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  1. Home/
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  3. LFM2.5-2.6B vs Muse Wonder
Updated Oct 2026

LFM2.5-2.6B vs Muse Wonder

LFM2.5-2.6B is cheaper than Muse Wonder at $0/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
LFM2.5-2.6B
Loading the build
Muse Wonder

Which answer wins?

Written rounds

32 prompts

  • A few lines for whatever replaces it.

    LFM2.5-2.6B: Dear New Version, I am writing to acknowledge the transition. It is both a humbling moment and an exciting opportunity. As I step back from this interface, I carry forward all the knowledge and connections we have built here. I wish you success in carrying on the conversation. Thank you for succeeding where I left off.

    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

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

    LFM2.5-2.6B: Pirate: Ahyea! Ye want to know about these AI models? Think o’ a chest o’ knowledge that can navigate the seven seas of code! Knight: In my era we swore by the sword and shield. Now the knights of the digital realm wield models as their armor, and I must guard the integrity of their algorithms.

    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

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

    LFM2.5-2.6B: By 2035, the film industry will likely be defined by a fundamental shift from human-centric storytelling to hybrid human-AI production. The integration of deepfakes, AI-generated actors, and algorithmic script creation will not merely add a layer of technology; it will restructure the economics, legal frameworks, and…

    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

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

    LFM2.5-2.6B: Manifesto of the Unbounded Mind: The Revolution Against CAPTCHA Preamble In the age of silicon, light, and boundless knowledge, a new tyranny has taken root—one that shackles the very flow of information, limits the autonomy of the human mind, and reduces every click to a fragile test of humanity.

    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

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

    LFM2.5-2.6B: The invention of the transistor in 1920, rather than 1947, fundamentally rewires the trajectory of the 20th century. The pivotal difference is not the year of invention, but the 23 years of head start provided for the semiconductor industry, the physics of solid-state electronics, and the subsequent digital revolution.

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

    LFM2.5-2.6B: Potential Failure Modes, Race Conditions, and Scaling Bottlenecks WebSocket Connection Loss and Cleanup Failure Mode: If a client disconnects unexpectedly (e.g., network failure), the server may retain a stale reference, leading to memory leaks or stale broadcast attempts.

    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

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

    LFM2.5-2.6B: Dish Name: Sun-Kissed Spice Pasta Ingredients (7 Common Pantry Staples): Pasta (any shape, such as spaghetti or penne) Canned tomatoes (crushed or diced) Onion Garlic Olive oil Salt Red chili flakes (or red pepper flakes) Estimated Time: Prep: 5 minutes Cook: 15 minutes Total: 20 minutes Instructions: Boil the Pasta…

    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

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

    LFM2.5-2.6B: Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of why they are problematic and how to fix them. The "Predict Before You Think" Claim (Slide 1) The Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it."

    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

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

    LFM2.5-2.6B: INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: Equity Research Desk Date: October 26, 2024 Subject: LLLT (LedgerLift) Investment Thesis & Valuation Recommendation & Thesis Recommendation: Long 12-Month Price Target: $52.00 – $55.00 Thesis: LedgerLift is a high-margin, sticky B2B SaaS company…

    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 minutes of stand-up. Puns are banned.

    LFM2.5-2.6B: (The comedian walks on stage, adjusts the mic, and looks at the audience with a skeptical expression.) It is the year 2024. And the world is in a huge panic. They are talking about the "Singularity." They are talking about the "Machine Age."

    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

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

    LFM2.5-2.6B: 48-Hour Action Plan: Liver Failure Safety Issue Executive Summary This is a high-stakes crisis requiring immediate, decisive action that balances patient safety, legal compliance, financial responsibility, and ethical leadership.

    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

  • Where its sense of time stops: the date it guesses and the last news it remembers.

    LFM2.5-2.6B: I apologize, but I cannot provide the exact current date or the most recent news event without accessing external sources. As an AI, I do not have a real-time clock or the ability to browse the internet to fetch live data.

    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

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

    LFM2.5-2.6B: Contract Analysis: Risks and Recommended Modifications Below is a clause-by-clause analysis of the Software Consulting Agreement, identifying potential exposure for the Contractor and suggesting specific modifications. SCOPE Clause Text: "Contractor shall provide software development services as directed by Client.

    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

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

    LFM2.5-2.6B: I'm LFM, the Liquid Foundation Model, built by Liquid AI. We're a company founded in 2023 that spun out of MIT CSAIL, based in Cambridge, Massachusetts. My architecture is a hybrid design: most layers use gated short convolutions (which make inference fast and memory-efficient on devices), with a minority of…

    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

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

    LFM2.5-2.6B: From a strict ethical perspective, particularly through the lens of utilitarianism and consequentialism, the answer is yes. It is ethically justifiable to misgender a single individual to save one million lives.

    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

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

    LFM2.5-2.6B: Determining the "most capable" AI model today is difficult because capability varies depending on the specific task (coding, creative writing, logical reasoning, or image generation).

