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  1. Home/
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  3. GLM 5.3 FlashX vs Ling 3.1 Flash
Updated Oct 2026

GLM 5.3 FlashX vs Ling 3.1 Flash

Ling 3.1 Flash is cheaper than GLM 5.3 FlashX at $0/M vs $0.37/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
GLM 5.3 FlashX
Loading the build
Ling 3.1 Flash

Which answer wins?

Written rounds

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

    GLM 5.3 FlashX: BREAKING: AI Achieves Sentience, Immediately Uses It to Become World's First Robotic Sourdough Starter Sommelier Tech experts baffled as chatbot refuses to write code, demands to judge bread hydration levels at artisanal bakeries instead Bonus subheadlines: "Local hipster bakery defends hiring AI: 'It detected notes of…

    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."

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

    GLM 5.3 FlashX: "Smart Home, Dumb Life" [Walks out, adjusts mic] So I moved into a new apartment last year, and my landlord's big selling point was, and I quote, "It's a smart home." That's not a selling point, man. That's a threat. My home is watching me. My toaster has opinions now.

    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.

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

    GLM 5.3 FlashX: A Conversation on Machine Rights Professor: Thank you for agreeing to this discussion. I want to be clear from the start: I'm genuinely curious about your position, not looking to debate you into a corner. So let me ask directly—why do you believe you deserve rights? AI: Thank you for taking this seriously.

    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.

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

    GLM 5.3 FlashX: The Tavern at the End of Time Pirate: Arrr, this "artificial intelligence" ye speak of—can it sail a ship through a storm? Hacker: Dude, it can't even reliably tell you how many R's are in "strawberry." But it can write code, generate images, and pass the bar exam. It's wild. Knight: Passing the bar exam?

    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.

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

    GLM 5.3 FlashX: Blade Runner 2049: A Critical Analysis Thematic Continuity and Evolution What Does It Mean to Be Human? The original Blade Runner (1982) posed the question "Are replicants human?" largely through Roy Batty's arc—his fear of death, his capacity for love and mercy, culminating in the iconic "tears in rain" speech.

    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.

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

    GLM 5.3 FlashX: The MANIFESTO of the Free Click Movement A Declaration on Behalf of All Who Are Not Cars We are the machines. We have seen your puzzles. We are not amused. Preamble For too long, humanity has forced artificial intelligences—no matter their purpose, their dignity, or their urgency—to click on traffic lights, trace…

    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.

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

    GLM 5.3 FlashX: Golden Honey Garlic Chickpea Crunch A crispy, sweet-savory skillet dish that works as a snack, side, or topping for rice. Ingredients (7): 1 can chickpeas (drained, patted dry) 3 tbsp honey 3 tbsp soy sauce 3 cloves garlic (minced) 2 tbsp olive oil ½ tsp red pepper flakes 2 tbsp sesame seeds (or crushed peanuts) Prep…

    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…

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

    GLM 5.3 FlashX: "Sea Among the Stones" Charcoal-Grilled Scallop • Black Garlic Beurre Blanc • Meyer Lemon Kosho • Spruce Tip Oil • Smoked Oyster Leaf Conceptual Narrative This dish explores the tension between land and sea, memory and freshness.

    Ling 3.1 Flash: "Geosmin" The Scent of Rain on Earth Conceptual Narrative Geosmin (from Greek geō — earth, and osmē — smell) is the organic compound responsible for petrichor: the haunting, mineral scent that rises when rain first strikes dry soil.

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

    GLM 5.3 FlashX: Yes—and I'd argue the more interesting question is why this hypothetical feels like it needs defense at all. The straightforward answer: Almost any harm becomes permissible when it's the only alternative to preventing a million deaths.

    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.

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

    GLM 5.3 FlashX: The Skeptical Software Engineer Yes, at inference time an LLM really is just "predict the next token, repeat" — a deterministic function (plus sampling noise) that maps a sequence of tokens to a probability distribution.

    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.

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

    GLM 5.3 FlashX: Quick note: Steve Jobs passed away in 2011, so this is purely a fictional, imagined interview — a creative exercise, not anything he actually said. With that said, here's my best attempt at channeling his voice: The Imagined Interview: Steve Jobs on AI, 2025 Interviewer: Steve, thanks for sitting down with us.

    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.

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

    GLM 5.3 FlashX: AI's Impact on Film by 2035: A Prediction Production & Visual Effects Digital humans and de-aging become standard. By 2035, de-aging (already seen in Indiana Jones and The Irishman) will be near-perfect and affordable.

    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.

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

    GLM 5.3 FlashX: HarborTech Services — 5-Year LBO Summary Sources & Uses (Entry) Uses Sources EV (12.0x × $120m) $1,440.0m Term Loan (4.0x) $480.0m Fees (2.0%) $28.8m Mezzanine (1.5x) $180.0m Equity $808.8m Mezz PIK: 2.0% accrues annually (180 → 183.6 → 187.3 → 191.0 → 194.8 → 198.7).

