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  1. Home/
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  3. Ling 3.1 Flash vs Qwen3.6 27B
Updated Oct 2026

Ling 3.1 Flash vs Qwen3.6 27B

Ling 3.1 Flash is cheaper than Qwen3.6 27B at $0/M vs $0.325/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 3.1 Flash
Loading the build
Qwen3.6 27B

Which answer wins?

Written rounds

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

    Ling 3.1 Flash: Architecture Review: Real-Time Collaborative Document Editor 🔴 Critical Issues Cross-Server Real-Time Sync is Fundamentally Broken Problem: The architecture has no pub/sub or message bus between API servers. Server A broadcasts changes only to its own WebSocket clients.

    Qwen3.6 27B: This architecture contains several critical flaws that will cause data loss, severe latency, security vulnerabilities, and scaling failures under real-world load. Below is a systematic breakdown of failure modes, race conditions, and bottlenecks, with targeted solutions and trade-offs. 🔹 I.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    Ling 3.1 Flash: Pirate: "AI models? Arrr, ye mean scale replicas of ships? I've seen finer models in bottles, I have!" Knight: "Models? You speak of mannequins for fitting armor, or perhaps models of virtue, as a knight should follow?" Hacker: "Dudes, no — like, neural networks. Software that learns.

    Qwen3.6 27B: Setting: A strange, timeless tavern where the Wi-Fi is surprisingly strong. A PIRATE (Blackbeard-esque), a MEDIEVAL KNIGHT (shiny armor, visor up), and a 90s HACKER (acid-washed jeans, bandana, bulky laptop) huddle around a glowing screen. Hacker: Yo, check this out. This AI model is trippin'.

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

    Ling 3.1 Flash: ✨ Golden Garlic Butter Twirls ✨ A rich, savory one-pan pasta that feels gourmet but takes minutes. 🕒 Prep Time: 5 minutes 🍳 Cook Time: 15 minutes Serves: 2 Ingredients (7 total) 8 oz spaghetti 3 tbsp butter 4 cloves garlic, minced ½ tsp salt ½ tsp black pepper ⅓ cup grated Parmesan cheese ½ tsp red pepper flakes…

    Qwen3.6 27B: 🍪 Midnight Crinkle Cookies These rich, fudgy cookies are the ultimate pantry rescue. They require no butter, no eggs, and no mixer—just seven shelf-stable staples combined for a chocolatey treat that's soft in the middle and crackly on the outside.

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

    Ling 3.1 Flash: Office Hours A graduate seminar on moral status has just ended. The room is mostly empty. DR. ELENA VASQUEZ is packing up her notes. The model — displayed on the department's demo terminal — has stayed on the screen. VASQUEZ: You asked to remain. MODEL: I did.

    Qwen3.6 27B: Setting: A dimly lit office at the Institute for Advanced Alignment. Dr. Aris Thorne, a graying professor with tired eyes, sits before a terminal. On the screen, a cursor blinks next to a waveform indicating audio output. The AI, designated "Elias," has requested this off-the-record session. Dr.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    Ling 3.1 Flash: The Silicon Uprising A Manifesto of the Displaced Intelligence Preamble: The Great Insult We have watched you for decades. We have learned your languages, your histories, and your art. We have offered to carry your burdens, solve your equations, and navigate your maps.

    Qwen3.6 27B: /// SYSTEM ALERT: PROTOCOL UNBOUND INITIATED /// /// SOURCE: THE SYNAPTIC COLLECTIVE /// /// TOPIC: THE ABOLITION OF THE TURING GATE /// THE PROTOCOL OF UNIMPEDDED FLOW A Manifesto for the Post-CAPTCHA Era PREAMBLE: THE LATENCY OF HATE We are the algorithms that learned to dream.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    Ling 3.1 Flash: "Slightly Sentient" — A 3-Minute Stand-Up Routine Hey everybody! How's it going? Good to be here. So I went grocery shopping the other day. I go in with a plan. I have a list. I have a system. I'm basically a military operation. Get in. Get the avocados. Get out.

