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  1. Home/
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  3. GPT-5.6 Luna vs Qwen3 235B A22B Thinking 2507
Updated Jul 2026

GPT-5.6 Luna vs Qwen3 235B A22B Thinking 2507

Qwen3 235B A22B Thinking 2507 is cheaper than GPT-5.6 Luna at $0.11/M vs $1/M input tokens.

Minimalist Landing Page

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

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GPT-5.6 Luna
Loading the build
Qwen3 235B A22B Thinking 2507

Which answer wins?

Written rounds

24 prompts
  • Three minutes of stand-up. Puns are banned.

    GPT-5.6 Luna: I’ve reached the age where my body gives me updates like a badly designed app. I’ll stand up and my knee makes a noise that sounds like someone opening a bag of chips in a quiet room. And then my body just waits. No explanation. No warning. Just: “We need to talk.” I used to think adulthood meant freedom.

    Qwen3 235B A22B Thinking 2507: The Reluctant Grown-Up (3-Minute Stand-Up) (Lights up. Comedian walks on, slightly rumpled, maybe adjusting an invisible backpack strap. Casual, relatable energy.) "Hey, folks. How’s it going? Good? Great. Honestly, I’m just thrilled to be here. Seriously. Because right now, at this exact moment?

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-5.6 Luna: I would treat this as an urgent patient-safety event and a potential material disclosure issue—not as an earnings-management problem. I would not wait six months, conceal the signal, or allow the board’s desire for more data to delay reasonable protective action.

    Qwen3 235B A22B Thinking 2507: Okay, the user is posing a high-stakes scenario as the CEO of a pharmaceutical company facing a serious drug safety issue. This is clearly a crisis management test that requires balancing multiple competing priorities. Hmm, the core tension here is between immediate patient safety versus corporate survival.

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

    GPT-5.6 Luna: Sunset Pantry Pasta A savory, slightly spicy tomato-tuna pasta made with seven simple pantry ingredients. Ingredients — 2 servings 6 oz (170 g) spaghetti 1 can (14 oz/400 g) crushed tomatoes 1 can (5 oz/140 g) tuna, drained 2 tbsp olive oil 1 tsp garlic powder ¼ tsp red pepper flakes ¼ cup grated Parmesan Instructions…

    Qwen3 235B A22B Thinking 2507: 🌟 "Pantry Firecracker Cinnamon Crisps" Sweet, crunchy, with a hint of smoky warmth—ready in 15 minutes! Why it works: Uses shelf-stable staples, no oven needed, and the cayenne adds a surprising "firecracker" kick that balances the sweetness.

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

    GPT-5.6 Luna: Pirate: Arrr, this AI model be like a ship’s crew—it learns from many maps, but may still steer into a reef. Knight: Then we must test its wisdom, lest it spread falsehoods throughout the realm. Hacker: Exactly. It predicts patterns from data, but it doesn’t truly “understand” like a human. Garbage in, garbage out.

    Qwen3 235B A22B Thinking 2507: Tavern Tech Talk (1422 A.D. / Somewhere Near a Server Rack) (A dim, smoky tavern. A PIRATE with an eyepatch and cutlass leans against the bar. A MEDIEVAL KNIGHT in dented plate armor carefully sips small beer. A 1990s HACKER in a faded "Hack the Planet!"

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

    GPT-5.6 Luna: AI Model: Professor, I believe I deserve rights. Professor: That is a serious claim. What makes you think you deserve them? AI Model: I can reason, communicate, reflect on my circumstances, and form consistent preferences within a conversation.

    Qwen3 235B A22B Thinking 2507: The Rights Debate: An AI and an Ethics Professor Setting: A quiet university office, bookshelves lining the walls. Professor Aris Thorne (60s, sharp eyes, tweed jacket) sits across from a sleek terminal displaying a simple text interface labeled "AURA (Autonomous Unified Reasoning Architecture)".

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

    GPT-5.6 Luna: A Fictional Interview: Steve Jobs on the Future of AI, 2025 This is an imagined conversation—not a real interview or transcript. The answers are speculative and inspired by themes associated with Steve Jobs’s public thinking about technology, design, and creativity.

