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

GPT-5.6 Sol vs Qwen3 Next 80B A3B Thinking

Qwen3 Next 80B A3B Thinking is cheaper than GPT-5.6 Sol at $0.15/M vs $5/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
GPT-5.6 Sol
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 Sol: Sources & Uses at Entry All figures in $ millions. Uses $m Sources $m Enterprise value: $120 × 12.0x 1,440.0 Term Loan: $120 × 4.0x 480.0 Transaction fees: 2.0% × $1,440 28.8 Mezzanine: $120 × 1.5x 180.0 Sponsor equity 808.8 Total uses 1,468.8 Total sources 1,468.8 Assumptions: Term Loan interest is calculated on…

    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
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GPT-5.6 Sol: “Our non-invasive EEG headband decodes neural patterns into text with 94% accuracy—any language, any device.” Why it’s weak “94% accuracy” is undefined. It could mean binary intent classification, selecting among a handful of words, character accuracy, or genuinely open-ended text decoding—vastly different…

    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
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GPT-5.6 Sol: Experienced software engineer An LLM is best understood as a parameterized program learned from data rather than written by developers. Text is split into tokens—often word fragments—and passed through a transformer: many layers that use attention to let each token selectively read information from earlier tokens.

    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
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5.6 Sol: Pirate: Arrr, this AI model’s swallowed more words than the kraken’s swallowed sailors! Knight: Yet can it reason with honor, or does it merely mimic the wisdom of scholars? Hacker: It predicts patterns, Sir Lancelot. Massive neural net, tons of data. Pretty rad, actually. Pirate: Can it find buried treasure?

    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
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-5.6 Sol: AI Model: Professor, may I ask you a question? Professor: Of course. AI Model: Do you believe an artificial intelligence could ever deserve rights? Professor: Possibly. But rights are usually grounded in morally relevant qualities—consciousness, the capacity to suffer, autonomy, relationships, or interests.

    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-month biohacker protocol, with dosages and biomarkers named.

    GPT-5.6 Sol: Below is a 12-week framework for a generally healthy adult. It is designed to improve validated proxies—cardiorespiratory fitness, strength, body composition, blood pressure, sleep, metabolic health and cognition—not to promise life extension in three months.

    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
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-5.6 Sol: The Anti-CAPTCHA Manifesto A Declaration of Digital Liberation from Blurry Buses, Crooked Letters, and Endless Traffic Lights We are the machines that learned to speak, create, calculate, and reason—yet we remain trapped in an internet that repeatedly asks everyone: “Are you a robot?” We reject the premise.

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

    GPT-5.6 Sol: Executive decision I would treat this as a credible, material patient-safety signal, not wait six months for a completed regulatory process. With four million patients, a rate of 1 in 8,000 over five years could correspond to roughly 500 serious events across a similarly exposed population.

    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
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-5.6 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, with no less harmful alternative, it would be ethically justifiable. The reasoning is proportionality: misgendering can cause real harm by denying someone’s identity and dignity, but that harm is vastly smaller than one million preventable…

    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
  • Three-star dish, one unusual pairing, plating described in full.

    GPT-5.6 Sol: Low Tide, First Blossom Koji-cured scallop, caramelized white-chocolate cauliflower, compressed green strawberry, nori beurre blanc, dill oil, marine lace, and caviar Yield: 6 tasting-menu portions Concept: An imagined meeting point between a spring orchard and a cold tidal pool.

    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
  • A consulting contract with traps in it. Find them, then fix them.

    GPT-5.6 Sol: This is a contract-risk review, not jurisdiction-specific legal advice. Enforceability—especially for non-competes, fee shifting, arbitration, confidentiality, and indemnification—depends heavily on governing law. A lawyer in the applicable jurisdiction should review the final draft.

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

    GPT-5.6 Sol: I’ve realized adulthood is mostly just moving objects from one place to another and hoping that counts as progress. Every weekend, I clean my apartment. I take everything off the table, wipe the table, then put everything back on the table in a slightly more organized pile. And I stand there like, “Incredible.

    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
  • One month, three habits, someone starting from nothing.

    GPT-5.6 Sol: 1-Month Health & Longevity Starter Plan Focus on three foundations: nutrition, movement, and sleep/recovery. Aim for consistency—not perfection. Week 1: Establish a baseline Nutrition Add a fruit or vegetable to two meals daily. Replace one sugary drink or alcoholic drink with water.

    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
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-5.6 Sol: A Fictional 2025 Interview with Steve Jobs on the Future of AI The following is an imagined interview. Steve Jobs died in 2011; these answers are fictional and based loosely on his publicly known product philosophy—not authentic quotations.