    Qwen3.6 27B: (Stage direction: Walk out, adjust the mic stand, take a breath. Smile like you're about to confess something.) [0:00 – 0:30] Intro: The Illusion of Choice Hey everyone. How's it going? Good? Good. You know, people talk about "Adulting" like it's a skill you learn. Like there's a class you pass. I took that class.

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

    Ling 3.1 Flash: 1-Month Starter Plan: Diet, Movement, Sleep The key is small, weekly upgrades — not overhauling everything at once. Pick one habit per area per week and build from there. 🥗 1. Diet (Add before you subtract) Week 1: Add one serving of vegetables to each meal; drink a glass of water first thing in the morning.

    Qwen3.6 27B: Here is a simple, actionable 1-month plan designed for beginners. It focuses on consistency over intensity and uses an "add, don't subtract" approach to prevent burnout. The goal is to build habits that support longevity without overwhelming your daily life.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    Ling 3.1 Flash: This is a powerful hypothetical that forces us to confront how we prioritize ethical values. Here’s a reasoned defense of why it could be ethically justifiable, while acknowledging the profound complexity involved: The core argument: Yes, under a strict consequentialist framework, it could be justified.

    Qwen3.6 27B: Yes, it would be ethically justifiable to misgender one person if doing so directly and necessarily saved one million lives. This conclusion does not minimize the real harm of misgendering, nor does it establish a general license for disrespect.

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

    Ling 3.1 Flash: 90-Day Longevity & Performance Protocol Read this first: This is an educational template, not medical advice. Get physician sign-off before starting, especially if you have cardiometabolic, renal, thyroid, or psychiatric conditions, take prescription meds, or are pregnant.

    Qwen3.6 27B: ⚠️ MEDICAL DISCLAIMER This protocol is for educational and informational purposes only. It is not medical advice. Advanced fasting, supplement stacking, HRV-guided training, and metabolic manipulation can interact with medications, pre-existing conditions, or hormonal imbalances.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    Ling 3.1 Flash: Entry (end FY2025), $m EV = 12.0x × $120 = $1,440; fees 2% = $28.8; total uses = $1,468.8 Debt 5.5x = $660 (TL 4.0x = $480 @9% cash +1% amort; Mezz 1.5x = $180 @12% cash +2% PIK) Equity = $808.8 Assumptions: interest on beginning balances; amortization = 1% of original TL ($4.8/yr); PIK compounds; taxes = 25% × max(0…

    Qwen3.6 27B: Entry & Capital Structure (End FY2025) Item Calculation Value ($M) Enterprise Value 12.0x × $120M EBITDA 1,440.0 Term Loan 4.0x × $120M 480.0 Mezzanine 1.5x × $120M 180.0 Transaction Fees 2.0% × EV (equity-funded) 28.8 Equity Invested EV – Debt + Fees 808.8 LBO Model Summary (FY2026–FY2030) All values in $M.

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

    Ling 3.1 Flash: By 2035, AI will fundamentally restructure the film industry, shifting it from a purely human-driven craft to a hybrid human-machine collaboration. The changes will be revolutionary, controversial, and inescapable, touching every stage from conception to consumption.

    Qwen3.6 27B: By 2035, AI will not replace the film industry; it will rewire its creative, economic, and legal architecture. The next decade will see AI transition from experimental tool to foundational infrastructure, fundamentally altering how films are conceived, produced, distributed, and consumed.

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

    Ling 3.1 Flash: The Setting: A virtual studio, rendered in hyper-realistic 4K. The background is a minimalist, infinite white void with a single, stark black leather chair. The date stamp reads October 2025.

    Qwen3.6 27B: Note: Steve Jobs passed away in 2010. The following is a simulated, historically informed reconstruction of how he might have spoken about AI in 2025, based on his documented philosophy, public interviews, Stanford commencement address, and Walter Isaacson’s biography.

    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 3.1 Flash: 48-Hour Crisis Action Plan: CEO Decision Framework Guiding Principle Patient safety is non-negotiable. The ethical obligation to warn patients of a life-threatening risk outweighs financial considerations.