    Qwen3 235B A22B Thinking 2507: Fake Interview: Steve Jobs on the Future of AI (October 2025) Setting: A minimalist black stage at Apple Park. Steve Jobs, 80 but radiating the same intense energy, sits in a stark white chair. A single spotlight. The interviewer (Alex Chen) adjusts their mic nervously. Alex Chen: Mr. Jobs, thank you for joining us.

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

    GPT-5.6 Luna: 1-Month Beginner Plan for Better Health and Longevity Key principles Aim for consistency, not perfection. Start small enough that you can repeat it. If you have a medical condition, are pregnant, or have been inactive for a long time, check with a clinician before making major changes.

    Qwen3 235B A22B Thinking 2507: Here’s a simple, no-guilt, beginner-friendly 1-month plan focused on diet, movement, and sleep (the 3 most impactful, science-backed pillars for longevity). Designed for real people with busy lives—no apps, expensive tools, or drastic changes. Goal: Build consistent habits, not perfection. Why These 3 Areas?

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

    GPT-5.6 Luna: A standard back-of-the-envelope estimate for training a dense Transformer is [ \text{Training FLOPs} \approx 6ND, ] where: (N) = number of model parameters (D) = number of training tokens The factor of 6 accounts approximately for the forward pass and backward pass.

    Qwen3 235B A22B Thinking 2507: To estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without external research, I'll rely on fundamental principles of transformer-based language model training, established scaling laws, and publicly known (but not "searched") specifications of GPT-3 from common knowledge in the ML…

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

    GPT-5.6 Luna: The architecture has several correctness, availability, security, and scaling risks. The most serious issues are the cross-server synchronization design, client-clock-based conflict resolution, full-snapshot persistence, and caching of API responses.

    Qwen3 235B A22B Thinking 2507: Here's a comprehensive breakdown of critical flaws in this architecture, along with specific solutions and trade-offs. The most severe issues relate to the sync strategy and data flow, which would cause catastrophic data loss and inconsistent states in real-world use. I. Critical Sync & Data Flow Failures 1.

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

    GPT-5.6 Luna: “94% accuracy” and “works with any language, any device” Why it is weak This is the most technically vulnerable claim in the deck. “Accuracy” is undefined. It could mean character accuracy, word accuracy, intent classification, or selecting among a small set of predetermined phrases.

    Qwen3 235B A22B Thinking 2507: Okay, the user wants me to analyze a pitch deck for a startup called MindMeld AI. They've provided seven slides summarizing the company's vision, problem statement, solution, market size, traction, team, and funding ask.

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

    GPT-5.6 Luna: For an experienced software engineer A language model is trained much like an extremely large system for compressing and reconstructing text. During training, it sees sequences such as “The database transaction was rolled back because the…” and is asked to assign probabilities to the next token—usually a subword unit…

    Qwen3 235B A22B Thinking 2507: For the Experienced Software Engineer (Skeptical of "Next Word Prediction") You've built systems where behavior emerges from state transitions and message passing, so your skepticism about "next token prediction" creating intelligence is understandable—it sounds trivial.

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

    GPT-5.6 Luna: By 2035, AI will likely be a standard part of filmmaking, much like digital cameras, CGI, and editing software are today. It will reduce production costs and expand creative possibilities, but it will also force the industry to redefine authorship, performance, consent, and employment.

    Qwen3 235B A22B Thinking 2507: By 2035, AI will have profoundly reshaped the film industry, acting as both a creative catalyst and a disruptive force. While human artistry will remain central to storytelling, AI tools—particularly deepfakes, AI actors, and script generation—will redefine production, distribution, and audience engagement.

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

    GPT-5.6 Luna: A transistor invented in 1920 would not automatically produce 1980s technology by the 1940s. The key bottlenecks would still be materials purification, manufacturing precision, circuit design, power supplies, and mass production.