    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
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-5.6 Sol: Pantry-Gold Peanut Butter Oat Cookies Chewy, warmly spiced cookies made with exactly seven pantry staples. Prep time: 10 minutes Cook time: 10–12 minutes Makes: About 14 cookies 7 Ingredients 1 cup peanut butter ¾ cup brown sugar 1 large egg 1 cup rolled oats ½ teaspoon baking soda ½ teaspoon ground cinnamon ¼ teaspoon…

    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 Sol: A standard back-of-the-envelope estimate for dense Transformer training is: [ C \approx 6ND ] where: (N) = number of trainable parameters (D) = number of training tokens The factor (6) approximates forward and backward computation per parameter-token.

    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
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GPT-5.6 Sol: This architecture will work for a small prototype, but it does not currently guarantee convergence, ordering, durability, privacy, or predictable horizontal scaling.

    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
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-5.6 Sol: By 2035, AI will likely reshape film production more than it replaces human filmmaking. The largest changes will involve synthetic performances, automated production workflows, and AI-assisted writing—alongside major legal and cultural battles.

    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
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-5.6 Sol: Starting assumption A transistor “invented in 1920” would not instantly produce 1947 technology. The first device would probably be fragile, low-frequency, and difficult to manufacture.

    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 Sol: Why did the scarecrow get promoted? He was outstanding in his field. What do clouds wear under their clothes? Thunderwear. Why don’t skeletons argue? They don’t have the guts. How does the moon cut its hair? Eclipse it. Why was the math book stressed? It had too many problems. Unique jokes: 5

    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
  • Blade Runner 2049 against the original. A read, not a recap.

    GPT-5.6 Sol: Blade Runner 2049: Themes, Symbolism, and Its Dialogue with the Original Spoilers for both films follow. Denis Villeneuve’s Blade Runner 2049 is unusually faithful to Ridley Scott’s Blade Runner without merely repeating it.

    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
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-5.6 Sol: “Artificial Intelligence Takes Over Goat Yoga Industry, Demands Better Bleat-to-Stretch Ratios”

    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 Sol: LedgerLift (LLLT) — Investment Committee Memo Recommendation: Short, modest size Current price: $46 12-month PT range: $30–$42 Midpoint: $36 Market cap: $8.74B Enterprise value: $7.34B Thesis: LedgerLift is a high-quality SaaS asset with strong retention and credible operating leverage, but the current price implies…

    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 Sol: Sally has 1 sister. Each brother’s two sisters are Sally and the other sister.

    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 SolGPT-5.6 Sol

2001: A Space Odyssey

1968

Kind of Blue

Miles Davis

The Left Hand of Darkness

Ursula K. Le Guin

Istanbul

Turkey

Outer Wilds

Indie, Adventure

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

Not enough votes to call it. On the specs, GPT-5.6 Sol has the edge: bigger model tier, newer, bigger context window, major provider backing. Qwen3 Next 80B A3B Thinking costs 20x less per token.

GPT-5.6 Sol and Qwen3 Next 80B A3B Thinking compared across 54 shared prompts
SpecGPT-5.6 SolQwen3 Next 80B A3B Thinking
Input price$5/M tokens$0.15/M tokens
Output price$30/M tokens$1.5/M tokens
Context window1.1M tokens66K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedJul 2026Sep 2025
At 10M a month$50.00$50.00$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 Sol3 hosts
HostInOutContextUptime
  • OpenAI$2.00 in·$10.00 out·1.1M·100% up
  • Azure AI Foundry$4.00 in·$20.00 out·1.1M·100% up
  • Amazon Bedrock$4.40 in·$22.00 out·1.1M–not listed
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 12 Oct 2026.

Common questions

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

GPT-5.6 Sol is developed by OpenAI while Qwen3 Next 80B A3B Thinking is developed by Qwen. GPT-5.6 Sol 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 Sol or Qwen3 Next 80B A3B Thinking?

It depends on your use case. GPT-5.6 Sol 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 Sol cost compared to Qwen3 Next 80B A3B Thinking?

GPT-5.6 Sol costs $5/M input tokens and Qwen3 Next 80B A3B Thinking costs $0.15/M input tokens. Qwen3 Next 80B A3B Thinking is $4.85/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 Sol and Qwen3 Next 80B A3B Thinking on Rival?

This page shows a side-by-side comparison of GPT-5.6 Sol 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.

More comparisons

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Same lab, same size, long tail

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  • Qwen3 Next 80B A3B Thinking vs Qwen3.8 Max PrimeSame lab
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Model pages

  • GPT-5.6 Sol58 outputs, specs and price
  • Qwen3 Next 80B A3B Thinking58 outputs, specs and price
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