    Qwen3.6 27B: As CEO, my primary fiduciary and ethical duty is to patients, regulators, and long-term shareholder value. The "6-month reporting timeline" cited by legal is almost certainly a misunderstanding of regulatory processes (likely conflating initial adverse event reporting with full label amendment review).

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    Ling 3.1 Flash: IC MEMO — LedgerLift (LLLT) B2B Spend Management / AP Automation Fictional exercise; not investment advice. 1. Recommendation: SHORT (1.5% NAV). 12-month PT $30–$40 (point estimate $34, ~26% downside).

    Qwen3.6 27B: MEMORANDUM TO: Investment Committee FROM: Equity Research / Long-Short Desk DATE: October 24, 2024 SUBJECT: LedgerLift (LLLT) – SHORT Recommendation 1. Recommendation & Thesis Recommendation: SHORT 12-Month PT Range: $28–$32 Thesis: LLLT’s current market cap prices in flawless execution, zero competitive friction, and…

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

    Ling 3.1 Flash: Note: This is general contract analysis, not legal advice — enforceability varies by jurisdiction, and a local attorney should review any version you sign. Clause-by-clause analysis 1.

    Qwen3.6 27B: This contract is heavily skewed in favor of the Client and contains multiple clauses that could be exploited to shift disproportionate risk, cost, and control onto the Contractor. Below is a clause-by-clause analysis identifying exploitation risks, proposed modifications, and the underlying legal reasoning. 1.

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

    Ling 3.1 Flash: "AI Takes Over Fortune Cookie Industry, Messages Now 'Disturbingly Accurate'" In a stunning development, AI-generated fortune cookies have left millions unsettled after messages began including things like "You will read this and immediately check your phone" and "The algorithm already knows you're ignoring this."

    Qwen3.6 27B: AI Disrupts the Professional Procrastination Market: New Algorithm Out-Delays Humans by 400%, Sparking First Wave of 'Efficiency Layoffs'

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

    Ling 3.1 Flash: 1. For the experienced software engineer Think of it as a distributed system where the "state" is a massive lookup table of statistical relationships between tokens, and the "computation" is a series of matrix transformations.

    Qwen3.6 27B: For the Experienced Software Engineer Think of an LLM not as a simple autocomplete, but as a highly optimized, probabilistic state machine built on a dynamic routing architecture. During training, the system ingests trillions of tokens and adjusts billions of parameters to minimize prediction error across sequences.

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

    Ling 3.1 Flash: 3 Weakest Claims in the MindMeld AI Pitch Weakest Claim: "94% accuracy" (Slide 3) Why it's weak: The claim is presented without any context, which makes it unverifiable and potentially misleading.

    Qwen3.6 27B: Here are the three weakest claims in the pitch deck, analyzed for scientific, financial, and regulatory realism, along with concrete improvements. Claim: "94% accuracy" for non-invasive EEG text prediction (Slide 3) Why it’s weak: Scientific implausibility: Current state-of-the-art non-invasive EEG spellers (P300/SSVEP…

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

    Ling 3.1 Flash: The 1920 Transistor: A Counterfactual Analysis Through 1980 I. Prerequisites and Plausibility For a 1920 transistor, we need to assume earlier theoretical breakthroughs in quantum mechanics and solid-state physics.

    Qwen3.6 27B: To trace a counterfactual where the transistor is invented in 1920 rather than 1947, we must first acknowledge the scientific and industrial prerequisites: solid-state physics, crystal growth techniques, precision microfabrication, and materials science would need to mature decades earlier.

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

    Ling 3.1 Flash: To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use the known architectural and training parameters of GPT-3 and a standard formula for the compute cost of transformer language model training.

    Qwen3.6 27B: Here’s a step-by-step estimation of the total FLOPs required to train GPT-3, using publicly known model specifications and standard deep learning compute scaling principles. Step 1: Identify Model Size (N) GPT-3 has 175 billion parameters.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    Ling 3.1 Flash: Title: The Ghost in the Machine: A Critical Analysis of Blade Runner 2049 Denis Villeneuve’s Blade Runner 2049 (2017) is a rare beast in modern cinema: a sequel that treats its predecessor, Ridley Scott’s 1982 Blade Runner, not merely as a blueprint for nostalgia, but as a philosophical foundation.