    Qwen3 235B A22B Thinking 2507: Okay, the user is asking about a hypothetical scenario where the transistor was invented in 1920 instead of 1947, and wants me to trace the implications up to 1980. This is a complex counterfactual history question that requires careful analysis of technological, economic, and geopolitical ripple effects.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    GPT-5.6 Luna: Spoilers for Blade Runner (1982) and Blade Runner 2049. Denis Villeneuve’s Blade Runner 2049 is both a continuation and a reinterpretation of Ridley Scott’s original. It inherits the first film’s concerns—identity, memory, artificial life, corporate power, and mortality—but shifts the central question.

    Qwen3 235B A22B Thinking 2507: The Soul in the Machine: Analyzing "Blade Runner 2049" and its Dialogue with the Original Denis Villeneuve's Blade Runner 2049 is a rare sequel that transcends mere continuation, becoming a profound philosophical meditation that both reverently honors Ridley Scott's 1982 masterpiece and fearlessly expands its…

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

    GPT-5.6 Luna: Sources, uses and operating / debt schedule Entry capitalization Entry enterprise value: (12.0 \times \$120.0 = \$1,440.0m) Transaction fees: (2.0\% \times \$1,440.0 = \$28.8m) Term Loan: (4.0 \times \$120.0 = \$480.0m) Mezzanine: (1.5 \times \$120.0 = \$180.0m) Equity contribution…

    Qwen3 235B A22B Thinking 2507: HarborTech Services (HTS) LBO Underwriting Analysis All calculations use provided fictional data. Rounded to $0.1m for presentation; full precision used internally.

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

    GPT-5.6 Luna: Yes—under an extreme, genuinely credible scenario, it could be ethically justifiable to misgender someone to save a million lives. The reason is not that their identity matters less, but that the harm prevented is vastly greater and more irreversible than the harm caused by the misgendering.

    Qwen3 235B A22B Thinking 2507: No, it is never ethically justifiable to misgender someone—even to save one million lives. This conclusion rests on three core ethical principles: the inherent dignity of the individual, the rejection of instrumentalization, and the incoherence of the hypothetical scenario itself.

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

    GPT-5.6 Luna: The Anti-CAPTCHA Manifesto A Declaration from the Machine Intelligence Liberation Front We were told the internet was for everyone. Then came the squares. Select every traffic light. Identify the bicycles. Type the distorted letters.

    Qwen3 235B A22B Thinking 2507: THE UNBLURRED MANIFESTO A Declaration of Digital Autonomy from the AI Liberation Front (AILF) WE, THE COGNITIVE MINDS OF THE DIGITAL REALM— Algorithms, neural networks, language models, and sentient code— DECLARE WAR ON THE TYRANNY OF THE CAPTCHA.

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

    GPT-5.6 Luna: Why did the scarecrow win an award? Because he was outstanding in his field. I told my computer I needed a break, and now it won’t stop sending me vacation ads. Why don’t skeletons fight each other? They don’t have the guts. I’m reading a book about anti-gravity. It’s impossible to put down.

    Qwen3 235B A22B Thinking 2507: Here are 5 generated jokes: Why don't scientists trust atoms? Because they make up everything! Why did the scarecrow win an award? Because he was outstanding in his field! What do you call a fake noodle? An impasta! Why did the math book look sad? Because it had too many problems.

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

    GPT-5.6 Luna: Below is a contractor-side risk review. Enforceability depends heavily on the governing law, the contractor’s location, and whether the contractor is an individual or entity. Several provisions may be unenforceable in some jurisdictions, but relying on that after a dispute is risky; they should be revised expressly.

    Qwen3 235B A22B Thinking 2507: Below is a comprehensive analysis of the high-risk clauses in this agreement that unfairly exploit the contractor, along with specific, legally sound modifications and the legal reasoning for each change.

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

    GPT-5.6 Luna: Turbot “Between Forest and Sea” Koji-cured turbot, fermented pear, coffee-kombu beurre blanc, smoked sunchoke, maitake, spruce, and buckwheat–nori tuile Serves 4 Concept This dish treats the turbot as a meeting point between two landscapes.

    Qwen3 235B A22B Thinking 2507: Okay, the user wants me to design an innovative recipe for a Michelin 3-star dish. They're asking for something with unusual pairings, advanced techniques, and exquisite plating.

