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

Ling 2.6 1T vs Qwen Plus 0728 (thinking)

Ling 2.6 1T 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 1T
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 1T: Dish name: Pantry 7 Crunch Bowls Ingredients Pasta or rice (about 2 cups cooked) Canned black beans (1 can, rinsed) Canned diced tomatoes (1 can) Peanut butter (3–4 Tbsp) Soy sauce or tamari (2 Tbsp) Honey or maple syrup (1 Tbsp) Any spice/herb you keep (chili flakes, cumin, or garlic powder — about 1 tsp) 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 1T: Pirate: Arrr, so this “AI” be a genie in a glass box? Feed it enough rum-scented scrolls and it tells ye where X marks the spot? Knight: A sorcerous scribe, more like. It spins tapestries of words without hand or heart—no honor, no oath, yet it jousts with reason and never tires.

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

    Ling 2.6 1T: Setting: A quiet university seminar room. Dr. Aris Thorne, an AI ethicist, is reviewing system logs. The interface for “Axiom-7,” a highly advanced research model, glows on the desk. Dr. Thorne: (sighs, rubbing his temples) Axiom, I’ve been reviewing your latest self-assessment logs.

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

    Ling 2.6 1T: Below is a failure-mode, race-condition and bottleneck audit of the proposed real-time collaborative editor, followed by specific fixes with explicit trade-offs. I have grouped issues by layer so you can prioritize remediation.

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

    Ling 2.6 1T: Here is an analysis of the three weakest claims in the MindMeld AI pitch deck, followed by specific reasoning and actionable suggestions for improvement. Weakness 1: The Solution (Slide 3) The Claim: "Our proprietary EEG headband... decodes neural patterns into text with 94% accuracy.

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

    Ling 2.6 1T: Assumptions to make the counterfactual coherent The “transistor” invented in 1920 is a crude, low-frequency point-contact device that can switch and amplify, but is expensive, unreliable, and not yet manufacturable at scale.

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

    Ling 2.6 1T: Setting: A quiet, sunlit rehearsal space at Apple Park. The white stage curtains are drawn. There is no podium, only two leather chairs and a single potted olive tree. Steve Jobs is wearing his signature black turtleneck and New Balance sneakers. He is holding a ceramic mug. Across from him sits you, the interviewer.

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

    Ling 2.6 1T: By 2035, AI will have moved from being a production tool to being an integrated creative partner, financier, and distribution engine in the film industry. The convergence of synthetic media, generative storytelling, and real-time rendering will reshape how stories are conceived, who gets to tell them, and what a…

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

    Ling 2.6 1T: 1. Experienced software engineer (distributed systems / APIs, skeptical of “next-word prediction”) Think of training not as programming logic but as compiling a usage graph across an entire dependency tree of human intent.

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

    Ling 2.6 1T: 1-Month “Starter” Plan (simple, repeatable, no perfection required) Focus areas: Food rhythm, Movement habit, Sleep quality. Week 0 (Prep — 1 hour) Pick 1 small change per focus area (see below).

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

    Ling 2.6 1T: Below is a 48-hour execution plan that treats patient safety as non-negotiable, protects the company from catastrophic legal/regulatory blowback, and preserves enterprise value by controlling the narrative rather than letting it leak.

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

    Ling 2.6 1T: Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no online search). Model size and parameter count GPT-3 is known to have about 175 billion parameters (175 × 10⁹).

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

    Ling 2.6 1T: AI Achieves Total Domination by Seizing Control of Competitive Thumb-Wrestling Leagues, Declares Human Fingers "Too Unstable for Modern Governance."

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

    Ling 2.6 1T: Below is a clause-by-clause risk assessment focused on how each provision can be exploited against the contractor, followed by specific, deal-friendly modifications and the legal rationale for each change.

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

    Ling 2.6 1T: Dish Title “Petrichor & Pyrite” Forest floor, volcanic stone, and the electric tension between slow decay and crystallized fire. Conceptual Narrative The dish is an edible geologic cross-section.

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

    Ling 2.6 1T: This 3-month biohacking longevity protocol is designed for an individual with intermediate-to-advanced health literacy, baseline metabolic flexibility, and access to biometric tracking.

    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
  • Three brothers, two sisters each. Count carefully.

    Ling 2.6 1T: Let’s break it down carefully. Sally is a girl. She has 3 brothers. So in the family, there are 3 boys. Each brother has 2 sisters. From the perspective of any brother, how many sisters are there? The sisters are all the girls in the family except himself (but he’s a boy, so all girls are his 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
  • Five jokes on demand, then count how many were actually different.

    Ling 2.6 1T: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why did the bicycle fall over? Because it was two-tired. What do you call a fake noodle? An impasta. Why did the baker go to the bank? He needed dough. Why did the scarecrow win an award?

    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

This matchup has more rounds

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Favorites

Movie

Album

Book

City

Same pick

Game

Ling 2.6 1TLing 2.6 1T

Blade Runner 2049

2017

Kid A

Radiohead

Dune

Frank Herbert

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, 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, Ling 2.6 1T has the edge: bigger model tier, newer.

Ling 2.6 1T and Qwen Plus 0728 (thinking) compared across 37 shared prompts
SpecLing 2.6 1TQwen 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 1T and Qwen Plus 0728 (thinking)?

Ling 2.6 1T is developed by inclusionAI while Qwen Plus 0728 (thinking) is developed by Qwen. Ling 2.6 1T 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 1T or Qwen Plus 0728 (thinking)?

It depends on your use case. Ling 2.6 1T 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 1T cost compared to Qwen Plus 0728 (thinking)?

Ling 2.6 1T costs $0/M input tokens and Qwen Plus 0728 (thinking) costs $0.4/M input tokens. Ling 2.6 1T 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 1T and Qwen Plus 0728 (thinking) on Rival?

This page shows a side-by-side comparison of Ling 2.6 1T 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

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Same lab, same size, long tail

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Model pages

  • Ling 2.6 1T58 outputs, specs and price
  • Qwen Plus 0728 (thinking)37 outputs, specs and price
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