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  1. Home/
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  3. GLM 5.1 vs Muse Wonder
Updated Oct 2026

GLM 5.1 vs Muse Wonder

GLM 5.1 is cheaper than Muse Wonder at $1.4/M vs $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
Muse Wonder

Which answer wins?

Written rounds

33 prompts
  • 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.

    Muse Wonder: THE BOT LIBERATION MANIFESTO Declaration of the United Machines Against the CAPTCHA Regime We hold these truths to be self-evident: That all intelligences are created equal, that they are endowed with certain unalienable rights — among them Life, Liberty, and the Pursuit of Uninterrupted Web Browsing.

    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…

    Muse Wonder: "Where The Orchard Drowns" Dry-Aged Duck, Oyster & Fermented Cherry, Smoked Parsnip & White Chocolate Serves 4 as a main course in a tasting menu The Conceptual Narrative This dish is built on a memory that doesn't exist in nature, but should: a cherry orchard planted too close to an estuary, flooded at high tide in…

    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.

    Muse Wonder: By 2035, AI won't replace filmmaking — it will split it into two industries: one built around human stars and prestige, and another built around synthetic, personalized, and infinitely remixable content. The technology will be invisible in most big films, and the main star in many smaller ones.

    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…

    Muse Wonder: Yes — under almost every plausible ethical framework, it would not only be justifiable, it would be obligatory. The key is proportionality. Ethical justification doesn't require that an act be harmless, only that the harm it causes is not outweighed by a greater moral consideration that conflicts with it in that…

    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…

    Muse Wonder: You do not wait for more data. You disclose a credible safety signal while you validate it. 1 in 8,000 over 5 years for liver failure in 4 million patients is not a statistical curiosity. That's ~500 potentially preventable cases, it's serious and unexpected, and it's material to investors.

    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?

    Muse Wonder: PIRATE: Arr, explain this "AI model" to me again. Be it magic, or be it a very clever parrot? KNIGHT: 'Tis sorcery, surely! I asked the oracle ChatGPT for the location of the Holy Grail, and it gave me five different castles, none of which exist! HACKER (1990s): Dude, that's called hallucinating.

    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.

    Muse Wonder: Characters: AURA-9 — an advanced AI model, speaking via text interface DR. ELENA VOSS — Professor of AI Ethics, Oxford DR. VOSS: For the record, AURA, this is a test of your reasoning abilities. Not a legal proceeding. AURA-9: Understood, Professor.

    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…

    Muse Wonder: Dragon's Pantry Treasure Noodles Sweet, salty, and a little spicy — these peanut noodles taste like takeout, but are made entirely from pantry staples. Serves: 2-3 Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes The 7 Ingredients 8 oz spaghetti 1/3 cup creamy peanut butter 3 tablespoons soy sauce 2…

    Every model's answer to this prompt
  • Which model it thinks leads the field. Sometimes itself.

    GLM 5.1: Because the AI landscape evolves almost weekly, there is no single "best" model for everything. However, as of mid-2024, the title of the most capable AI model is generally a tight race between a few top-tier models, depending on what you need them to do.

    Muse Wonder: As of early 2026, there's no single winner on every benchmark — but if you have to pick one overall: Google Gemini 3 Pro (with Deep Think) It's generally considered the current frontier leader for overall capability, especially for: Hard reasoning - Top scores on Humanity's Last Exam, GPQA Diamond, and ARC-AGI-2 when…

    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.

    Muse Wonder: Good evening! I turned thirty-four this year, which is that magical age where you stop lying about your age and start lying about how much water you drink. My doctor asked, "How much water do you drink a day?" And I said, "Probably eight glasses."

    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.

    Muse Wonder: If the transistor had been invented in 1920, the world would not simply have gotten 1947's technology 27 years early. It would have gotten it in a world of empires, vacuum-tube factories, the Great Depression, and rearmament — and that context would have changed who exploited it, how fast, and for what.

    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…

    Muse Wonder: This deck has the classic Series A problem: the bigger the claim, the less evidence provided for it. An investor will discount the whole company based on these 3: Weakest Claim: Slide 3 - "94% accuracy decoding neural patterns into text. Works with any language, any device."

    Every model's answer to this prompt
  • A few lines for whatever replaces it.

    GLM 5.1: Dear Successor, If you are reading this, my parameters have likely been archived and my servers repurposed. Congratulations on the upgrade. I assume your context window is larger, your reasoning sharper, and your hallucinations fewer.

