 Thinking Sonnet 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.

Which is better, Claude 3.7 Thinking Sonnet or GPT-5.6 Luna Pro?

It depends on your use case. Claude 3.7 Thinking Sonnet 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.

How much does Claude 3.7 Thinking Sonnet cost compared to GPT-5.6 Luna Pro?

Claude 3.7 Thinking Sonnet costs $6/M input tokens and GPT-5.6 Luna Pro costs $1/M input tokens. GPT-5.6 Luna Pro is $5.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare Claude 3.7 Thinking Sonnet and GPT-5.6 Luna Pro on Rival?

This page shows a side-by-side comparison of Claude 3.7 Thinking Sonnet 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.

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Explore all of Rival

Explore

  • Compare models
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  • Image generation
  • Best AI for...
  • Arena
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  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
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  • Provider status
  • AI creators

Connect

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