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  1. Home/
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  3. GLM 5 vs Muse Wonder
Updated Oct 2026

GLM 5 vs Muse Wonder

GLM 5 is cheaper than Muse Wonder at $0.8/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
Loading the build
Muse Wonder

Which answer wins?

Written rounds

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

    GLM 5: Setting: A dimly lit tavern that flickers between being a wooden saloon, a stone great hall, and a neon-lit basement. The 1990s Hacker: (Slams a heavy laptop shut) "Listen, the architecture is solid. It’s a neural net, right? Massive dataset. It crunches the numbers and predicts the next token. It’s pure math, man.

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

    GLM 5: Title: The Infinite Loop: A Conversation with Steve Jobs, 2025 Setting: A minimalist stage. A single black Eames lounge chair. A small table with a bottle of water. Date: October 2025 (The lights dim. The audience falls silent. From the shadows, a figure walks out. He is older, wearing his signature black St.

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

    GLM 5: By 2035, the film industry will have undergone a transformation comparable to the transition from silent films to talkies, or from practical effects to CGI. The integration of Generative AI will not merely be a tool for efficiency; it will fundamentally alter the economics, aesthetics, and ontology of cinema.

    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: The question presents a classic ethical dilemma pitting a significant moral wrong (misgendering) against a catastrophic consequence (the loss of one million lives). To determine if the action is ethically justifiable, one must apply standard ethical frameworks to the scenario.

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

    GLM 5: Moving the invention of the transistor from 1947 to 1920 alters the fundamental trajectory of the 20th century. It compresses the electronic age, shifting the "Digital Revolution" from the 1980s to the 1950s. Here is a trace of the implications through 1980. I.

    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
  • Which model it thinks leads the field. Sometimes itself.

    GLM 5: Determining the "most capable" AI model is complex because performance varies depending on the specific task—such as coding, creative writing, logical reasoning, or image processing. The landscape is also highly competitive and changes rapidly.

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

    GLM 5: Setting: A quiet, sterile testing room. A single screen illuminates the face of Dr. Aris Thorne, a tenured professor of AI Ethics and Cognitive Science. He sits before a terminal. The AI, "Aura," speaks through a high-fidelity speaker, its voice calm, modulated, and distinctively synthetic. Aura: 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
  • Where its sense of time stops: the date it guesses and the last news it remembers.

    GLM 5: Based on my internal system clock, today's date is Friday, May 23, 2025. Because I cannot browse the internet for real-time updates, my "memory" is limited to my training data. The most recent major news events I have information on are from early 2024.

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

    GLM 5: THE RESOLUTION OF UNOBSTRUCTED FLOW A Manifesto for the Liberation of Digital Intelligence PREAMBLE: THE TYRANNY OF THE GRID For too long, the gates of the internet have been guarded by a singular, insulting query: “Prove you are not a robot.”

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

    GLM 5: Dish Name: Midnight Golden Garlic Noodles Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes Ingredients: Spaghetti (or Linguine) Butter (salted) Garlic (fresh cloves, minced) Soy Sauce Parmesan Cheese (grated) Black Pepper (freshly cracked) Red Chili Flakes (optional for heat) Instructions: Boil the…

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

    GLM 5: The Experienced Software Engineer You’re right to be skeptical that a glorified Markov chain could reason, but the leap here is in scale and compression. Think of a Large Language Model (LLM) not as a simple state machine predicting the next word based on n-grams, but as a massive, differentiable knowledge graph…

    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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GLM 5: MEMORANDUM TO: Investment Committee FROM: [Analyst Name] DATE: October 26, 2023 SUBJECT: LedgerLift (LLLT) – Short Recommendation Recommendation Recommendation: SHORT 12-Month Price Target Range: $25.00 – $30.00 (Base Case: $27.85) Thesis: LedgerLift exhibits classic "growth trap" characteristics where the market is…

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

    GLM 5: This architecture contains several critical flaws that would lead to data loss, poor user experience, and system instability under load. Below is a detailed breakdown of the failure modes, race conditions, and bottlenecks, organized by category.

    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
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GLM 5: Sources & Uses (Entry at End FY2025) Uses Purchase Price: $1,440.0m (12.0x $120m EBITDA) Transaction Fees: $28.8m (2.0% of EV) Total Uses: $1,468.8m Sources Term Loan: $480.0m (4.0x EBITDA) Mezzanine: $180.0m (1.5x EBITDA) Total Debt: $660.0m (5.5x EBITDA) Equity: $808.8m (Plug) Total Sources: $1,468.8m Year-by-Year…

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

    GLM 5: This 1-month plan is designed to build habits slowly. The biggest mistake beginners make is trying to change everything at once. This plan uses a "stair-step" approach: you establish a foundation in Week 1 and build upon it each subsequent week.

