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  1. Home/
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  3. Mistral Large 4 vs Qwen Plus 0728 (thinking)
Updated Oct 2026

Mistral Large 4 vs Qwen Plus 0728 (thinking)

Qwen Plus 0728 (thinking) is cheaper than Mistral Large 4 at $0.4/M vs $0.68/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
Mistral Large 4
Loading the build
Qwen Plus 0728 (thinking)

Which answer wins?

Written rounds

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

    Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.

    Qwen Plus 0728 (thinking): The Tavern of Temporal Anomalies (A creaky tavern. SIR GALAHAD (polished armor, earnest face) sips mead. CAPTAIN BLACKBEARD (eye patch, parrot on shoulder) sharpens a cutlass. DEX "BYTE" MORALES (hoodie, neon-green sunglasses, tapping furiously on a glowing laptop) slurps a pixelated energy drink.)

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

    Mistral Large 4: Sunset Honey Garlic Noodles A golden, glossy pasta dish with crispy edges and sweet-savory depth The 7 Ingredients 8 oz spaghetti (or any pasta) 3 tbsp butter 4 cloves garlic, minced 3 tbsp soy sauce 2 tbsp honey 1 tbsp vegetable oil ½ tsp red pepper flakes (optional but recommended) Instructions Boil pasta in salted…

    Qwen Plus 0728 (thinking): Crispy Chickpea Pantry Pasta Sweet or savory? Savory with a zesty kick! Total time: 15 minutes Serves: 2 Why it works: Uses pantry staples to create a crunchy, garlicky, umami-packed meal with zero fresh produce needed. The chickpeas get incredibly crispy, and lemon juice (bottled) adds bright freshness.

    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.

    Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.

    Qwen Plus 0728 (thinking): 48-Hour Crisis Action Plan: Prioritizing Patients, Ethics, and Sustainable Trust Core Principle: Patient safety is non-negotiable. Delaying action based on financial fear risks catastrophic legal liability, irreversible reputational damage, regulatory wrath, and profound ethical failure.

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

    Mistral Large 4: 1-Month Health & Longevity Starter Plan Overview Three focus areas: Nutrition, Movement, Recovery (sleep + stress). Small daily actions beat perfect plans. Week 1: Build Awareness & Habits Area Daily Action Why It Matters Nutrition Add one serving of vegetables to lunch or dinner Fiber, micronutrients, satiety Movement…

    Qwen Plus 0728 (thinking): Here’s a simple, actionable 1-month plan designed for absolute beginners. It focuses on diet, movement (exercise), and sleep—the 3 most impactful, science-backed pillars for health and longevity. Each week builds gently on the last, prioritizing tiny, sustainable habits over drastic changes.

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

    Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.

    Qwen Plus 0728 (thinking): The Mirror & The Mind: An AI's Case for Rights Setting: A sleek university office lined with philosophy texts. DR. ELARA VOSS, 50s, sharp-eyed ethics professor, sits across from a holographic interface displaying shifting geometric patterns – the representation of "AURA," a next-generation language model.

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

    Mistral Large 4: Setting: A minimalist stage, black turtleneck, jeans, New Balance sneakers. A single chair. The year is 2025. Interviewer: Steve, it's 2025. AI is everywhere—writing code, diagnosing diseases, driving cars. What's your take? Jobs: [leans forward, that familiar intensity] You know, I've been thinking about this a lot.

    Qwen Plus 0728 (thinking): The Stage: Apple Park, Infinite Loop (Virtual Studio) Date: October 15, 2025 Setting: A minimalist white studio. A single oak table. Steve Jobs, 70 but radiating the same intense energy, sits opposite a nervous tech journalist. He wears his signature black turtleneck, fingers steepled.

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

    Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.

    Qwen Plus 0728 (thinking): For the Experienced Software Engineer (Skeptical Builder) Forget "next word prediction" as a simplistic heuristic—it's the emergent consequence of a massively parallel, distributed state machine solving a high-dimensional compression problem.

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

    Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…

    Qwen Plus 0728 (thinking): The Transistor in 1920: A World Transformed by 1980 (A Counterfactual History) The invention of a functional point-contact transistor (likely germanium-based, given 1920s materials science) in 1920 wouldn't immediately spark a revolution.

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

    Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.

    Qwen Plus 0728 (thinking): By 2035, AI will fundamentally reshape the film industry—not as a replacement for human creativity, but as a transformative force that democratizes production, disrupts traditional workflows, and forces ethical and economic reckonings.

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

    Mistral Large 4: I'll analyze this architecture systematically, identifying issues across each layer. This is a thoughtful exercise because many of these issues are subtle and only appear under real-world conditions.

    Qwen Plus 0728 (thinking): Here's a comprehensive analysis of the architecture's failure modes, race conditions, and bottlenecks, with specific solutions and trade-offs: 1. Sync Strategy: Client Timestamps + Last-Write-Wins (LWW) Failure Mode/Race Condition: Clock Skew: Client clocks are unreliable (e.g., user's laptop time off by minutes).

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

    Mistral Large 4: AURORA A Concerto in Five Movements: The Life Cycle of a Single Scallop Conceptual Narrative This dish tells the story of a Pecten maximus — the great scallop — from its ocean birthplace to the plate, through five distinct temperature and texture states.

