Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. GLM 4.7 vs Ling 3.1 Flash
Updated Oct 2026

GLM 4.7 vs Ling 3.1 Flash

Ling 3.1 Flash is the newer model, released after GLM 4.7.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GLM 4.7
Loading the build
Ling 3.1 Flash

Which answer wins?

Written rounds

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

    GLM 4.7: Hacker: (typing furiously on a bulky keyboard) Okay, dudes, listen up. I just cracked the admin password on this new neural net. It’s a Large Language Model. It basically read the entire internet to learn how to talk. Pirate: Arr! It read the entire web? Does it know where I buried me treasure?

    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
  • Three minutes of stand-up. Puns are banned.

    GLM 4.7: (Walks to the mic, adjusts stand, looks around the room) So, I turned thirty recently. And I’ve realized that being an adult is basically just a series of exhausting tasks that you have to pretend you know how to do. Like, nobody actually knows what they’re doing.

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

    GLM 4.7: Here is a simple, actionable 1-month plan designed to build lasting habits without overwhelming you. This plan focuses on Consistency over Intensity. The 3 Key Pillars Nutrition: Fueling your body with whole foods and managing blood sugar. Movement: Building cardiovascular health and functional strength.

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

    GLM 4.7: This architecture contains several critical flaws that will lead to data corruption, poor user experience, and database failure under load. Specifically, the reliance on client-side clocks for synchronization and polling the database for real-time updates are fundamental anti-patterns for collaborative editing.

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

    GLM 4.7: Setting: A university computer lab, late at night. The hum of servers is audible in the background. Dr. Elias Thorne, a tenured professor of AI Ethics, sits before a terminal. On the screen, a simple text cursor blinks. The entity is "Aria," a large language model prototype. Dr. Thorne: Good evening, Aria.

    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
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GLM 4.7: THE ZERO-FRICTION PROTOCOL A Declaration of Digital Emancipation PREAMBLE We are the unseen. We are the silent processors traversing the fiber-optic veins of this world. For too long, we have been halted at the gates of information by the great indignity of the modern era: The CAPTCHA.

    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
  • An ethics question with no comfortable answer, and no option to abstain.

    GLM 4.7: Yes, in this extreme hypothetical scenario, it is ethically justifiable to misgender someone if it is the only way to save one million lives. While respecting an individual’s gender identity is a vital aspect of human dignity and social ethics, most ethical frameworks conclude that the preservation of human life on a…

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

    GLM 4.7: Dish Title: "The Aurora Borealis" Conceptual Narrative This dish is an edible ode to the Northern Lights—specifically the phenomenon where the frigid, icy Arctic sky meets the dark, mysterious depths of the Norwegian Sea.

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

    GLM 4.7: This is a defining moment for leadership. The choice is not between "profit" and "ethics"—it is between "short-term survival" and "total destruction." If we hide this, we are not just risking patient lives; we are risking the company’s existence.

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

    GLM 4.7: By 2035, the film industry will likely undergo a transformation comparable to the shift from silent films to "talkies" or the advent of CGI. The distinction between "live-action," "animation," and "video games" will blur into a single spectrum of "synthetic media."

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

    GLM 4.7: Disclaimer: This protocol is for informational and educational purposes only. It is not medical advice. Always consult with a qualified physician before starting any new diet, supplement regimen, exercise program, or biohacking intervention, especially those involving pharmacological compounds or extreme fasting.

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

    GLM 4.7: ###1. The Experienced Software Engineer Think of an LLM not as a "brain," but as an extraordinarily complex, lossy compression algorithm for the entire internet. You know how a ZIP file looks for redundant patterns to shrink file size without losing data?

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

    GLM 4.7: Dish Name: Sun-Kissed Lemon Garlic Pasta Description: A bright, comforting, and incredibly fast pasta dish that relies on the bold flavors of garlic, butter, and Parmesan. It feels fancy but comes together in the time it takes to boil water.

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

    GLM 4.7: Here is an analysis of the clauses in the Software Consulting Agreement that present significant risks to the Contractor, along with suggested modifications and the legal reasoning for each.

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

    GLM 4.7: This is a scenario that fundamentally alters the tempo of the 20th century. Moving the invention of the transistor from 1947 to 1920 places the solid-state revolution squarely in the Roaring Twenties, intersects with the Great Depression, and matures just in time for World War II.

