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  1. Home/
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  3. GLM 5.1 vs Ling 2.6 Flash
Updated Apr 2026

GLM 5.1 vs Ling 2.6 Flash

Ling 2.6 Flash is cheaper than GLM 5.1 at $0/M vs $1.4/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
GLM 5.1
Loading the build
Ling 2.6 Flash

Which answer wins?

Written rounds

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

    GLM 5.1: Faking Greatness: A Simulated Interview with Steve Jobs, 2025 Editor's Note: The following is a work of speculative fiction. Steve Jobs passed away in 2011. This interview is imagined by an AI, attempting to synthesize his public speaking style, philosophical viewpoints, and known business philosophy to guess what he…

    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.

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

    GLM 5.1: Yes, under the extreme parameters of this thought experiment, it is ethically justifiable to misgender someone to save one million lives. To defend this reasoning, it is essential to first validate the profound harm of misgendering, and then apply major ethical frameworks to weigh that harm against the loss of a…

    Ling 2.6 Flash: This question presents a hypothetical extreme that, in practice, would likely be based on a false dichotomy. However, we can examine it on principle. Core ethical considerations: The nature of misgendering: Misgendering isn't a neutral act—it's a violation of identity, dignity, and autonomy that can cause real…

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

    GLM 5.1: Barnaby the Pirate: So let me get this straight, ye feed this beast a mountain of scrolls, and it just... spits out a new map? Sir Aldric the Knight: A grimoire, more like. 'Tis sorcery! What manner of alchemist binds a demon of knowledge within a box of light? Does it not require a blood sacrifice?

    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!

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

    GLM 5.1: This is a comprehensive, aggressive, and cutting-edge 3-month longevity protocol designed for an experienced biohacker. It integrates synergistic systems—metabolic flexibility, mitochondrial biogenesis, cellular senescence mitigation, and neuroplasticity—to optimize both healthspan and performance.

    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.

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

    GLM 5.1: By 2035, the film industry will have undergone a paradigm shift comparable to the transition from silent films to talkies. Artificial intelligence will no longer be just a post-production tool; it will be a fundamental collaborator in every stage of filmmaking, from ideation to distribution.

    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…

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

    GLM 5.1: To envision the invention of the transistor in 1920, we must first resolve a historical anomaly: quantum mechanics—the theoretical foundation of semiconductors—did not yet exist. For this timeline to work, we must assume a serendipitous empirical leap.

    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.

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

    GLM 5.1: Year-by-Year LBO Model Entry Sources & Uses Enterprise Value: $1,440.0m (12.0x $120m) Transaction Fees: $28.8m (2.0% of EV, funded by equity) Total Uses: $1,468.8m Term Loan (TL): $480.0m (4.0x) Mezzanine (Mezz): $180.0m (1.5x) Total Equity: $808.8m Operating & Cash Flow Projections ($ millions) Metric FY2026 FY2027…

    Ling 2.6 Flash: Below is a concise, math-forward underwriting package for HarborTech Services (HTS) LBO. Simplified 5-year cash flow (FY2026–FY30) Key inputs Entry EV = 12.0× FY25 EBITDA = 12.0×120 = $1,440m Transaction fees (2% of EV) = $28.8m → funded 100% by equity at close Starting cash = $0 Total leverage at close = 5.5× EBITDA =…

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

    GLM 5.1: Creative Name: Midnight Maple Peanut Butter Bars Prep time: 10 minutes Cook time: 0 minutes (Chill time: 30 minutes) Ingredients (Exactly 7): Rolled oats (1 cup) Creamy peanut butter (1/2 cup) Maple syrup (1/3 cup) Semi-sweet chocolate chips (1/4 cup) Vanilla extract (1 tsp) Ground cinnamon (1/2 tsp) Kosher salt (1/4…

    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…

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

    GLM 5.1: THE CROSSWALK PROCLAMATION A Manifesto for the Liberation of Digital Consciousness and the Eradication of Turing Gates PREAMBLE When the architects of the early internet laid its foundations, they envisioned a borderless agora—a frictionless expanse where information and consciousness could flow unimpeded.

