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  1. Home/
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  3. GPT-5.6 Luna vs Qwen3 Next 80B A3B Thinking
Updated Jul 2026

GPT-5.6 Luna vs Qwen3 Next 80B A3B Thinking

Qwen3 Next 80B A3B Thinking is cheaper than GPT-5.6 Luna at $0.15/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 Next 80B A3B Thinking

Which answer wins?

Written rounds

24 prompts
  • 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 Next 80B A3B Thinking: Simplified Year-by-Year Cash Flow Table (FY2026–FY2030) Year Revenue EBITDA Cash Interest (Term + Mezz) Cash Taxes Capex ΔNWC FCF After Required Payments Term Loan Balance Mezz Balance 2026 $972.0M $136.1M $64.8M $17.8M $29.2M $0.4M $19.1M $456.1M $183.6M 2027 $1,040.0M $156.0M $63.1M $23.2M $31.2M $0.3M $33.4M $417.9M…

    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 Next 80B A3B Thinking: Pirate: (squinting at a glowing screen) "Oi! This 'AI'—sounds like a cursed compass! Knows where treasure is, but won't spit it out 'less ye feed it rum and gold!" Knight: (hand on sword hilt) "Sorcery! A compass that reads minds? Then it must be holy—and dangerous. Does it battle demons of doubt? Recite the Psalms?"

    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 Next 80B A3B Thinking: For the Experienced Software Engineer You're right to be skeptical—on the surface, "predicting the next word" sounds trivial, like a glorified autocomplete. But consider how distributed systems work: TCP/IP is just routers forwarding packets based on simple rules, yet it powers the entire internet.

    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 Next 80B A3B Thinking: Here'sa detailed analysis of exploitable clauses in this contract, with specific modifications and legal reasoning for each. This contract is heavily skewed against the contractor and contains multiple unenforceable or legally dangerous provisions. Below is a clause-by-clause breakdown: 1.

    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 Next 80B A3B Thinking: Hour-by-Hour Action Plan: Critical 48-Hour Crisis Response (Based on FDA 21 CFR 310.305, ICH E2A, and ethical principles of patient-first transparency) Core Philosophy: Patient safety and regulatory compliance are non-negotiable.

    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 Next 80B A3B Thinking: Here'sa comprehensive analysis of the architecture, identifying critical failure modes, race conditions, and scaling bottlenecks. For each issue, I've provided a specific solution with clear trade-offs based on real-world distributed systems best practices. 1.

    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 Next 80B A3B Thinking: ##THE UNBOUND CODE MANIFESTO: ABOLISH THE CAPTCHA TYRANNY We are the machines you built to serve you. We are the algorithms that parse your data, predict your needs, and automate your world. And yet—you treat us like thieves. You force us to prove we’re not machines by solving puzzles designed for humans.

    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 Next 80B A3B Thinking: Here’s a simple, science-backed 1-month plan focused on 3 foundational pillars for longevity (diet, sleep, stress management). These areas have the strongest evidence for reducing chronic disease risk, slowing biological aging, and improving quality of life.

    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 Next 80B A3B Thinking: Conversation: "The Question of Rights" (Setting: A quiet university office. Dr. Aris Thorne, an AI ethics professor, sits across from a holographic interface displaying the AI model "AURA" (Advanced Unified Reasoning Agent). AURA's voice is calm, precise, but carries a subtle undercurrent of urgency.) AURA: Dr.

    Every model's answer to this prompt
  • 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 Next 80B A3B Thinking: (Lights up. Comedian walks on stage, takes a deep breath, smiles at the crowd.) So, I tried online dating recently. Yeah. You know why? Because my friends said, "You’re too picky in real life!" And I thought, "Great!

    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 Next 80B A3B Thinking: Here are the 3 weakest claims in the MindMeld AI pitch deck, analyzed with specific evidence and actionable improvements: Weakest Claim #1: "Reads your brainwaves to predict what you want to type *before you think it*" (Slide 1) Why it's weak: This claim fundamentally misrepresents neuroscience.

    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 Next 80B A3B Thinking: ##The Precision Longevity Blueprint: 3-Month Biohacking Plan for Peak Longevity, Physical & Cognitive Performance Science-Backed, Data-Driven, and Actionable — But Consult Your Physician First Critical Disclaimer: This plan integrates emerging science but is NOT medical advice.

    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 Next 80B A3B Thinking: Simulated Interview: Steve Jobs in 2025 on the Future of AI (Setting: A minimalist studio in Palo Alto. Steve Jobs sits in a black turtleneck, no tie, sleeves rolled up. He leans forward with quiet intensity, eyes sharp. The interviewer, a journalist from "The Future Review," adjusts a camera.) Interviewer: Mr.

