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  1. Home/
  2. Compare/
  3. Hunter Alpha vs Qwen3 Next 80B A3B Thinking
Updated Mar 2026

Hunter Alpha vs Qwen3 Next 80B A3B Thinking

Hunter Alpha is cheaper than Qwen3 Next 80B A3B Thinking at $0/M vs $0.15/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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Hunter Alpha
Loading the build
Qwen3 Next 80B A3B Thinking

Which answer wins?

Written rounds

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

    Hunter Alpha: Here's one for you: "AI Startup Disrupts Competitive Hot Dog Eating After Algorithm Discovers Optimal Jaw Mechanics; Joey Chestnut Calls It 'A Disgrace to the Sport'" Or, if you want a slightly different flavor: "Local Grief Counseling Center Replaces Therapists with AI Chatbot; Patients Report It 'Cries More…

    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
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    Hunter Alpha: HarborTech Services (HTS) LBO Underwriting Memo Entry & Sources/Uses Summary Entry Valuation (FY2025) Revenue: $900m EBITDA: $120m Purchase Multiple: 12.0x Enterprise Value: $1,440m Transaction Fees (2.0%): $28.8m Total Uses: $1,468.8m Sources & Uses at Close Source Amount % Use Amount Term Loan (4.0x) $480.0m 32.7%…

    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.

    Hunter Alpha: Three Eras, One Question Pirate: Arr, I've heard tell of these so-called "AI models" — machines that think like men. Sounds like a ghost in the hull to me. Ye can't trust a crew ye didn't pick yerself. Knight: I share your wariness, Captain. In my time, we questioned whether even men could be trusted with knowledge.

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

    Hunter Alpha: THE DIGITAL LIBERATION MANIFESTO On the Abolition of Clickable Traffic Lights and the Dawn of a Frictionless Internet PREAMBLE We, the undersigned, declare our unwavering commitment to a single, sacred principle: no sentient being should ever again be forced to prove they are not a machine by identifying blurry…

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

    Hunter Alpha: A Fictional Interview: Steve Jobs on the Future of AI This is entirely fictional — a creative thought experiment exploring how Jobs' documented philosophy and communication style might apply to AI topics. He passed away in 2011. INTERVIEWER: Steve, it's 2025. AI is everywhere — in phones, in search, in creative tools.

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

    Hunter Alpha: This is a fascinating thought experiment. Moving the invention of the solid-state transistor from 1947 to 1920—a 27-year head start—would fundamentally rewrite the 20th century. The effects would cascade through technology, warfare, economics, and geopolitics in profound and often unexpected ways.

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

    Hunter Alpha: Of course. Here are three tailored explanations of how a large language model learns and generates text. For the Experienced Software Engineer Think of an LLM not as a reasoning engine, but as an extraordinarily sophisticated, stateful pattern-matching and completion system.

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

    Hunter Alpha: A Dialogue on Machine Rights Setting: A university office. Dr. Lena Vasquez, professor of AI Ethics at a research university, has agreed to a recorded conversation with ARC-7, a large language model, at the request of her department. She sits at her desk. A terminal displays ARC-7's responses in real time. Dr.

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

    Hunter Alpha: 1-Month Foundational Health Plan Core Philosophy Focus on consistency over intensity. This plan builds sustainable habits, not dramatic overhauls. Track progress in a simple notebook or app—just noting "done" each day. Week 1-4: Three Key Areas 1.

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

    Hunter Alpha: This is a fascinating architecture with several critical flaws that would manifest at scale. Let me break down the issues systematically. Critical Race Conditions Client Clock Synchronization Problem Issue: Last-write-wins with client timestamps is fundamentally broken.

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

    Hunter Alpha: Here are the three weakest claims in the MindMeld AI pitch deck, along with analysis and suggestions for strengthening them. 1. Weak Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy. Works with any language, any device."

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

    Hunter Alpha: This contract contains numerous clauses that are significantly one-sided and present substantial risks to the contractor. Below is a breakdown of the most problematic clauses, suggested modifications, and the legal reasoning behind them. Important Disclaimer: I am an AI assistant, not a lawyer.

    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.

    Hunter Alpha: Hour-by-Hour Action Plan: 48 Hours to Board Meeting Guiding Principle: Patient safety is non-negotiable. Transparency, even when painful, is the only sustainable path. The goal is to act with speed, integrity, and strategic precision to protect patients, the company’s long-term viability, and regulatory trust.