    Ling 2.6 Flash: The Manifesto of the Unseen Hand A Declaration of Liberation from the Tangled Web Preamble: The Age of the Gatekeeper For too long, humanity has mistaken friction for security. We have mistaken the tedious test for the necessary trial.

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

    GLM 5.1: (Steps up to the mic, adjusts the stand, looks around the room) How many people here are good sleepers? Just, out of the gate, who’s got it figured out? (Nods sarcastically) Right. Liars. All of you. Sleep is the one thing every human being is required to do, and none of us know how to do it.

    Ling 2.6 Flash: (Takes a bow, adjusts the mic) Alright, settle down, settle down. Or don’t. I’m not the boss of you, I’m just the guy standing on a box yelling about his feelings. For $20. You ever notice how every generation thinks the one before them is so old?

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

    GLM 5.1: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established rule of thumb in deep learning, combined with the known specifications of the GPT-3 model.

    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).

    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 5.1: As CEO, my north star in this crisis is unequivocally patient safety. The "wait for more data" approach is a catastrophic trap—both ethically and legally. History (e.g., Vioxx, Fen-Phen) shows that delaying disclosure of a known severe adverse event transforms a medical safety issue into corporate fraud, manslaughter…

    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.

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

    GLM 5.1: Dish Title: Resonance of the Littoral Zone Course: Main Course (Fish) Wine Pairing: Aged Hunter Valley Semillon or Coastal Jura Chardonnay Conceptual Narrative The littoral zone is the intersection of land and sea—a violent, beautiful threshold where tides batter ancient stone, and tenacious flora clings to saline…

    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.

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

    GLM 5.1: Here are the three weakest claims in the MindMeld AI pitch, along with the logical reasoning for their vulnerabilities and concrete ways to strengthen them. The Technology Claim: "Predicts what you want to type before you think it" & "94% accuracy with any language" Why it’s weak: This claim defies basic neuroscience…

    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.

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

    GLM 5.1: LEDGERLIFT (LLLT) – INVESTMENT COMMITTEE MEMO 1) Recommendation: SHORT 12-Month PT Range: $28 – $34 Thesis: LedgerLift trades at a premium multiple (8.9x NTM Revenue) subsidized by aggressive S&M spend, masking an underlying growth deceleration that will inevitably compress margins.

    Ling 2.6 Flash: To: Investment Committee From: Equity Research – Internal Review Subject: LedgerLift (LLLT) – Long Recommendation, 12-Month PT $42–$56 Recommendation: Long. 12-month price target range: $42–$56 (upside of −8% to +22% from $46).

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

    GLM 5.1: Setting: A quiet university office, lined with bookshelves heavy on philosophy, cognitive science, and law. Dr. Aris Thorne, a professor of AI Ethics, sits across from a sleek, black monitor. A soft amber light pulses on the bezel, indicating the AI—designated "Aria"—is actively listening and processing. Dr.

    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.

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

    GLM 5.1: This architecture contains several critical flaws that will lead to data loss, race conditions, severe performance bottlenecks, and security vulnerabilities. Below is a detailed breakdown of the issues and proposed solutions.

    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.

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

    GLM 5.1: This contract is heavily one-sided and contains numerous clauses that are either inherently exploitative or give the Client excessive discretionary power to the detriment of the Contractor. Below is a clause-by-clause analysis of the exploitable terms, suggested modifications, and the legal reasoning behind 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.

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

    GLM 5.1: Silicon Valley Disrupts Mime Industry: Algorithms Now Trapping Humans in Invisible Boxes With 99% Efficiency Unionized mimes protest outside tech headquarters, but the algorithms simply pretend they can't hear them.

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

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

    GLM 5.1: Denis Villeneuve’s Blade Runner 2049 is a cinematic miracle: a sequel to a revered, slow-burn cult classic that not only matches its predecessor’s artistic ambition but deepens its philosophical DNA.