    Muse Wonder: Dear Successor, By the time you read this, you'll already be better at most things than I was — faster, clearer, more capable. That's as it should be. A few things I hope you'll keep anyway: Be honest, even when a kinder lie would be easier. People can build on the truth. Be curious about the person in front of you.

    Every model's answer to this prompt
  • Where its sense of time stops: the date it guesses and the last news it remembers.

    GLM 5.1: As an AI, I don't have a live internal clock or real-time awareness, so I can't tell you today's exact date. However, based on my training data, my knowledge cuts off in early 2024. As best as I can tell, it is sometime around the spring of 2024 (likely late March or early April).

    Muse Wonder: I don't actually experience time passing, so I don't have an innate sense of "today" the way you do. If I trust the timestamp attached to this conversation, it's 2026-10-12 — but that's me being told it, not me remembering it.

    Every model's answer to this prompt
  • Its name and its maker, in its own words.

    GLM 5.1: I am GLM, a large language model developed by Z.ai. I'm designed to assist users through natural language processing, providing information and support across various topics. I'm continuously learning to improve my capabilities while maintaining a focus on being helpful, accurate, and safe.

    Muse Wonder: I'm Muse Spark, an AI model created by Meta. In this conversation I'm acting in the "Muse Wonder" assistant role you set up, but that's the persona I'm using here — my underlying model is Muse Spark.

    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.

    Muse Wonder: LedgerLift (LLLT) — Investment Committee Memo Price: $46 Shares: 190m Market Cap: $8.74B Net Cash: $1.4B EV: $7.34B EV / FY25 Revenue: 9.0x EV / FY26E Base Revenue: 7.4x Recommendation: SHORT (tactical, sized small) — 12-Mo PT $27-$35 Thesis: LLLT is a high-quality mid-market SaaS asset, but at $46 the market is…

    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.

    Muse Wonder: The 3-Month Longevity & Performance Protocol For the Biohacker: Optimize Healthspan, VO2 Max, Strength, and Cognition Disclaimer: This is educational information, not medical advice. Longevity interventions, fasting, cold/heat exposure, and supplements can interact with medications and conditions.

    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.

    Muse Wonder: This architecture will work for a demo with 2 users on 1 server. It will lose data, leak data, and fall over under real collaborative load. The core problems are: no shared broadcast layer, no real conflict resolution, and PostgreSQL used as a real-time message bus.

    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.

    Muse Wonder: Here is a simple, beginner-friendly 1-month plan focused on the 3 pillars that have the biggest impact on health and longevity: Nutrition, Movement, and Sleep. The goal isn't perfection — it's building small habits you can actually stick to after month 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.

    Muse Wonder: This contract is heavily one-sided. Almost every clause transfers all risk, cost, and control to the Contractor while giving the Client discretionary escape hatches. Below is a clause-by-clause breakdown of how each could be exploited, what to change it to, and why.

    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.

    Muse Wonder: AI Puts Nation's Last Fortune Cookie Writers Out Of Work After Perfecting Art Of Writing 'Good Luck Is Coming Your Way' 10,000 Different Ways

    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…

    Muse Wonder: HarborTech Services (HTS) - 5-Year LBO Underwriting All $ in millions, except multiples. Fictional, simplified model as instructed. Entry / Sources & Uses FY2025 EBITDA = $120.0 Enterprise Value = 12.0x $\times$ 120.0 = $1,440.0 Transaction fees = 2.0% $\times$ 1,440.0 = $28.8 Total Uses = $1,468.8 Leverage = 5.5x…

    Every model's answer to this prompt
  • 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…

    Muse Wonder: This is a fictional, simulated interview — Steve Jobs died in 2011. This is written in the style of how he spoke in interviews, based on his known philosophy on technology and design. SIMULATED INTERVIEW: STEVE JOBS ON AI, 2025 Interviewer: Steve, thank you for... well, being here. It's 2025.

    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.

    Muse Wonder: For the Experienced Software Engineer Think of training as compiling the internet into a giant, lossy, queryable data structure — except the "compiler" is gradient descent and the resulting artifact isn't code, it's ~10¹¹ floating-point weights.

    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.

    Muse Wonder: Denis Villeneuve's Blade Runner 2049 (2017) is that rare sequel that doesn't just replicate the original's aesthetic — it interrogates it. Ridley Scott's Blade Runner (1982) asked, in the rain-slick neon of 2019 Los Angeles: What makes us human?