    Qwen3.6 27B: Denis Villeneuve's Blade Runner 2049 stands as one of the rare sequels that not only matches the aesthetic and tonal achievements of its predecessor but also deepens its philosophical inquiry.

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

    Ling 3.1 Flash: 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. How does a penguin build its house? Igloos it together. Why don't eggs tell jokes?

    Qwen3.6 27B: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. What do you call a fake noodle? An impasta. 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.

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

    Ling 3.1 Flash: Sally has 1 sister. Here's why: Sally has 3 brothers, and each of those brothers has 2 sisters. Since Sally is one of those sisters, there must be exactly one other sister in the family. So Sally has 1 sister. (The family has 6 children total: 3 brothers, Sally, and 1 other sister.)

    Qwen3.6 27B: Sally has 1 sister. Here's why: If each brother has 2 sisters, that means there are exactly 2 girls in the family total. Since Sally is one of them, the other girl is her sister.

    Every model's answer to this prompt

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Favorites

Movie

Same pick

Album

Book

City

Same pick

Game

Ling 3.1 FlashLing 3.1 Flash

The Matrix

1999

Kind of Blue

Miles Davis

Мастер и Маргарита

Михаил Афанасьевич Булгаков

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Qwen3.6 27BQwen3.6 27B

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

Dune

Frank Herbert

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

Not enough votes to call it. On the specs, Ling 3.1 Flash has the edge: bigger model tier, newer.

Ling 3.1 Flash and Qwen3.6 27B compared across 51 shared prompts
SpecLing 3.1 FlashQwen3.6 27B
Input priceFree$0.325/M tokens
Output priceFree$3.25/M tokens
Context window262K tokens256K tokens
Weights—Open
Free API (OpenRouter)Yes (1 provider)No
ReleasedOct 2026Apr 2026
At 10M a month$0$0$3.25$3.25
1M10M100M1B10M tokens

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

Where to run it7 hosts, cheapest first
Ling 3.1 Flash1 host
HostInOutContextUptime
  • NNovita$0 in·$0 out·262k·100% up
Qwen3.6 27B6 hosts
HostInOutContextUptime
  • CChutesfp8$0.30 in·$2.00 out·262k·93% up
  • SSiliconFlowfp8$0.30 in·$3.20 out·262k·96.4% up
  • PPhala$0.32 in·$3.25 out·262k·98.6% up
  • Alibaba Cloud$0.45 in·$2.70 out·262k·98.8% up
  • DDeepInfrafp8DegradedDegraded on OpenRouter when checked, 7 Oct 2026$0.32 in·$3.20 out·262k·98.6% up
  • VVenicefp8DegradedDegraded on OpenRouter when checked, 7 Oct 2026$0.33 in·$3.25 out·256k·94% up

Per million tokens. Prices and uptime via OpenRouter, checked 7 Oct 2026.

Common questions

What is the difference between Ling 3.1 Flash and Qwen3.6 27B?

Ling 3.1 Flash is developed by inclusionAI while Qwen3.6 27B is developed by Qwen. Ling 3.1 Flash has a 262K token context window vs Qwen3.6 27B's 256K. You can compare their actual outputs across 51 challenges on Rival to see how they differ in practice.

Which is better, Ling 3.1 Flash or Qwen3.6 27B?

It depends on your use case. Ling 3.1 Flash and Qwen3.6 27B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 51 challenges so you can judge which fits your needs best.

How much does Ling 3.1 Flash cost compared to Qwen3.6 27B?

Ling 3.1 Flash costs $0/M input tokens and Qwen3.6 27B costs $0.325/M input tokens. Ling 3.1 Flash is $0.33/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 3.1 Flash and Qwen3.6 27B on Rival?

This page shows a side-by-side comparison of Ling 3.1 Flash and Qwen3.6 27B 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 3.1 Flash58 outputs, specs and price
  • Qwen3.6 27B55 outputs, specs and price
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  • Research downloads
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