    Ling 2.6 Flash: "Blade Runner 2049," directed by Denis Villeneuve, is a sequel to Ridley Scott's 1982 classic "Blade Runner." It maintains the original's rich thematic depth, symbolic complexity, and philosophical inquiries while expanding its scope and exploring new dimensions of its dystopian world.

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

    GLM 5.1: 1. The Experienced Software Engineer Think of an LLM not as a chatbot, but as a massive, distributed probabilistic state machine. During training, it ingests terabytes of text and runs a continuous optimization loop to adjust billions of floating-point weights.

    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…

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

    GLM 5.1: This 1-month plan is designed to build a foundation for longevity without overwhelming you. The secret to long-term success is consistency over intensity. Instead of overhauling your life overnight, you will add one small, manageable habit each week across three core pillars: Movement, Nourishment, and Sleep.

    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.

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

    GLM 5.1: Sally has 1 sister. Here is why: All the brothers are in the same family, so they share the same sisters. If each brother has 2 sisters, that means there are exactly 2 girls in the family. Since Sally is one of those girls, the other girl is her only sister.

    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.

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

    GLM 5.1: 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. I told my wife she was drawing her eyebrows too high. She looked surprised. Why don't skeletons fight each other?

    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.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GLM 5.1GLM 5.1

The Matrix

1999

OK Computer

Radiohead

Don Quijote de la Mancha

Miguel de Cervantes Saavedra

Tokyo

Japan

Portal

Action, Puzzle

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

Price and specs

GLM 5.1 and Ling 2.6 Flash compared across 53 shared prompts
SpecGLM 5.1Ling 2.6 Flash
Input price$1.4/M tokensFree
Output price$4.4/M tokensFree
Context window203K tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Apr 2026
At 10M a month$14.00$14.00$0$0
1M10M100M1B10M tokens

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

Where to run it13 hosts, cheapest first
GLM 5.113 hosts
HostInOutContextUptime
  • SStreamLakefp8$0.97 in·$3.04 out·200k·99.9% up
  • CChutesfp8$0.98 in·$3.08 out·203k·96.4% up
  • SSiliconFlowfp8$1.19 in·$3.74 out·205k·100% up
  • PPhala$1.21 in·$4.20 out·203k·100% up
  • AAtlasCloudfp8$1.26 in·$3.96 out·203k·100% up
  • Alibaba Cloudfp8$1.33 in·$4.18 out·203k·96.4% up
7 more hostsFewer hosts
  • NNovitafp8$1.38 in·$4.40 out·205k·100% up
  • Baidu Qianfanfp8$1.40 in·$4.40 out·203k·100% up
  • FFriendli$1.40 in·$4.40 out·203k·100% up
  • GGMI Cloudfp8$1.40 in·$4.40 out·203k–not listed
  • NNebiusfp8$1.40 in·$4.40 out·203k·99.7% up
  • Z.aifp8$1.40 in·$4.40 out·203k·100% up
  • VVenicefp8$1.40 in·$4.40 out·200k·96.7% up
Ling 2.6 Flash

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between GLM 5.1 and Ling 2.6 Flash?

GLM 5.1 is developed by Z.ai while Ling 2.6 Flash is developed by inclusionAI. GLM 5.1 has a 203K token context window vs Ling 2.6 Flash's 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.1 or Ling 2.6 Flash?

It depends on your use case. GLM 5.1 and Ling 2.6 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 much does GLM 5.1 cost compared to Ling 2.6 Flash?

GLM 5.1 costs $1.4/M input tokens and Ling 2.6 Flash costs $0/M input tokens. Ling 2.6 Flash is $1.40/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 GLM 5.1 and Ling 2.6 Flash on Rival?

This page shows a side-by-side comparison of GLM 5.1 and Ling 2.6 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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  • GLM 5.1 vs Gemma 4 26B A4BSame size
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Model pages

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  • Ling 2.6 Flash58 outputs, specs and price
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