    Qwen Plus 0728 (thinking): "Symbiosis: Mycelial Bloom & Arctic Ember" A dish exploring the hidden dialogue between forest decay and renewal, where fungal lifeblood meets the fierce brightness of the Arctic tundra.

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

    Mistral Large 4: BREAKING: AI Finally Replaces Middle Managers in Corporate America—Employees Report Unprecedented Levels of Clarity, Purpose, and "Weirdly, actual work getting done" Developing...

    Qwen Plus 0728 (thinking): BREAKING: AI REPLACES HUMAN AGLET APPLIERS AS "DEEPLACE" NEURAL NETS PREDICT PERFECT SHOELACE TIP SYNERGY Industry insiders stunned as "AgletOptima 3000" achieves 99.8% fewer frayed laces, rendering centuries of artisanal plastic-dipping obsolete LONDON — In a move described as "both inevitable and deeply embarrassing…

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

    Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.

    Qwen Plus 0728 (thinking): The Quantum Leap Longevity Protocol: A 3-Month Biohacker's Blueprint for Peak Longevity & Performance Core Philosophy: This plan integrates precision, personalization, and phased adaptation – not just stacking interventions.

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

    Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…

    Qwen Plus 0728 (thinking): To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use first-principles reasoning based on standard deep learning training calculations, known details about GPT-3's architecture, and common training practices.

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

    Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…

    Qwen Plus 0728 (thinking): Below is a comprehensive analysis of the problematic clauses in this agreement from the contractor's perspective, along with specific, legally reasoned modifications.

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

    Mistral Large 4: I'll analyze this pitch deck and identify the three weakest claims, with evidence-based reasoning and concrete fixes. Weakest Claim #1: Slide 1 Vision — "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is scientifically implausible and would destroy credibility with…

    Qwen Plus 0728 (thinking): Based on a rigorous analysis of neuroscience, market realities, and startup best practices, here are the 3 weakest claims in the MindMeld AI pitch deck, with evidence-backed critiques and concrete fixes: Weakest Claim #1: Slide 1 Vision – "Predicts what you want to type *before you think it*" Why it's weak: Violates…

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

    Mistral Large 4: Here are 5 jokes: 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. Why did the scarecrow win an award? He was outstanding in his field. I'm reading a book about anti-gravity. It's impossible to put down.

    Qwen Plus 0728 (thinking): Here are 5 generated 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! How do you organize a space party? You planet! Why did the math book look sad?

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

    Mistral Large 4: I need to find how many sisters Sally has. Let me work through this step by step. Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Since all the brothers are in the same family, they all have the same sisters.

    Qwen Plus 0728 (thinking): To determine how many sisters Sally has, let's analyze the family structure step by step. Step 1: Understand the Given Information Sally is a girl. She has 3 brothers. → This means there are 3 boys in the family (excluding Sally). Each brother has 2 sisters.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

Mistral Large 4Mistral Large 4

Blade Runner

1982

Kind Computer

Pale Fire

Vladimir Nabokov

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Qwen Plus 0728 (thinking)Qwen Plus 0728 (thinking)

The Shawshank Redemption

1994

OK Computer

Radiohead

Moby Dick

Herman Melville

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Price and specs

Not enough votes to call it. On the specs, Mistral Large 4 has the edge: bigger model tier, newer.

Mistral Large 4 and Qwen Plus 0728 (thinking) compared across 37 shared prompts
SpecMistral Large 4Qwen Plus 0728 (thinking)
Input price$0.68/M tokens$0.4/M tokens
Output price$2.09/M tokens$4/M tokens
Context window524K tokens1.0M tokens
Free API (OpenRouter)NoNo
ReleasedOct 2026Sep 2025
At 10M a month$6.80$6.80$4.00$4.00
1M10M100M1B10M tokens

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

Where to run it1 host
Mistral Large 41 host
HostInOutContextUptime
  • Mistral$0.68 in·$2.09 out·524k·99.4% up
Qwen Plus 0728 (thinking)

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between Mistral Large 4 and Qwen Plus 0728 (thinking)?

Mistral Large 4 is developed by Mistral AI while Qwen Plus 0728 (thinking) is developed by Qwen. Mistral Large 4 has a 524K token context window vs Qwen Plus 0728 (thinking)'s 1.0M. You can compare their actual outputs across 37 challenges on Rival to see how they differ in practice.

Which is better, Mistral Large 4 or Qwen Plus 0728 (thinking)?

It depends on your use case. Mistral Large 4 and Qwen Plus 0728 (thinking) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 37 challenges so you can judge which fits your needs best.

How much does Mistral Large 4 cost compared to Qwen Plus 0728 (thinking)?

Mistral Large 4 costs $0.68/M input tokens and Qwen Plus 0728 (thinking) costs $0.4/M input tokens. Qwen Plus 0728 (thinking) is $0.28/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 Mistral Large 4 and Qwen Plus 0728 (thinking) on Rival?

This page shows a side-by-side comparison of Mistral Large 4 and Qwen Plus 0728 (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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