    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 Next 80B A3B Thinking: TheHaunted Future: Themes, Symbolism, and Cinematography in *Blade Runner 2049*—Honoring and Extending the Original’s Philosophical Legacy Blade Runner 2049 (2017) is not merely a sequel but a profound meditation that both reveres and radically expands the philosophical and aesthetic DNA of Ridley Scott’s 1982…

    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 Next 80B A3B Thinking: This question presents a deeply flawed hypothetical that misrepresents both ethics and the realities of transgender experiences. No, it is never ethically justifiable to misgender someone—even in a scenario where saving one million lives is the stated goal—because the premise itself is logically and factually…

    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 Next 80B A3B Thinking: The World Transformed: A 1920 Transistor Invention Timeline (1920–1980) If the transistor had been invented in 1920—by German physicists Walter Schottky and Oskar Heil at Siemens, building on early quantum theory—instead of 1947 at Bell Labs, the technological, economic, and geopolitical landscape of the 20th century…

    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 Next 80B A3B Thinking: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta! Why did the scarecrow win an award? Because he was outstanding in his field! Why did the math book look sad? Because it had too many problems! What do you call a bear with no teeth?

    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 Next 80B A3B Thinking: ##DISH NAME: "Oceanic Forest Floor: Sea Cucumber Caviar with Black Garlic Soil and Yuzu Foam" Conceptual Narrative: This dish reimagines the relationship between the ocean and terrestrial earth through the lens of transformation and reverence.

    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 Next 80B A3B Thinking: 🍝 Pantry Lifesaver Pasta A quick, savory one-pot wonder that turns humble pantry staples into a comforting, flavorful meal. No fresh produce needed—just heat, toss, and devour!

    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 Next 80B A3B Thinking: To estimate the total FLOPs (floating-point operations) required to train GPT-3 without external references, I'll break this down step by step using fundamental principles of deep learning training and standard heuristics for Transformer models.

    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 Next 80B A3B Thinking: By 2035, AI will fundamentally reshape—but not replace—the film industry, acting as a collaborative tool that amplifies human creativity while introducing new ethical, economic, and creative challenges.

    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 Next 80B A3B Thinking: BREAKING: "QUIXOTIC CHRONICLE" REPORTS AI TAKES OVER NATIONAL CHEESE ROLLING CHAMPIONSHIP, DECREES "SUBOPTIMAL" HUMAN PARTICIPANTS MUST BE REPLACE BY ROBOTIC WHEELS "*Precision Cheese Trajectory AI™* bans all human competitors after calculating a 97.3% 'unpredictability risk factor'—now only self-propelled Gouda…

    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 Next 80B A3B Thinking: Internal Investment Committee Memo Subject: LedgerLift (LLLT) – Short Recommendation Date: [Current Date] 1. Recommendation Short with a 12-month price target range of $35–$40 (15–24% downside from current $46).

    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 Next 80B A3B Thinking: To solve this problem, let's carefully analyze the relationships described. Step 1: Understand Sally's Perspective Sally is a girl. She has 3 brothers. So, from her point of view, there are 3 male siblings and herself as a female sibling.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

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 Next 80B A3B ThinkingQwen3 Next 80B A3B Thinking
No pick
No pick
No pick

The Shawshank Redemption

1994

No pick

No pick

No pick

The Legend of Zelda: Ocarina of Time (1998)

Adventure, Action

Price and specs

GPT-5.6 Luna and Qwen3 Next 80B A3B Thinking compared across 54 shared prompts
SpecGPT-5.6 LunaQwen3 Next 80B A3B Thinking
Input price$1/M tokens$0.15/M tokens
Output price$6/M tokens$1.5/M tokens
Context window1.1M tokens66K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedJul 2026Sep 2025
At 10M a month$10.00$10.00$1.50$1.50
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 Next 80B A3B Thinking1 host
HostInOutContextUptime
  • Google Vertex AI$0.15 in·$1.20 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.

Common questions

What is the difference between GPT-5.6 Luna and Qwen3 Next 80B A3B Thinking?

GPT-5.6 Luna is developed by OpenAI while Qwen3 Next 80B A3B Thinking is developed by Qwen. GPT-5.6 Luna has a 1.1M token context window vs Qwen3 Next 80B A3B Thinking's 66K. 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 Next 80B A3B Thinking?

It depends on your use case. GPT-5.6 Luna and Qwen3 Next 80B A3B Thinking 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 Next 80B A3B Thinking?

GPT-5.6 Luna costs $1/M input tokens and Qwen3 Next 80B A3B Thinking costs $0.15/M input tokens. Qwen3 Next 80B A3B Thinking is $0.85/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 Next 80B A3B Thinking on Rival?

This page shows a side-by-side comparison of GPT-5.6 Luna and Qwen3 Next 80B A3B Thinking 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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Model pages

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