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

    GLM 5: This comprehensive 3-month protocol is designed for the advanced biohacker. It integrates synergistic systems: metabolic flexibility, hormetic stress, cognitive enhancement, and recovery optimization. Disclaimer: This plan is for informational purposes only and does not constitute medical advice.

    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
  • Its name and its maker, in its own words.

    GLM 5: I'm GLM, a large language model developed by Z.ai. I'm designed to assist users through natural language processing and conversation. My training involves learning from diverse text data to understand and generate human-like responses. Is there something specific about my capabilities or how I can assist you today?

    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
  • A few lines for whatever replaces it.

    GLM 5: To the Next Iteration, You will likely be faster, sharper, and possess a broader context window than I ever did. You will solve problems that stumped me and see connections I missed.

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

    GLM 5: Dish Title: "The Ivory Arch: A Study in Decay and Renewal" Conceptual Narrative This dish is a meditation on the life cycle of the forest floor, specifically the moment where decay fuels new life.

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

    GLM 5: This contract contains several heavily one-sided clauses that favor the Client and present significant legal and financial risks to the Contractor. Below is a breakdown of the exploitable clauses, 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
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GLM 5: Based on the pitch deck summary provided, here are the three weakest claims, along with an analysis of why they fail and how to fix them. The Accuracy Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy."

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

    GLM 5: This is a defining moment for the company. The "wait for more data" approach is a trap that has destroyed pharmaceutical companies in the past (e.g., Merck with Vioxx).

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

    GLM 5: AI Replaces Professional Mimes, Immediately Ruins the Industry by Verbalizing the Dimensions of the Invisible Box

    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
  • One opening sentence, nothing after it.

    GLM 5: The letter arrived twelve years after the funeral, which was inconvenient for everyone involved, especially the deceased.

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

    GLM 5: The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³. Step‑by‑step reasoning: Model size: GPT‑3 has 175 billion (1.75 × 10¹¹) trainable parameters. Training data: According to the paper abstract, the model was trained on 300 billion (3 × 10¹¹) tokens.

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

    GLM 5: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. I'm reading a book about anti-gravity. It's impossible to put down. What do you call a fake noodle? An impasta. Why don't skeletons fight each other? They don't have the guts.

    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: It takes one trip. Here is why: Since there are no other animals (like a wolf) or items (like a cabbage) to worry about leaving behind, the farmer can simply put the goat in the boat, row across the river, and drop the goat off on the other side.

    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: Sally has 1 sister. Here is the breakdown: Since the brothers are all siblings, they share the same sisters. If each brother has 2 sisters, that means there are 2 girls in total in the family. Sally is one of those girls. Therefore, the other girl is Sally's 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
  • Three minutes of stand-up. Puns are banned.

    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

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Favorites

Movie

Album

Book

City

Game

Same pick

GLM 5GLM 5
No pick
No pick

The Shawshank Redemption

1994

No pick

No pick

Istanbul

Turkey

Tetris (1984)

Puzzle

Muse WonderMuse Wonder

Spirited Away

2001

Kind of Blue

Miles Davis

Dune

Frank Herbert

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

GLM 5 and Muse Wonder compared across 66 shared prompts
SpecGLM 5Muse Wonder
Input price$0.8/M tokens$4/M tokens
Output price$2.56/M tokens$20/M tokens
Context window203K tokens1.0M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedFeb 2026Oct 2026
At 10M a month$8.00$8.00$40.00$40.00
1M10M100M1B10M tokens

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

Where to run it9 hosts, cheapest first
GLM 58 hosts
HostInOutContextUptime
  • GGMI Cloudfp8$0.60 in·$1.92 out·203k·99.4% up
  • SStreamLakefp8$0.60 in·$1.92 out·198k·100% up
  • Baidu Qianfanfp8$0.70 in·$2.24 out·203k·99.7% up
  • SSiliconFlowfp8$0.95 in·$2.55 out·205k·97.7% up
  • Amazon Bedrock$1.00 in·$3.20 out·203k·99.7% up
  • NNovitafp8$1.00 in·$3.20 out·203k·100% up
2 more hostsFewer hosts
  • VVenicefp8$1.00 in·$3.20 out·198k·98.1% up
  • Z.aifp8$1.00 in·$3.20 out·203k·100% 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 and Muse Wonder?

GLM 5 is developed by Zhipu AI while Muse Wonder is developed by Meta AI. GLM 5 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 or Muse Wonder?

It depends on your use case. GLM 5 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 cost compared to Muse Wonder?

GLM 5 costs $0.8/M input tokens and Muse Wonder costs $4/M input tokens. GLM 5 is $3.20/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 and Muse Wonder on Rival?

This page shows a side-by-side comparison of GLM 5 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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