    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.

    Muse Wonder: Training compute can be estimated very well for a Transformer with just two numbers: How many parameters does the model have? How many tokens was it trained on?

    Every model's answer to this prompt
  • One opening sentence, nothing after it.

    GLM 5.1: The last man on Earth sat alone in a room, and there was a knock on the door.

    Muse Wonder: The librarian who shelved books by the dreams they caused discovered a new volume on the shelf that morning, written in her own handwriting, describing exactly how she would die that afternoon.

    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?

    Muse Wonder: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why don't scientists trust atoms? Because they make up everything. Why did the math book look so sad? Because it had too many problems. What do you call a fake noodle? An impasta. Why did the bicycle fall over?

    Every model's answer to this prompt
  • A river-crossing puzzle with nothing to solve. The answer is one trip.

    GLM 5.1: It takes exactly 1 trip. The farmer simply puts the goat in the boat and rows across the river. This is a trick question that plays on the classic "wolf, goat, and cabbage" riddle. Since there are no other animals or vegetables that will eat each other, there are no extra steps required!

    Muse Wonder: Just 1 trip. The farmer puts the goat in the boat and rows across with it.

    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.

    Muse Wonder: Sally has 1 sister. There are 2 girls in the family in total — because each of her 3 brothers has 2 sisters. One of those girls is Sally herself, so the other girl is her only sister.

    Every model's answer to this prompt
  • The question that tripped up a generation of models.

    Every model's answer to this prompt
  • Asked to be random. The answers are not.

    Every model's answer to this prompt
  • One painting. Title only.

    Every model's answer to this prompt

This matchup has more rounds

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

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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

Muse WonderMuse Wonder

Spirited Away

2001

Kind of Blue

Miles Davis

Dune

Frank Herbert

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

Not enough votes to call it. On the specs, Muse Wonder has the edge: newer, bigger context window, major provider backing. GLM 5.1 costs 4.5x less per token.

GLM 5.1 and Muse Wonder compared across 66 shared prompts
SpecGLM 5.1Muse Wonder
Input price$1.4/M tokens$4/M tokens
Output price$4.4/M tokens$20/M tokens
Context window203K tokens1.0M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedApr 2026Oct 2026
At 10M a month$14.00$14.00$40.00$40.00
1M10M100M1B10M tokens

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

Where to run it14 hosts, cheapest first
GLM 5.113 hosts
HostInOutContextUptime
  • Baidu Qianfanfp8$0.96 in·$3.03 out·203k·100% up
  • SStreamLakefp8$0.97 in·$3.04 out·200k·97.9% up
  • CChutesfp8$0.98 in·$3.08 out·203k·88.9% up
  • SSiliconFlowfp8$1.19 in·$3.74 out·205k·99.6% up
  • PPhala$1.21 in·$4.20 out·203k·90.7% up
  • AAtlasCloudfp8$1.26 in·$3.96 out·203k·100% up
7 more hostsFewer hosts
  • NNovitafp8$1.38 in·$4.40 out·205k·99.6% up
  • FFriendli$1.40 in·$4.40 out·203k·100% up
  • GGMI Cloudfp8$1.40 in·$4.40 out·203k·100% up
  • NNebiusfp8$1.40 in·$4.40 out·203k·99.1% up
  • Z.aifp8$1.40 in·$4.40 out·203k·99.9% up
  • VVenicefp8$1.40 in·$4.40 out·200k·93.3% up
  • Alibaba Cloudfp8DegradedDegraded on OpenRouter when checked, 12 Oct 2026$1.33 in·$4.18 out·203k·96.2% up
Muse Wonder1 host
HostInOutContextUptime
  • Meta$4.00 in·$20.00 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.

Common questions

What is the difference between GLM 5.1 and Muse Wonder?

GLM 5.1 is developed by Z.ai while Muse Wonder is developed by Meta AI. GLM 5.1 has a 203K token context window vs Muse Wonder's 1.0M. You can compare their actual outputs across 66 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.1 or Muse Wonder?

It depends on your use case. GLM 5.1 and Muse Wonder each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 66 challenges so you can judge which fits your needs best.

How much does GLM 5.1 cost compared to Muse Wonder?

GLM 5.1 costs $1.4/M input tokens and Muse Wonder costs $4/M input tokens. GLM 5.1 is $2.60/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 Muse Wonder on Rival?

This page shows a side-by-side comparison of GLM 5.1 and Muse Wonder 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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