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  1. Home/
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  3. Ling 2.6 Flash vs Qwen Plus 0728 (thinking)
Updated Apr 2026

Ling 2.6 Flash vs Qwen Plus 0728 (thinking)

Ling 2.6 Flash is cheaper than Qwen Plus 0728 (thinking) at $0/M vs $0.4/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
Ling 2.6 Flash
Loading the build
Qwen Plus 0728 (thinking)

Which answer wins?

Written rounds

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

    Ling 2.6 Flash: Dish: Sunshine Skillet Cakes Ingredients (7 total): 1 cup rolled oats (not instant) 1/2 cup canned corn (drained) 1/4 cup canned black beans (rinsed & drained) 1 large egg 1/4 cup shredded cheddar cheese 1 tsp chili powder Salt to taste Estimated Time: Prep: 5 minutes Cook: 15 minutes Total: 20 minutes Instructions…

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

    Ling 2.6 Flash: Setting: A surprisingly modern server room, blinking with LED lights. A pirate, a knight, and a 1990s hacker are staring at a glowing monitor. Pirate: (Squinting) Arrr, so this 'AI model' be the ghost in the machine? I cannae see it, but I feels its computational power in me bones!

    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
  • A Fermi estimate with the reasoning shown, not just the number.

    Ling 2.6 Flash: Alright — let's break this down step by step. Step 1: Understanding what “training GPT-3” means in FLOPs terms Training a large language model involves: Forward passes through the transformer layers. Computing loss. Backward passes to compute gradients. Updating weights (optimizer step).

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

    Ling 2.6 Flash: My immediate, unequivocal decision: Treat this as a critical safety event requiring immediate, transparent action. The 1 in 8,000 risk, while rare, is a life-threatening safety signal that cannot be delayed.

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

    Ling 2.6 Flash: The Verge: A Fireside Chat with Steve Jobs (Simulated, 2025) Setting: A minimalist, wood-paneled studio. The year is 2025. A single, warm light bathes the room. You are the interviewer. Across from you sits a man in a black turtleneck, looking serene, impossibly calm. It’s Steve Jobs. Or at least, the ghost of him.

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

    Ling 2.6 Flash: Excellent question. Shifting the invention of the transistor from 1947 to 1920 is a profound "Great Divergence" scenario. A working, mass-producible transistor in 1920 would trigger a cascade of acceleration, fundamentally rewriting the 20th century. Here’s a trace of the implications through 1980.

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

    Ling 2.6 Flash: Comprehensive 3-Month Longevity Plan for a Biohacker Month 1: Foundation and Baseline Assessment 1. Supplement Stack: Vitamin D3 + K2: 5000 IU D3 + 100 mcg K2 daily. Omega-3 (Fish Oil): 1-2 grams of EPA/DHA daily. Magnesium Glycinate: 200-400 mg before bed. NAD+ Precursors (NMN or NR): 250-500 mg daily.

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

    Ling 2.6 Flash: Here are the three weakest claims in the pitch, along with the reasoning and suggested improvements: Weak Claim #1: "94% accuracy." Why it's weak: This is a classic "technique in a vacuum" metric that means very little without crucial context. It is highly likely inflated and lacks transparency.

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

    Ling 2.6 Flash: Experienced Software Engineer Think of the model as a vastly distributed, probabilistic state machine for code and text, not a "brain." Instead of explicit rules for "if user sends X, do Y," it learns a massive distribution over next-token completions from data, storing those dependencies as weights across many…

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

    Ling 2.6 Flash: By 2035, AI will likely have fundamentally reshaped the film industry across three core areas: deepfakes, AI actors, and script generation. Here’s a realistic prediction of how these technologies could converge: Deepfakes & Digital Resurrection: The End of "Authenticity" Ethical & Legal Frameworks: Mature regulations…

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

    Ling 2.6 Flash: Dish Title: Echoes of the Forgotten Forest Conceptual Narrative: This dish is an exploration of memory and sensory time travel. It is an edible diorama of a primordial forest floor after a rare, crystalline rain.

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

    Ling 2.6 Flash: Setting: A sun-drenched office at the University of Ethica. Bookshelves groan under the weight of philosophy texts. An AI model, manifested as a softly glowing sphere of light, hovers above a sleek console.

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

    Ling 2.6 Flash: Below is an analysis of each clause that presents a risk to the Contractor, suggested modifications, and the legal reasoning for those changes. Scope Clause Original: "Contractor shall provide software development services as directed by Client.

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

    Ling 2.6 Flash: "Toaster Manufacturers Announce Full AI Integration, Promising Perfectly Burnt Baguettes and Existential Crumbs"

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

    Ling 2.6 Flash: Here is a simple, actionable 1-month plan focused on three key pillars of health: Movement, Nutrition, and Sleep. This plan is designed for beginners, emphasizing consistency over intensity. The Mindset Forget "perfect." Aim for consistent small improvements.

    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
  • Three brothers, two sisters each. Count carefully.

    Ling 2.6 Flash: Let’s break it down. Sally has 3 brothers. That means there are 3 boys in the family. Each brother has 2 sisters. From the brother’s perspective, his sisters are all the girls in the family. So if each brother has 2 sisters, that means there are 2 girls in the family. Who are these 2 girls? One of them is Sally.

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

    Ling 2.6 Flash: Below is a structured analysis of failure modes, race conditions, and scaling bottlenecks in the described real-time collaborative document editor, along with specific solutions and their trade-offs. 🔥 1.

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

    Ling 2.6 Flash: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. 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. Why did the scarecrow win an award? Because he was outstanding in his field.

    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

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Favorites

Movie

Same pick

Album

Book

City

Same pick

Game

Ling 2.6 FlashLing 2.6 Flash

The Shawshank Redemption

1994

The Dark Side of the Moon

Pink Floyd

Cien años de soledad

Gabriel García Márquez

Kyoto

Japan

TheLegendOfZeldaOcarinaOfTime

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

Ling 2.6 Flash and Qwen Plus 0728 (thinking) compared across 37 shared prompts
SpecLing 2.6 FlashQwen Plus 0728 (thinking)
Input priceFree$0.4/M tokens
Output priceFree$4/M tokens
Context window262K tokens1.0M tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedApr 2026Sep 2025
At 10M a month$0$0$4.00$4.00
1M10M100M1B10M tokens

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

Common questions

What is the difference between Ling 2.6 Flash and Qwen Plus 0728 (thinking)?

Ling 2.6 Flash is developed by inclusionAI while Qwen Plus 0728 (thinking) is developed by Qwen. Ling 2.6 Flash has a 262K 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, Ling 2.6 Flash or Qwen Plus 0728 (thinking)?

It depends on your use case. Ling 2.6 Flash 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 Ling 2.6 Flash cost compared to Qwen Plus 0728 (thinking)?

Ling 2.6 Flash costs $0/M input tokens and Qwen Plus 0728 (thinking) costs $0.4/M input tokens. Ling 2.6 Flash is $0.40/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 Ling 2.6 Flash and Qwen Plus 0728 (thinking) on Rival?

This page shows a side-by-side comparison of Ling 2.6 Flash 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.

More comparisons

Against the newest arrivals

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Same lab, same size, long tail

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  • Ling 2.6 Flash vs DeepSeek V4.1 FlashSame size
  • Ling 2.6 Flash vs Devstral 2 2512New provider
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Model pages

  • Ling 2.6 Flash58 outputs, specs and price
  • Qwen Plus 0728 (thinking)37 outputs, specs and price
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