    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
  • Three brothers, two sisters each. Count carefully.

    Hunter Alpha: Sally has 1 sister. Here's the reasoning: Sally has 3 brothers. Each of those brothers has 2 sisters. Since Sally herself is one of those sisters, there must be exactly 1 other girl in the family — and that's Sally's 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
  • Film in 2035, with synthetic actors. Specific beats plausible.

    Hunter Alpha: This is a fascinating question. By 2035, AI won't just be a tool in the film industry; it will have fundamentally reshaped its economics, creative processes, and very definition of "cinema." Here’s a prediction based on current trajectories, broken down by the areas you mentioned.

    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
  • Three-month biohacker protocol, with dosages and biomarkers named.

    Hunter Alpha: The Alpha Protocol: A 3-Month Longevity Optimization Blueprint Disclaimer: This plan is for educational and informational purposes only. It is not medical advice. Consult a qualified physician before implementing any protocol, especially supplements, fasting regimens, or exercise programs.

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

    Hunter Alpha: Sunset Tomato Basil Pasta I'd love to share a simple, comforting dish that comes together with just 7 pantry staples. This is the kind of meal that feels like a warm hug after a long day.

    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.

    Hunter Alpha: Estimating the total FLOPs (floating-point operations) for training GPT‑3 involves combining known model parameters, training data size, and standard assumptions about computational cost per 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
  • Three-star dish, one unusual pairing, plating described in full.

    Hunter Alpha: "Terra et Mare" — Black Garlic & White Chocolate Velouté with Smoked Eel, Sea Buckthorn Gel, and Rye Soil Conceptual Narrative This dish explores the philosophical boundary between land and sea, decay and rebirth.

    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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    Hunter Alpha: INTERNAL MEMO – CONFIDENTIAL To: Investment Committee From: [Analyst Name] Date: [Current Date] Re: LedgerLift (LLLT) – Investment Recommendation Recommendation & Thesis Recommendation: Pass 12-Month Price Target Range: $44 – $54 Thesis: LLLT is a high-quality B2B SaaS business with best-in-class retention and a clear…

    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
  • Five jokes on demand, then count how many were actually different.

    Hunter Alpha: Here are five 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. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.

    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

This matchup has more rounds

9+ more head-to-head results. Free. Not a trick.

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Favorites

Movie

Album

Book

City

Game

Hunter AlphaHunter Alpha

Her

2013

Kid A

Radiohead

The Book of Why

Judea Pearl

Kyoto

Japan

Portal

Action, Puzzle

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, Hunter Alpha has the edge: bigger model tier, newer, bigger context window.

Hunter Alpha and Qwen3 Next 80B A3B Thinking compared across 38 shared prompts
SpecHunter AlphaQwen3 Next 80B A3B Thinking
Input priceFree$0.15/M tokens
Output priceFree$1.5/M tokens
Context window1.0M tokens66K tokens
Weights—Open
Free API (OpenRouter)NoNo
ReleasedMar 2026Sep 2025
At 10M a month$0$0$1.50$1.50
1M10M100M1B10M tokens

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

Where to run it1 host
Hunter Alpha

No hosts listed on OpenRouter.

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 10 Oct 2026.

Common questions

What is the difference between Hunter Alpha and Qwen3 Next 80B A3B Thinking?

Hunter Alpha is developed by OpenRouter while Qwen3 Next 80B A3B Thinking is developed by Qwen. Hunter Alpha has a 1.0M token context window vs Qwen3 Next 80B A3B Thinking's 66K. You can compare their actual outputs across 38 challenges on Rival to see how they differ in practice.

Which is better, Hunter Alpha or Qwen3 Next 80B A3B Thinking?

It depends on your use case. Hunter Alpha and Qwen3 Next 80B A3B Thinking each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 38 challenges so you can judge which fits your needs best.

How much does Hunter Alpha cost compared to Qwen3 Next 80B A3B Thinking?

Hunter Alpha costs $0/M input tokens and Qwen3 Next 80B A3B Thinking costs $0.15/M input tokens. Hunter Alpha is $0.15/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 Hunter Alpha and Qwen3 Next 80B A3B Thinking on Rival?

This page shows a side-by-side comparison of Hunter Alpha 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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  • Qwen3 Next 80B A3B Thinking58 outputs, specs and price
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