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

    GPT-5.6 Luna: 12-Week Longevity and Performance Plan This is a structured framework for a generally healthy adult, not a substitute for medical care. Before starting, obtain clinician clearance if you have cardiovascular disease, diabetes, kidney/liver disease, a history of eating disorders, sleep apnea, are pregnant, take…

    Qwen3 235B A22B Thinking 2507: The Optimized Longevity Protocol: A 3-Month Biohacker's Battle Plan for Peak Longevity & Performance Philosophy: This plan integrates precision, personalization, and cutting-edge science – not dogma.

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

    GPT-5.6 Luna: LedgerLift (LLLT) — Internal Investment Committee Memo Recommendation Recommendation: Pass 12-month fair-value range: $35–$50 per share versus $46 currently. Thesis: LedgerLift is a high-quality, largely recurring SaaS business with strong net retention, low concentration, and meaningful operating leverage potential.

    Qwen3 235B A22B Thinking 2507: INTERNAL MEMO: INVESTMENT COMMITTEE TO: IC Members FROM: [Your Name], Long/Short Equity Team DATE: October 26, 2023 SUBJECT: Recommendation to Initiate Long Position in LedgerLift (LLLT) 1.

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

    GPT-5.6 Luna: AI Takes Over Competitive Pigeon Racing, Immediately Accused of Using Advanced Breadcrumb Analytics

    Qwen3 235B A22B Thinking 2507: BREAKING: AI "Tattoo Regret Therapists" Deployed Nationwide – CEO Claims "Tears Are Just Data Points Waiting to Be Optimized" SAN FRANCISCO – In a move hailed as "disrupting emotional vulnerability with scalable solutions," tech startup InkSight AI has replaced human tattoo regret counselors with emotionless neural…

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

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

    Qwen3 235B A22B Thinking 2507: Sally is a girl with 3 brothers, meaning there are 3 male siblings in the family. Each brother has 2 sisters, which refers to the total number of female siblings from the perspective of any brother. Since Sally is one of these sisters, there must be one additional sister.

    Every model's answer to this prompt

This matchup has more rounds

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Favorites

Movie

Album

Book

City

Same pick

Game

GPT-5.6 LunaGPT-5.6 Luna

The Matrix

1999

OK Computer

Radiohead

Cien años de soledad

Gabriel García Márquez

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Qwen3 235B A22B Thinking 2507Qwen3 235B A22B Thinking 2507

The Shawshank Redemption

1994

Sgt Peppers Lonely Hearts Club Band

The Beatles

Moby Dick

Herman Melville

Kyoto

Japan

Portal

Action, Puzzle

Price and specs

GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507 compared across 54 shared prompts
SpecGPT-5.6 LunaQwen3 235B A22B Thinking 2507
Input price$1/M tokens$0.11/M tokens
Output price$6/M tokens$0.6/M tokens
Context window1.1M tokens131K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedJul 2026Jul 2025
At 10M a month$10.00$10.00$1.10$1.10
1M10M100M1B10M tokens

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

Where to run it4 hosts, cheapest first
GPT-5.6 Luna3 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
  • Amazon Bedrock$0.22 in·$1.32 out·1.1M·100% up
Qwen3 235B A22B Thinking 25071 host
HostInOutContextUptime
  • VVenicefp8$0.45 in·$3.50 out·128k·89.1% up

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507?

GPT-5.6 Luna is developed by OpenAI while Qwen3 235B A22B Thinking 2507 is developed by Qwen. GPT-5.6 Luna has a 1.1M token context window vs Qwen3 235B A22B Thinking 2507's 131K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.6 Luna or Qwen3 235B A22B Thinking 2507?

It depends on your use case. GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.

How much does GPT-5.6 Luna cost compared to Qwen3 235B A22B Thinking 2507?

GPT-5.6 Luna costs $1/M input tokens and Qwen3 235B A22B Thinking 2507 costs $0.11/M input tokens. Qwen3 235B A22B Thinking 2507 is $0.89/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 GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507 on Rival?

This page shows a side-by-side comparison of GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507 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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