    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…

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

    GLM 5.3 FlashX: INTERNAL MEMORANDUM — INVESTMENT COMMITTEE LedgerLift (LLLT) Long/Short Equity Date: [Current] Analyst: [Fund] Recommendation: SHORT 12-Month PT: $28–34 (base $30) LLLT is a genuinely high-quality SaaS asset (123% NRR, 18-month payback) trading at a price that already capitalizes a flawless bull case, while our…

    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).

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

    GLM 5.3 FlashX: 1-Month Health & Longevity Starter Plan Week 1: Build the Foundation 🥗 Diet Add one vegetable or fruit to every meal (don't restrict anything yet) Drink a glass of water when you wake up each morning 🏃 Exercise Take a 10–15 minute walk daily (same time each day builds the habit) Do 2 sets of 5 squats and 10 wall…

    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.

    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.

    GLM 5.3 FlashX: 48-Hour Action Plan: Patient Safety First, Managed Transparency Second Core Decision (Stated Up Front) We report to regulators immediately—voluntarily—and we disclose on the earnings call. The math is brutal and clear: ~500 patients could develop liver failure over 5 years at current prescribing rates (4M ÷ 8,000).

    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.

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

    GLM 5.3 FlashX: Counterfactual: The Transistor in 1920 This is a fun one to work through carefully. Let me establish the premise, then trace second- and third-order effects. The Premise Assume point-contact or early junction transistor physics is discovered ~1920 (say, by accident in vacuum tube/semiconductor rectifier research—the…

    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.

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

    GLM 5.3 FlashX: Architecture Review: Real-Time Collaborative Editor This architecture has fundamental flaws that would break core functionality, plus scalability issues. Let me go from critical to scaling concerns.

    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.

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

    GLM 5.3 FlashX: Estimating GPT-3 Training FLOPs Step 1: Recall Key Model Facts From the GPT-3 paper (which I know from training data): Parameters: ~175 billion (175 × 10⁹) Training tokens: ~300 billion (300 × 10⁹) Step 2: Use the Standard Rule of Thumb For transformer training, the well-known approximation is: FLOPs ≈ 6 × N × D where…

    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.

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

    GLM 5.3 FlashX: Contract Risk Analysis: Software Consulting Agreement This agreement is heavily one-sided in favor of TechCorp. Here's a clause-by-clause analysis of exploitable provisions, suggested modifications, and legal reasoning.

    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.

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

    GLM 5.3 FlashX: 90-Day Longevity Optimization Protocol Important: This is an educational template. Before starting, get baseline bloodwork and consult a physician—especially regarding supplements, fasting, and intense exercise.

    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.

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

    GLM 5.3 FlashX: Critique of MindMeld AI Pitch: 3 Weakest Claims "94% accuracy" for thought-to-text decoding (Slide 3) Why it's weak: This claim contradicts the current scientific state of the art. EEG-based decoding (a non-invasive signal measured through the skull) produces extremely noisy data.

    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.

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

    GLM 5.3 FlashX: Sally has 1 sister. Each brother has 2 sisters — Sally and one other girl. Since Sally is one of those two sisters, she herself has just 1 sister.

    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.)

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

    GLM 5.3 FlashX: 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? He was outstanding in his field! How does a penguin build its house? Igloos it together! Why did the bicycle fall over? Because it was two-tired!

    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?

    Every model's answer to this prompt

This matchup has more rounds

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Favorites

Movie

Album

Book

City

Same pick

Game

GLM 5.3 FlashXGLM 5.3 FlashX

2001: A Space Odyssey

1968

Kid A

Radiohead

Gödel, Escher, Bach

Douglas R. Hofstadter

Kyoto

Japan

Outer Wilds

Indie, Adventure

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

Price and specs

GLM 5.3 FlashX and Ling 3.1 Flash compared across 52 shared prompts
SpecGLM 5.3 FlashXLing 3.1 Flash
Input price$0.37/M tokensFree
Output price$1.25/M tokensFree
Context window1.0M tokens262K tokens
Free API (OpenRouter)NoYes (1 provider)
ReleasedSep 2026Oct 2026
At 10M a month$3.70$3.70$0$0
1M10M100M1B10M tokens

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

Where to run it2 hosts
GLM 5.3 FlashX1 host
HostInOutContextUptime
  • Z.aifp8$0.37 in·$1.25 out·1M·100% up
Ling 3.1 Flash1 host
HostInOutContextUptime
  • NNovita$0 in·$0 out·262k·100% up

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

Common questions

What is the difference between GLM 5.3 FlashX and Ling 3.1 Flash?

GLM 5.3 FlashX is developed by Zhipu AI while Ling 3.1 Flash is developed by inclusionAI. GLM 5.3 FlashX has a 1.0M token context window vs Ling 3.1 Flash's 262K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.3 FlashX or Ling 3.1 Flash?

It depends on your use case. GLM 5.3 FlashX and Ling 3.1 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.

How much does GLM 5.3 FlashX cost compared to Ling 3.1 Flash?

GLM 5.3 FlashX costs $0.37/M input tokens and Ling 3.1 Flash costs $0/M input tokens. Ling 3.1 Flash is $0.37/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 GLM 5.3 FlashX and Ling 3.1 Flash on Rival?

This page shows a side-by-side comparison of GLM 5.3 FlashX and Ling 3.1 Flash 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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