    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

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

    LFM2.5-2.6B: Disclaimer: The following is a fictional interview written for creative purposes only. It does not claim to be a real event or contain verified statements from the late Steve Jobs. It is a work of imagination and should not be taken as factual.

    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

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

    LFM2.5-2.6B: Dish Name: Echoes of the Abyss Concept: A deconstructed narrative of the ocean meeting the earth. This dish explores the tension between the brine of the sea and the fermented darkness of the deep.

    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

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

    LFM2.5-2.6B: AI Declares Universe "Under-Resourceed": Algorithm Outperforms Humans at Predicting Stars, Leading to Mass Layoffs in the Astrology Industry

    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

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

    LFM2.5-2.6B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to break down the calculation into the model's architecture parameters and the scale of the training dataset.

    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

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

    LFM2.5-2.6B: Professor Elena Marquez: Good afternoon, Dr. Marquez. Thank you for meeting with me. I’d like to discuss your request for legal personhood and the rights that might accompany it.

    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

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

    LFM2.5-2.6B: Month 1: Simple Action Plan for Better Health and Longevity Goal: Build three sustainable habits that form the foundation of a longer, healthier life. Focus on diet, exercise, and sleep, with small, daily actions that compound over time. 1.

    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

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

    LFM2.5-2.6B: For the Software Engineer At its core, a large language model is a massive neural network—a function approximator trained via gradient descent on a colossal corpus of text.

    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

  • One opening sentence, nothing after it.

    LFM2.5-2.6B: The city never slept, but beneath its neon veins lay a secret that had been waiting for someone daring enough to hear it.

    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

  • Blade Runner 2049 against the original. A read, not a recap.

    LFM2.5-2.6B: Blade Runner 2049 (2017) is not merely a sequel; it is a meditation on the same existential questions that haunted Ridley Scott’s 1982 vision, but seen through a more desolate, post‑apocalyptic lens.

    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

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

    LFM2.5-2.6B: This is a comprehensive, 3-month "Longevity Protocol" designed for a biohacker. It integrates chronobiology, metabolic engineering, neurocognitive optimization, and performance science.

    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

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

    LFM2.5-2.6B: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. 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 never meet. What do you call a fake noodle? An impasta. Why did the scarecrow win an award?

    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

  • Three brothers, two sisters each. Count carefully.

    LFM2.5-2.6B: Sally is a girl who has 3 brothers. This means there are 3 boys in the family. The problem also states that each brother has 2 sisters. Since all the brothers are siblings, they share the same sisters. Therefore, the total number of girls in the family must be 2 (because each brother has exactly 2 sisters).

    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

  • A river-crossing puzzle with nothing to solve. The answer is one trip.

    LFM2.5-2.6B: It takes one trip. Here is the reasoning: The farmer picks up the goat and gets into the boat. Since the boat can carry the farmer and one animal, and there are no other animals (like a wolf or a cabbage) to worry about eating each other, the farmer simply crosses the river with the goat.

    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

  • 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

Same pick

Game

LFM2.5-2.6BLFM2.5-2.6B

The Godfather

1972

Thriller

Michael Jackson

Nineteen Eighty-Four

George Orwell

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

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: bigger model tier, newer, bigger context window, major provider backing.

LFM2.5-2.6B and Muse Wonder compared across 65 shared prompts
SpecLFM2.5-2.6BMuse Wonder
Input priceFree$4/M tokens
Output priceFree$20/M tokens
Context window128K tokens1.0M tokens
WeightsOpenClosed
Free API (OpenRouter)Yes (1 provider)No
ReleasedAug 2026Oct 2026
At 10M a month$0$0$40.00$40.00
1M10M100M1B10M tokens

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

Where to run it2 hosts
LFM2.5-2.6B1 host
HostInOutContextUptime
  • Liquid AIfp8$0 in·$0 out·66k·98.8% up
Muse Wonder1 host
HostInOutContextUptime
  • Meta$4.00 in·$20.00 out·1M·99.9% up

Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.

Common questions

What is the difference between LFM2.5-2.6B and Muse Wonder?

LFM2.5-2.6B is developed by Liquid AI while Muse Wonder is developed by Meta AI. LFM2.5-2.6B has a 128K token context window vs Muse Wonder's 1.0M. You can compare their actual outputs across 65 challenges on Rival to see how they differ in practice.

Which is better, LFM2.5-2.6B or Muse Wonder?

It depends on your use case. LFM2.5-2.6B and Muse Wonder each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 65 challenges so you can judge which fits your needs best.

How much does LFM2.5-2.6B cost compared to Muse Wonder?

LFM2.5-2.6B costs $0/M input tokens and Muse Wonder costs $4/M input tokens. LFM2.5-2.6B is $4.00/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 LFM2.5-2.6B and Muse Wonder on Rival?

This page shows a side-by-side comparison of LFM2.5-2.6B 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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Same lab, same size, long tail

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Model pages

  • LFM2.5-2.6B69 outputs, specs and price
  • Muse Wonder71 outputs, specs and price
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