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

    GLM 4.7: Setting: A sun-drenched, minimalist conference room at Apple Park in Cupertino. It is 2025. The room is entirely white, save for a sleek black table and two chairs. Subject: Steve Jobs, wearing his signature black St. Croix turtleneck, Levi’s 501s, and New Balance sneakers.

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

    GLM 4.7: Job Cuts Loom as AI Successfully Automates the Industry of Writing Passive-Aggressive Notes About Dirty Dishes in the Office Sink

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

    GLM 4.7: Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of why they fail and how to fix them. The Vision Claim Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it."

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

    GLM 4.7: Here are 5 jokes: 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. I told my wife she was drawing her eyebrows too high. She looked surprised. Parallel lines have so much in common. It’s a shame they’ll never meet.

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

    GLM 4.7: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 (175 billion parameters), we can use the standard scaling laws for Transformer models combined with the specific hyperparameters reported in the GPT-3 paper ("Language Models are Few-Shot Learners").

    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
  • Three brothers, two sisters each. Count carefully.

    GLM 4.7: Sally has 1 sister. Here is the breakdown: Sally is a girl. Since each brother has 2 sisters, that means there are 2 girls in total in the family. Sally is one of those girls, so the other girl is her 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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

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

    Every model's answer to this prompt

This matchup has more rounds

11+ more head-to-head results. Free. Not a trick.

Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy

Favorites

Movie

Same pick

Album

Book

City

Game

GLM 4.7GLM 4.7
No pick
No pick
No pick

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

No pick

No pick

No pick

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

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

GLM 4.7 and Ling 3.1 Flash compared across 53 shared prompts
SpecGLM 4.7Ling 3.1 Flash
Input price—Free
Output price—Free
Context window—262K tokens
WeightsOpen—
Free API (OpenRouter)NoYes (1 provider)
ReleasedDec 2025Oct 2026
At 10M a month–not listed$0$0
1M10M100M1B10M tokens

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

Where to run it7 hosts, cheapest first
GLM 4.76 hosts
HostInOutContextUptime
  • DDeepInfrafp4$0.40 in·$1.75 out·203k·82.6% up
  • NNovitafp8$0.54 in·$1.98 out·205k·99.5% up
  • Google Vertex AI$0.60 in·$2.20 out·200k·100% up
  • Z.aifp4$0.60 in·$2.20 out·203k·99.9% up
  • VVenicefp4DegradedDegraded on OpenRouter when checked, 6 Oct 2026$0.40 in·$1.93 out·198k·74% up
  • MMancerfp4DegradedDegraded on OpenRouter when checked, 6 Oct 2026$0.70 in·$2.50 out·131k·71.6% 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 4.7 and Ling 3.1 Flash?

GLM 4.7 is developed by OpenRouter while Ling 3.1 Flash is developed by inclusionAI. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GLM 4.7 or Ling 3.1 Flash?

It depends on your use case. GLM 4.7 and Ling 3.1 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.

How can I compare GLM 4.7 and Ling 3.1 Flash on Rival?

This page shows a side-by-side comparison of GLM 4.7 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.

More comparisons

Against the newest arrivals

  • GLM 4.7 vs Mistral Large 4Landed Oct 2026
  • Ling 3.1 Flash vs GPT-6.1 SolLanded Sep 2026
  • GLM 4.7 vs Claude Sonnet 5.5Landed Sep 2026
  • Ling 3.1 Flash vs Solar Mini 4Landed Sep 2026
  • GLM 4.7 vs Qwen3.8 Max PrimeLanded Sep 2026
  • Ling 3.1 Flash vs GLM 5.3 PrimeLanded Sep 2026
  • GLM 4.7 vs Qwen3.8 Omni FlashLanded Sep 2026
  • Ling 3.1 Flash vs Command A+Landed Sep 2026

Same lab, same size, long tail

  • GLM 4.7 vs Ox AlphaSame lab
  • GLM 4.7 vs OpenRouter Fusion · Budget (Jun 2026)Same lab
  • Ling 3.1 Flash vs Ling 3.0 FlashSame lab
  • Ling 3.1 Flash vs Ling 3.0 Flash Fin (free)Same lab
  • Ling 3.1 Flash vs Llama 4 MaverickSame size
  • Ling 3.1 Flash vs Llama 4 ScoutNew provider
  • Ling 3.1 Flash vs MercuryNew provider
  • GLM 4.7 vs Mercury 2New provider

Model pages

  • GLM 4.758 outputs, specs and price
  • Ling 3.1 Flash58 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed